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Page 1 (75) Project Number: CELTIC / CP5-026 Project Title: Wireless World Initiative New Radio WINNER+ Document Type: PU (Public) Document Identifier: D1.8 Document Title: D1.8 Intermediate Report on CoMP (Coordinated Multi-Point) and Relaying in the Framework of CoMP Source Activity: WP1 Editor: Sylvie Mayrargue Authors: Mauro Boldi, Carmen Botella, Federico Boccardi, Valeria D’Amico, Eric Hardouin, Magnus Olsson, Harri Pennanen, Peter Rost, Valentin Savin, Tommy Svensson, Antti Tölli Status / Version: Stable 0.1 Date Last changes: 20.01.10 File Name: D1.8.doc Abstract: This deliverable is an intermediate report on CoMP (Coordinated Multi-Point) and on Relaying in the Framework of CoMP. It describes the second set of innovations encompassing concepts about promising principles or ideas as well as detailed innovative techniques in the context of the work towards the WINNER+ system concept. For each concept, the associated benefits as well as the corresponding requirements on the system architecture and protocols, measurements and signalling, are considered. Regarding CoMP algorithms, focus is put on schemes with reduced requirements in terms of backhauling considering two categories: “Coordinated Beamforming”, and “Joint Processing”. As for relaying, a relay-assisted interference channel, and a distributed LDPC coding for a Decode and Forward relay are introduced. Keywords: Coordinated Beamforming, Coordinated multipoint systems, Joint Processing, Relaying Document History: 05.05.2009 1 rst Draft version 04.08.2009 2 nd Draft version 02.10.2009 Clean version

Intermediate Report on CoMP

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Page 1: Intermediate Report on CoMP

Page 1 (75)

Project Number: CELTIC / CP5-026

Project Title: Wireless World Initiative New Radio – WINNER+

Document Type: PU (Public)

Document Identifier: D1.8

Document Title: D1.8 Intermediate Report on CoMP (Coordinated Multi-Point) and Relaying in the

Framework of CoMP

Source Activity: WP1

Editor: Sylvie Mayrargue

Authors: Mauro Boldi, Carmen Botella, Federico Boccardi, Valeria D’Amico, Eric Hardouin, Magnus Olsson, Harri Pennanen, Peter Rost,

Valentin Savin, Tommy Svensson, Antti Tölli

Status / Version: Stable 0.1

Date Last changes: 20.01.10

File Name: D1.8.doc

Abstract:

This deliverable is an intermediate report on CoMP (Coordinated Multi-Point) and on Relaying in the Framework of CoMP. It describes the second set of innovations encompassing concepts about promising principles or ideas as well as detailed innovative techniques in the context of the work towards the WINNER+ system concept. For each concept, the associated benefits as well as the corresponding requirements on the system architecture and protocols, measurements and signalling, are considered.

Regarding CoMP algorithms, focus is put on schemes with reduced requirements in terms of backhauling considering two categories: “Coordinated Beamforming”, and “Joint Processing”. As for relaying, a relay-assisted interference channel, and a distributed LDPC coding for a Decode and Forward relay are introduced.

Keywords:

Coordinated Beamforming, Coordinated multipoint systems, Joint Processing, Relaying

Document History:

05.05.2009 1rst

Draft version

04.08.2009 2nd

Draft version

02.10.2009 Clean version

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Table of Contents

1. Introduction ............................................................................................ 8

2. Investigations on CoMP issues .......................................................... 10

2.1 Assessment of transmission strategies in a RoF based CoMP architecture ......................... 10

2.2 Coordinated beamforming concepts .................................................................................. 14

2.2.1 Centralized non-codebook based coordinated beamforming ....................................... 16

2.2.2 Decentralized non-codebook based coordinated beamforming .................................... 18

2.2.3 Codebook-based coordinated beamforming ............................................................... 22

2.3 Joint processing with relaxed requirement ........................................................................ 25

2.3.1 Performance of joint processing schemes under varying CSI requirements ................. 26

2.3.2 Joint processing with reduced backhaul requirement by MAC coordination ................ 27

2.3.3 A generalized method for joint design of linear transceivers with CoMP transmission 31

3. Investigations on relaying in the framework of CoMP ..................... 35

3.1 Impact of interference on design and performance of relaying protocols ............................ 35

3.2 Distributed LDPC coding for the single relay channel ....................................................... 41

4. Conclusion ........................................................................................... 46

5. References ........................................................................................... 48

A. Appendix .............................................................................................. 52

A.1 System level performance evaluation of coordinated beamforming and joint processing .... 52

A.2 Further details and performance evaluation of decentralized coordinated beamforming ..... 56

A.3 Simulation conditions details for codebook-based coordinated beamforming .................... 60

A.4 Performance investigation of joint processing schemes considering area coverage. ............ 61

A.5 Joint processing with reduced backhaul requirement by MAC coordination....................... 67

A.6 Further details and performance evaluation of generalized CoMP transmission method ..... 69

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Authors

Partner Name Phone / Fax / e-mail

CEA-LETI Sylvie Mayrargue Phone: +33 (0)4 38 78 62 42

Fax: +33 (0)4 38 78 65 86

e-mail: [email protected]

Valentin Savin Phone: +33 (0)4 38 78 07 11

Fax :: +33 (0)4 38 78 65 86

e-mail: [email protected]

Alcatel-Lucent UK Federico Boccardi Phone: +44 (0)1793776670

Fax: +44 (0)1793776725

e-mail: [email protected]

Chalmers University Tommy Svensson Phone: +46 31 772 1823

of Technology Fax: +46 31 772 1748

e-mail: [email protected]

Carmen Botella Phone: +46 31 772 1885

Fax: +46 31 772 1748

e-mail: [email protected]

Ericsson AB Magnus Olsson Phone: +46 10 71 30774

Fax: +46 10 71 31480

e-mail: [email protected]

France Telecom Eric Hardouin Phone: +33 1 45 29 44 16

Fax: +33 1 45 29 45 34

e-mail: [email protected]

Telecom Italia Lab Mauro Boldi Phone: +39 011 228 7771

Fax:+390112285224

e-mail: [email protected]

Valeria D’Amico Phone: +39 011 228 7544

Fax:+390112285224

e-mail: [email protected]

Bruno Melis Phone: +39 011 228 7121

Fax:+390112285224

e-mail: [email protected]

University of Oulu Antti Tölli Phone: +358 8 553 2986

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Fax: +358 8 553 2845

e-mail: [email protected]

Harri Pennanen Phone: +358 8 553 2854

Fax: +358 8 553 2845

e-mail: [email protected]

Technische Universität Peter Rost Phone: +49 351 463 41042

Dresden/ Vodafone Chair Fax: +49 351 463 41099

e-mail: [email protected]

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Executive Summary

This deliverable presents the innovative concepts on Coordinated Multi Point (CoMP) identified by the

Innovation Group and studied during year two. These concepts are candidate for potential inclusion in the

WINNER+ system concept. They consist of promising principles or ideas, and include detailed

innovative techniques if already available. State-of-the-art reviews are provided for each identified

concept, as well as first considerations about the requirements on the system, especially regarding

measurements and signalling, and architecture and protocols.

CoMP transmission and reception is studied, which refers to a system where the transmission and/or

reception at multiple, geographically separated antenna sites is dynamically coordinated in order to

improve system performance. CoMP is seen as one of the most promising means to provide high data rate

services for a large number of users, with a high spectral efficiency over the entire cell area. In year one,

WINNER+ studied different aspects of CoMP extensively. These included system architectures, different

approaches and algorithms for performing the coordination, and the requirements in terms of

measurements, signalling, backhauling constraints, etc. these put on the system. The work is reported in

[WIN+D14]. Much attention was put on joint processing/transmission schemes, in which users data are

shared between cooperating base stations (BSs). It was found that these schemes have potential to provide

significant performance gains, however at the price of high requirements on the backhaul links in terms of

latency and capacity since user data, channel state information (CSI), and precoding weights need to be

shared among the transmission points. During year two more focus has been put on another category of schemes, i.e, coordinated beamforming where each user data are available at a single BS. Only

information such as channel state or other quality indicators are shared by cooperating BSs, as well as

scheduling decisions and/or generated beams. Another topic studied during year 2 has been ways to

reduce the backhauling requirements for schemes based on joint processing. In both cases, clustering (i.e.

determining which BSs will cooperate) is key to algorithmic performance.

The introduction of Relay Nodes (RN) which are controlled by the network allows to use them as part of

a CoMP system. The RNs can be used to extend the actual coverage or to densify the actual network to

enhance the user throughput at the cell edge. Relay nodes are connected to a BS via over-the-air in-band

links (e.g. specific control channels or in-band backhaul, depending on the relay type), enabling a tight

coordination but at the price of a possible delay between the coordinated nodes. In year one, different

aspects of coordinated relaying schemes were investigated, e.g. coding schemes and schedulers taking

relaying into account. The conclusion was that it is worthwhile investigating relaying in the framework of

CoMP further. Hence, various cooperative relaying schemes have been studied in year two. The first

innovation considers a relay-assisted interference channel with two communication pairs and one relay

node for each pair. Various cooperative and non-cooperative schemes are compared. The second

innovation considers distributed Low Density Parity Codes (LDPC) coding for a Decode and Forward

(DF) type relay.

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List of acronyms and abbreviations

3GPP 3rd Generation Partnership Project

AF Amplify-and-Forward

AWGN Additive White Gaussian Noise

BC Broadcast Channel

BS Base Station

CBF Codebook Based Beamforming

CDF Cumulative Distribution Function

CJP Centralized Joint Processing

CDMA Code Division Multiple Access

CoMP Coordinated MultiPoint

CSI Channel State Information

CU Central Unit

DAS Distributed Antenna System

DF Decode-and-Forward

DJP Distributed Joint Processing

DL Downlink

DPC Dirty Paper Coding

ETW Etkin-Tse-Wang

E-UTRA Evolved UMTS Terrestrial Radio Access

FDD Frequency Division Duplex

HK Han-Kobayashi

IC Interference Channel

ICI Inter-Cell Interference

IEEE Institute of Electrical and Electronics Engineers

ITU International Telecommunication Union

LDPC Low-density Parity-check

LI-PMI Least Interfering PMI

LMMSE Linear Minimum Mean Square Error

LOS Line Of Sight

LTE Long Term Evolution of 3GPP mobile system

LTE-A LTE-Advanced

MAC Medium Access Control / Multiple Access Channel

MI-PMI Most Interfering PMI

MIMO Multiple-Input Multiple-Output

MMSE Minimum Mean Square Error

MRC Maximum Ratio Combining

MuBF Multi-user Beamforming

OFDM Orthogonal Frequency Division Multiplexing

OFDMA Orthogonal Frequency Division Multiple Access

PHY Physical Layer

PJP Partial Joint Processing

PMI Precoding Matrix Index

QPSK Quadrature Phase Shift Keying

RAN Radio Access Network

RoF Radio over Fibre

RN Relay Node

RRM Radio Resource Management

RRU Remote Radio Unit

RU Remote Unit

RX Receive

SDMA Spatial Division Multiple Access

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SIC Successive Interference Cancellation

SINR Signal to Interference plus Noise Ratio

SNR Signal to Noise Ratio

SOCP Second Order Cone Program

TC Turbo Code

TDD Time Division Duplex

TDMA Time Division Multiple Access

TTI Transmission Time Interval

TX Transmitter

UE User Equipment

UL Uplink

UMTS Universal Mobile Telecommunications System

UT User Terminal

WiMAX Worldwide Interoperability for Microwave Access

ZF Zero-Forcing

ZF-DPC Zero-Forcing Dirty Paper Coding

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1. Introduction

Future cellular networks will need to provide high data rate services for a large number of users, which

requires a high spectral efficiency over the entire cell area. In order to achieve this, it is important that the

radio interface is robust to interference and in particular inter-cell interference (ICI) which appears when

the same radio resources is re-used in different cells in an uncoordinated way. Naturally, ICI particularly

degrades the performance of users located in the cell edge areas, which creates a performance discrepancy

between cell edge and inner cell users. One possible means to alleviate this performance discrepancy is to

employ Coordinated Multi Point (CoMP) transmission and reception, which refers to a system where the

transmission and/or reception at multiple, geographically separated antenna sites is dynamically

coordinated in order to improve system performance.

The CoMP framework encompasses all the system designs allowing tight coordination between multiple

radio access points for transmission and/or reception. Three types of coordinated entities can be considered, as stated in [WIN+D14] and depicted in HFigure 1-1:

Remote radio units (RRU);

Cells, which involve intra-BS or inter-BS coordination;

Relay nodes (RNs).

Figure 1-1: Different instances of systems able to implement CoMP.

The coordination can either be distributed, by means of direct communication between the different sites,

or by means of a central coordinating node.

At a high level, downlink coordination schemes can be divided into two categories (this classification mostly follows the one 3GPP adopted in the study item for Long Term Evolution Advanced (LTE-A)

[3GPP36814]):

Coordinated scheduling and/or beamforming

Joint processing/transmission

The first category is characterized by that data to a single user equipment (UE) is instantaneously

transmitted from one of the transmission points, and that scheduling decisions and/or generated beams are coordinated in order to control the created interference. The main advantages of these schemes compared

to schemes involving joint processing/transmission (see below) are that the requirements on the

coordination links and on the backhaul are significantly reduced, since typically

only information on scheduling decisions and/or generated beams (and information needed for

their generation) need to be coordinated, and

user data do not need to be made available at the coordinated transmission points, since there is

only one serving transmission point for one particular UE.

The second category, joint processing/transmission, is characterized by that data to a single UE is

simultaneously transmitted from multiple transmission points, e.g. to (coherently or non-coherently)

improve the received signal quality and/or cancel actively interference for other UEs. This category of schemes puts higher requirements on the coordination links and the backhaul since user data need to be

made available at the multiple coordinated transmission points. The amount of data to be exchanged over

the coordination links is also larger, e.g. channel knowledge and computed transmission weights.

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In year 1, WINNER+ studied different aspects of CoMP extensively. These included system

architectures, different approaches and algorithms for performing the coordination, and the requirements

in terms of measurements, signalling, backhauling constraints, etc. these put on the system. The work is

reported in [WIN+D14]. Much attention was put on the second category of coordination schemes, i.e., joint processing/transmission. It was found that these schemes have potential to provide significant

performance gains, however to the cost of high requirements on the backhaul links in terms of latency and

capacity since user data, channel state information (CSI), and precoding weights needed to be shared

among the transmission points.

Hence, during year 2 WINNER+ put more focus on the first category of schemes, i.e., coordinated

beamforming, but also on how to reduce the backhauling requirements for schemes based on joint

processing. It’s worth noting that these requirements are being closely evaluated to assess the actual

feasibility of all the possible CoMP schemes, both from a technical and also from an economical point of

view. If low-latency exchanges are needed between BSs, then CoMP schemes will not be able to be

deployed everywhere but only in the areas where a suitable backhaul is present, or they will require

important investments from the operator to upgrade the backhaul. This could somehow limit the practical usability of the techniques.

Either in coordinated beamforming and in joint processing case, the notion of clustering is key to

coordinating beamforming and relaxed coherent joint processing. Indeed, “CoMP” means that several

base stations share some knowledge about users. However, as the number of users and BSs increase, the

signaling overhead required for the inter-base information exchange and the amount of feedback needed

from the users also increase. Therefore, cooperation should be restrained to a limited number of BSs. To

achieve this goal, the network is thus divided into clusters of cooperative cells. Cluster selection is

obviously a key to cooperation algorithms performance. Cluster formation may be static [Ven07] [BH07],

if the clusters remain fixed in time, or dynamic [PGH08]. Selection may be performed by a central entity,

i.e. in a network-centric manner, or in a per-user way, i.e. in a user-centric manner. Usually, network-centric clustering divides the network into a set of disjoint cluster of BSs, that is, one BS can belong only

to one cluster [BHA08], [PGH08], [MF07a] and [MF07b]. In contrast, in the user-centric clustering

approaches, one BS may belong to more than one cluster, depending on the parameter under

consideration [PBG+04], [PBG+08]. From the user point of view, this means that, in a given cell, each

user may have a different set of cooperating BSs. The concept of clustering, as used in WINNER+, is

closely related to those of CoMP cooperative sets and/or measurement sets as defined in 3GPP

[3GPP36814].

These different approaches are all considered in this document. Further details of these studies are given

in Sections 2.2 and 2.3. In addition, work has been also conducted on different aspects related to relaying

in the framework of CoMP, as can be seen in Section 3. It should also be mentioned that CoMP issues

such as scheduling and Radio Resource Management (RRM) are only implicitly dealt with in this document. They are considered in [WIN+D1.5] where upper layers are the main focus.

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2. Investigations on CoMP issues

As already mentioned above, the focus during year 2 is on CoMP solutions that have relaxed

requirements in terms of backhauling and complexity compared to the full blown joint processing

schemes that were studied during year 1.

The first area to be considered is transmission strategies in a Radio over Fiber (RoF) based CoMP architecture. In this study, different low-complexity transmission strategies in a distributed antenna

system based CoMP scheme are evaluated. More details can be found in Section 2.1 below.

The second area in focus is coordinated beamforming. Some different concepts for this are studied. Both

centralized and decentralized as well as non-codebook based and codebook based approaches are

investigated. Further details are given below in Section 2.2.

As to coherent multi-user multi-cell precoding techniques, reduced complexity approaches are proposed

in Section 2.3.

2.1 Assessment of transmission strategies in a RoF based CoMP architecture

2.1.1 Description

An example of a possible architecture of RoF based CoMP in a multi-cell environment can be a scenario

where a cell is covered by a RoF Central Unit (CU) and a number of distributed antenna modules called Remote Units (RU). The main processing modules are typically performed at the central unit which is

connected with the distributed antennas.

In traditional cellular systems the same area is covered by only a single high-power base station. In one

possible implementation of a cellular distributed CoMP system a certain number of antenna modules is

used to cover the same area, each adopting a lower power level, as illustrated in HFigure 2-1. The actual

number of distributed antenna modules would be determined by coverage, user densities, and other environmental factors. The entire cell coverage, as shown in Figure 2-1, is schematically represented by

an ideal circle containing smaller areas corresponding to the distributed nodes RU coverage spots. One

surrounding tier of six interfering cells has been considered in a unitary frequency reuse scheme,.

As it is known CoMP can include a centrally located or distributed processing among the collaborating

nodes, but at a first and simple level CoMP introduces multiple nodes transmissions schemes, also known as Distributed Antenna Systems (DAS). Evaluations regarding the distribution criteria of a CoMP scheme

are of high relevance, representing the starting point before evolving towards more efficient processing.

In particular, in distributed DAS CoMP, there are several possible transmission strategies using multiple

distributed antenna modules. Although many methods are possible, in the suggested proposal two

transmission strategies will be considered:

the power weighted transmission scheme

the single transmit selection scheme

The power weighted transmission scheme is based on the simultaneous transmission from the central unit

and all the distributed remote units, while maintaining constant the overall total transmitted power. The

most convenient power distribution scheme among the different nodes is chosen.

In the single transmit selection scheme, a certain method to choose from the nodes the one that should

transmit has to be found; a single node only is turned on depending on the outcome of the selected

method. Many different selection methods can be found in literature, such as maximizing the Signal to

Interference plus Noise Ratio (SINR) or capacity. A possible and simple scheme that will be adopted in

the following is the criterion of minimizing the propagation loss, that minimizes the required transmit

power (and hence the interference caused to other cells).

The use of distributed antenna systems is expected to reduce ICI and to improve SINR especially for

users near cell boundaries, which normally are performance limiting users, compared to conventional

cellular systems in an interference-limited multi-cell environment. As a result, distributed antenna

systems achieve lower symbol error probability and higher capacity than conventional cellular systems. It

will follow that distributed antenna architectures could appear to be one possible effective solution for

reducing ICI in an interference-limited cellular environment.

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Figure 2-1: The Distributed Antenna System (DAS CoMP) scheme.

History Continuation of the work in IR1.2,

Section 5.2.1

Duplexing mode FDD and TDD

Clustering mode (1) Static in power weighted scheme and

dynamic in transmit selection scheme

Clustering mode (2) User-centric

Codebook-based No

Data exchanges: users data Data plane transferred to all RUs in

power weighted scheme and to best RU in transmit selection scheme

Data exchanges: Channels Impulse

Responses

No

Data exchanges : others

Data exchange rate: slow or fast

None

We will derive the general downlink system model valid for a network made of C cells, with M single

antenna mobile users per cell, that can be referred both to a traditional cellular network (in which only 1

transmitter per cell is used) and to a CoMP network (in which more than 1 transmitter per cell is used). In

the model formulation we will refer to a CoMP system made of R single antenna remote units.

In general, the downlink system model of a CoMP cellular network can be expressed as:

nxPHy (2.1)

where M,C R C H is the channel matrix,

C R,M C P is the CoMP processing matrix, which depends

on the particular chosen CoMP configuration, ,1

1 2, ,..,T M

Mx x x C x is a complex vector that

contains the transmitted signal, ,1

1 2 M ,y ,.., yT My C y is a complex vector that contains the M

signals received by the M mobile users, and ,1MCn is the noise vector. The element ( )k

ijh of the

matrix H is the complex channel gain between any of the mobile users i (with 1 i M ) and any of

the remote units j (with 1 j R ), belonging to any of the considered cells k (with 1 k C ). The

element ( )k

jip of the matrix P is the square root of the transmission power applied at any of the

remote units j (with 1 j R ), transmitting towards any of the mobile users i (with 1 i M ),

belonging to any of the considered cells k (with 1 k C ). The model of equation (2.1) makes the

hypotheses of a flat fading channel as it occurs for example for each subcarrier in an Orthogonal Frequency Division Multiple Access (OFDMA) based system. The model given by the equation (2.1) can

be easily extended to the case of remote units and mobile terminals equipped with multiple antennas.

In the following we derive, as a means of example, the expression of equation (2.1) for the particular case

of 2C cells in the network topology (the respective sub-matrixes are separated by dashed lines),

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3R remote units equipped with a single antenna and 2M mobile users equipped with a single

antenna. In such hypothesis, equation (2.1) becomes:

2

1

2

1

)2(

32

)2(

31

)2(

22

)2(

21

)2(

12

)2(

11

)1(

32

)1(

31

)1(

22

)1(

21

)1(

12

)1(

11

)2(

23

)2(

22

)2(

21

)1(

23

)1(

22

)1(

21

)2(

13

)2(

12

)2(

11

)1(

13

)1(

12

)1(

11

2

1

2

1

)2(

)1()2()1(

2

1

n

n

x

x

pp

pp

pp

pp

pp

pp

hhhhhh

hhhhhh

n

n

x

x

y

y

P

PHH

(2.2)

where iy is the signal received at the i-th terminal and jx is the signal transmitted by the j-th remote

unit.

The particular expression of C R,M C P for CoMP transmit selection and for CoMP power weighted

schemes, when only one user per cell is considered, is as follows:

)2(

32

)2(

22

)2(

12

)1(

31

)1(

21

)1(

11

_

)2(

)1(

_

0

0

0

0

0

0

,

00

0

00

00

0

00

p

p

p

p

p

p

p

p

pwCoMP

ij

ij

selCoMPPP . (2.3)

In the above expression (2.3):

for the COMP transmit selection scheme (COMP_sel): the only non null term ( ) , 1,2k

jip for k is selected by the criterion, for instance, of minimizing the path-loss

(PL) between any of the remote units transmitting towards the considered mobile user: ( ) min (PL(j,i)), 1,2k

jij

p for k , for each given mobile user i ;

for the COMP power weighted scheme (COMP_pw): ( )

1

, 1,2R

k

ji BS

j

p P for k

.

where BSP is the total transmit power of the conventional base station.

2.1.2 State of the art

A first interesting paper by Choi-Andrews can be considered as reference ([CA07]) for DAS showing that

in addition to coverage improvements, DAS can also have potential advantages such as reduced power

and increased system capacity in a single cell environment. The paper analytically quantifies downlink

capacity of multi-cell DAS for two transmission strategies: selection diversity (where just one of the

distributed antennas is used) and blanket transmission (where all antennas in the cell broadcast data). In

particular, in the paper, the results have been drawn out analytically under some assumptions: the ergodic

capacity of cellular DAS versus the normalized distance from the home base station is derived when the CSI is known at the receiver end. In deriving the results, six RUs have been considered in the DAS

configuration. The transmit power of each distributed antenna module is a fraction of the transmit power

of the home base station. No fast fading modelling is considered when deriving the results. The

conclusions derived from this paper are that the single transmit selection scheme achieves the highest

throughput owing to the ICI reduction and macroscopic selection diversity.

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A second interesting paper can be considered as reference [SWO08]. In this paper the performance of a

DAS under time-varying frequency-selective fading based on a realistic channel model is investigated.

Specifically, by shifting the hexagonal cellular layout in the conventional system and using sectorized

antennas instead of omnidirectional antennas at each BS, the performance in terms of outage probability

and outage capacity improves by a large extent without a need for additional BS towers. The results in the

paper show that with the same total transmit power and bandwidth, DAS can reduce the ICI in a multi-

cell environment and improve the outage capacity especially near the cell boundary.

2.1.3 Expected performance or benefits

In this section some results of the performance of the above mentioned CoMP systems, obtained by

means of computer simulations, will be provided. A dedicated software tool has been developed. In order

to calibrate such a tool and to gain some initial insight in the DAS scheme, preliminary simulations have

been run aligning to the hypothesis used in [CA07]. The propagation effects encountered between either

the BS or the RUs towards the UE are modelled in the simulator and include path loss, shadowing and

fast fading. The path loss for a given distance is calculated by means of the Walfish-Ikegami analytical

model. The probability distribution function of the shadowing is assumed lognormal, so that the

shadowing expressed in dB can be modelled as a Gaussian random variable. Finally the fast fading is

modeled by randomly extracting for each user and in each simulation snapshot one channel matrix. As a

first approximation the channel coefficients have been considered independent. This analysis quantifies downlink capacity of multi-cell CoMP (calculated based on the ergodic Shannon capacity) for the two

considered transmission strategies: transmit selection scheme (where just one of the distributed antennas

is used) and power weighted scheme (where all antennas in the cell broadcast data). In particular, in the

paper, the results have been drawn out analytically under some assumptions:

the CSI is known at the receiver end.

six RUs have been considered in the DAS CoMP configuration and each RU is evenly located

on a common circle with radius cRr3

2 , where cR is the cell radius.

the transmit power of each distributed antenna module is 0.1P and the transmit power of the

home base station is 0.4P in DAS CoMP whereas the transmit power of the base station in the

conventional cellular system is P .

in the case of the power weighted scheme, the signals transmitted by the different RUs are

combined non coherently at the receiver end.

Starting from the same assumptions the simulations to derive a first estimation of the overall capacity in

the scenario of RoF based DAS CoMP have been performed. In the following HFigure 2-2, the ergodic

capacity versus the normalized distance from the home base station has been evaluated by means of

simulations. The single user capacity has been depicted as a function of the normalized distance of the UE

from the home base station moving from the cell center 0r to the cell edge 1r .

From the simulation results, it is clear that the transmit selection scheme achieves the highest throughput

due to the ICI reduction and macroscopic selection diversity. Although the achieved throughput of the

power weighted scheme in the cellular DAS is slightly lower than that of conventional omni-directional

cellular system near the home base station, due to reduced transmit power, the achieved throughput of the

power weighted scheme in DAS has substantially higher throughput beyond the normalized distance of

around 2

1r , and obtaining its maximum value in correspondence to the position of the RUs at

3

2r .

The presence of the latter local maximum (see Figure 2-2), in the case of DAS system deployment, is due

to the fact that when the user is moving towards the cell edge, the presence of the RUs (positioned at

3

2r ) enables to achieve a better throughput performance near the RU positions with respect to what is

achievable with a classic omnidirectional system.

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Figure 2-2: Ergodic capacity vs. the normalized distance from the home base station

2.1.4 Expected requirements on signalling and measurements

The proposed evaluation is based on theoretical distributed schemes aiming at the most convenient in

terms of capacity of the system. These schemes could require exchange of information in order to perform

the best selection, both in the case of power weighted or single transmit selection schemes; however, the

overall amount of exchanged data would be very small and suited with the capacity offered by RoF

architectures, to be considered as straightforward enablers.

2.1.5 Expected requirements on architecture and protocols

The presented schemes are quite generic, but they are studied in order to be applied to RoF architectures.

As a consequence the foreseen impacts are those of RoF schemes introduction, widely described in

[WIN+D14].

2.2 Coordinated beamforming concepts

Assuming linear transceiver processing, a CoMP system with N antennas is ideally able to accommodate

up to N streams without becoming interference limited. The inter-stream interference can be controlled or

even completely eliminated by a proper precoder selection. This is especially true in the coherent joint

processing case, where user data is conveyed from multiple BS antenna heads over a large virtual

Multiple Input Multiple Output (MIMO) channel.

The coherent multi-user multi-cell precoding techniques, however, have high requirements in terms of

signalling and measurements. In addition to the complete channel knowledge of all jointly processed links, a tight synchronisation across the transmitting nodes and centralized entities performing scheduling

and computation of joint precoding weights is required in order to avoid carrier phase drifting at different

transmit nodes. A large amount of data needs to be exchanged between the network nodes. Thus, high-

speed links, such as optical fibres or dedicated radio links, are needed.

Another form of coordinated transmission is a dynamic multi-cell scheduling and interference avoidance,

where the network nodes coordinate their transmissions (precoder design, scheduling) in order to

minimize the inter-cell interference. The carrier phase coherence between the transmit nodes is not

required, since each data stream is transmitted from a single transmission point. Thus, the non-coherent

coordinated multi-cell transmission approaches have somewhat looser requirements on the coordination

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and the backhaul, but could potentially still need centralised resource management mechanisms. This

family of methods we refer to as coordinated beamforming.

Coordinated beamforming can be carried out in different ways, some examples are given in [VAL+06],

[SCW+07], and [YSL+08]. In WINNER+, both centralized and decentralized as well as non-codebook

based and codebook based approaches are investigated.

One of the earliest studies that can be considered to fall into the category of coordinated beamforming

considered the minimum power beamformer design, also known as the sum power minimization under

the minimum SINR constraint per user. This problem has been extensively studied over the past decade.

In order to solve this problem for single-antenna users, the uplink-downlink SINR duality was utilized in [RLT98] and [VM99] [SB04] for multi-cell and single-cell cases, respectively. The duality property was

utilized to develop iterative algorithms for calculating the optimal beamformers and power allocations.

Furthermore, an optimal solution for the problem of maximizing the jointly achievable SINR margin

under a total transmitted power based on the uplink-downlink duality was proposed in [SB04]. A

modified form of the single-cell multiuser beamforming algorithm in [SB04] is presented in section 2.2.1

for coordinated beamforming concept, in which the beamforming vectors and power allocation are found

to maximize the jointly-achievable SINR margin under per-transmitter power constraints

It was shown in [BO99] and [BO01] that the minimum power beamforming problem can be formulated as

a second order cone program (SOCP) [BV04] for rank one channels (a single receive antenna per user or

fixed receiver beamformers). A coordinated multi-cell scenario was considered, where all the transceivers are jointly optimized while considering the other-cell transmissions as inter-cell interference. Similarly to

[BO99] and [BO01], the power minimization problem was cast as an SOCP in [WES06]. In addition, an

efficient algorithm based on fixed point iterates was proposed. Furthermore, the worst case SINR was

maximized subject to a total power constraint based on generalized eigenvalue problem in [WES06]. The

SINR optimization problem can also be carried out via power optimization using bisection method. A

similar approach based on bisection method was proposed in [SVH06].

The solutions above need complete channel knowledge between all pairs of users and BSs, and hence,

they require centralized resource management mechanisms. It is fair to assume that each BS can measure

at least the channels of all cell edge users, independent of which BS they are identified with, for example,

during the uplink (UL) transmission phase of the Time Division Duplex (TDD) frame. In such a case,

each BS could simply form nulls (zero forcing) towards a set of users served by other BSs while optimizing the transmission for the set of served users. In a more general form of operation, each BS can

employ less restrictive interference balancing criteria (allowing some controlled interference), and take

that interference into account when designing the precoders in the adjacent BSs. This obviously requires

some extra signalling across the backhaul network but may result in improved performance in certain

scenarios.

An efficient iterative algorithm based on uplink-downlink duality was introduced in [DY08]. The multi-

cell minimum power beamformer design problem was solved via a dual uplink problem, where the

downlink beamformers are designed locally based on the reciprocal uplink channels and virtual uplink

powers of all users. This allows also for a distributed implementation, where virtual uplink powers are

exchanged between BS nodes in a coordinated manner. An alternative distributed method is presented in section 2.2.2 in which the optimal minimum power beamformers can be obtained locally at each BS

relying on inter-cell interference terms exchange between adjacent BSs.

An alternative scheme compared to those previously reported makes use of a report from the UE about

the beams that the neighbouring BSs should avoid or favour in order to limit the interference. Such an

approach is only possible when codebook-based beamforming is employed, so that the beams that a

potential interferer can use are known in advance at the UE. The report of the interfering beams requires

only a modest increase in feedback overhead since only the index of the beam(s) needs to be reported.

Nevertheless, low-latency communication links between the coordinated BSs are required in order to

exchange the information about the served UEs at a given time instant. This concept has been presented

in [WIN+D14], and is further investigated in section 2.2.3.

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2.2.1 Centralized non-codebook based coordinated beamforming

The first coordinated beamforming concept in this deliverable assumes fixed coordination clusters, where

the coordination is carried out in a centralized fashion. The coordination has the following main

characteristics:

(i) Transmit powers and beamforming weights are jointly adapted for all UEs in the coordination

cluster in order to minimize caused interference,

(ii) Non-coherent, i.e. non-frequency dependent: beamforming weights are constant for all

frequencies,

(iii) Each UE receives transmissions from a single transmission point, i.e. no cooperative joint

transmission from multiple transmission points takes place.

(iii) Spatial Division Multiple Access (SDMA): one transmission point serves multiple UEs as in

multiuser MIMO.

Hence, this coordinated beamforming scheme can simply be viewed as coordinated multiuser MIMO

where the beamforming weights are jointly adjusted for all transmission points in the coordination cluster

in order to minimize the caused interference.

History N/a

Duplexing mode FDD or TDD

Clustering mode (1) Static (but can also be implemented in a

dynamic fashion)

Clustering mode (2) Network centric

Codebook-based No

Data exchanges: users data No

Data exchanges: Channels Impulse

Responses

No

Data exchanges : others

Data exchange rate: slow or fast

Transmit channel correlation, precoding

weights

Slow rate

2.2.1.1 Description

Consider a coordination cluster with N transmission points, each equipped with an antenna array

comprising TM antenna elements, and K UEs equipped with RM receive antennas. Assuming

Orthogonal Frequency Division Multiplexing (OFDM) transmission, the baseband signal received by UE i for frequency f in the downlink is given by

( ) ( ) ( ) ( )i i iy f H f x f w f (2.4)

where the R TM NM matrix )( fH i is the composite channel between all transmission points and UE i;

)( fwi represents thermal noise, including inter-cluster interference which is assumed to be white

Gaussian distributed with covariance 2( ) ( )

H

f i i iE w f w f I ; and )( fx is an 1TNM vector

representing the sum signal transmitted from all transmission points, given by

K

iiii

K

ii

fsupfxfx11

)()()( (2.5)

where ( )is f is a modulation symbol (drawn from a unit-variance symbol alphabet) which is transmitted

to UE i using the transmit power and beamforming vectors ip and iu , respectively. The beamforming

vectors are normalized to have unit power

1H

i iE u u for i =1,2,…K (2.6)

The average SINR for UE i, assuming Maximum Ratio Combining (MRC) reception, is given by

2

),(SINRi

ikki

H

kk

ii

H

ii

iuRup

uRuppU

(2.7)

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where U is the matrix of transmit beamformers given by KuuuU ,, 21 , p is the vector of transmit

powers, and iR is the transmit correlation matrix for UE i, given by

Niiii

H

ii RRRdiagfHfHR ,2,1,f ,,,)()(E . (2.8)

since the transmission points are separated by large distances and therefore their antennas are mutually

uncorrelated.

The considered coordinated beamforming scheme is a modified form of the multiuser beamforming

(MuBF) algorithm in [SB04] in which the beamforming vectors and power allocation are found to

maximize the jointly-achievable SINR margin:

i

i

KipU

pUC

),(SINRminmax

],,2,1[, (2.9)

under per-transmitter power constraints

NnPpnSi

i ,2,1,max

where i is the target SINR for UE i, nS is the set of UEs connected to transmission point n and maxP is

the maximum transmission point transmit power.

The beamforming vectors and downlink power allocations are found iteratively using the uplink-

downlink duality theorem [RLT98] [VM99] [SB04] which states that the downlink broadcast channel has

a virtual dual uplink multiple access channel which has the same SINR achievable regions as the

downlink and the same beamforming vectors achieve the SINRs in both links.

2.2.1.2 Expected performance or benefits

As for most beamforming schemes it can be expected that the most appropriate scenarios for coordinated

beamforming have low angular spreads which are found in urban, suburban and rural environments with above roof-top antenna deployment. Due to low feedback requirements (see below) coordinated

beamforming is especially interesting in Frequency Division Duplex (FDD) mode, where joint processing

might be difficult to realize in a practical system due to the high feedback requirements. It can also be

expected that coordinated beamforming is robust to user mobility, hence making it an interesting

alternative both in low and high mobility scenarios.

A detailed system level performance evaluation of the herein described coordinated beamforming scheme is provided in Appendix A.1, where its performance is also compared to that of joint processing based on

zero-forcing (ZF) precoding that was evaluated in Appendix B.7 in [WIN+D14]. A summary of the

results provided in Appendix A.1 is given in Table 2-1.

Table 2-1: Summary of system level performance results

Scenario Transmission scheme Cell spectral efficiency

[bps/Hz/cell]

Cell edge user spectral

efficiency [bps/Hz]

3GPP Case 1

No CoMP 2.56 0.074

Joint processing based on ZF

3.81 0.108

Coordinated

beamforming

3.01 0.078

ITU Urban Macro

No CoMP 1.32 0.035

Joint processing based

on ZF

1.20 0.026

Coordinated

beamforming

1.97 0.053

ITU Rural Macro

No CoMP 1.52 0.050

Joint processing based

on ZF

1.05 0.019

Coordinated

beamforming

2.64 0.071

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As can be expected, in the low mobility scenario 3GPP Case 1 [3GPP25814] joint processing based on

ZF performs very well; the gain over the reference non-CoMP system based on LTE release 8 is almost

50% both in cell spectral efficiency as well as cell edge user spectral efficiency. Coordinated

beamforming does also perform reasonably well here; the gain over the non-CoMP is 15-20% in cell

spectral efficiency, while the cell edge performance is only slightly better.

In scenarios with higher user mobility, e.g. International Telecommunications Union (ITU)Urban Macro

[ITURM2135] where the users are moving in vehicles at 30 km/h, it can be seen that the performance of

joint processing based on ZF degrades and is actually slightly worse than that of the reference non-CoMP

system. This is of course due to the fact that short-term channel state information needed for the ZF

precoding gets outdated, hence resulting in that the applied precoding weights are invalid. Coordinated

beamforming, on the other hand, is robust to user mobility and works well in this scenario. The gain over

the non-CoMP system is in the order of 50%, both in cell spectral efficiency and cell edge user

performance. The relative gain over the non-CoMP is higher than in 3GPP Case 1, which most probably

is explained by the fact that we now have outdoor users, and also a line-of-sight (LoS) component in the

channel model, which together makes it easier to exploit the directivity properties of the beamforming.

Finally, in the ITU Rural Macro scenario where the users are moving at 120 km/h, it can be seen that the

joint processing based on ZF breaks down even further, and that the performance now is far below that of the non-CoMP system. Again, it is demonstrated that coordinated beamforming is robust to user mobility,

and the gain over the non-CoMP system is now 70% in cell spectral efficiency and about 40% in cell edge

user spectral efficiency.

2.2.1.3 Expected requirements on signalling and measurements

The considered coordinated beamforming scheme requires transmit channel correlation estimates for

antenna arrays at all transmission points in the coordination cluster. These estimates can be obtained by

means of UE-specific uplink sounding reference signals. In case of TDD it is straightforward to apply the

estimated correlation matrices for downlink transmission, while for FDD it may be necessary to perform a

frequency translation of them. Methods for this can be found in e.g. [CHC04]. The uplink sounding

reference signals can be broadband as frequency-dependent channel information is not needed to estimate

transmit channel correlation. Frequency-dependent channel information is only needed for the serving

transmission point for channel-dependent link adaptation. The reference signals may have low time

density as transmit channel correlation does not change with fast fading.

2.2.1.4 Expected requirements on architecture and protocols

Since each UE receives data from a single transmission point, there is no need to share user data between transmission points. The information that need to be exchanged between the transmission points and the

central processor are the transmit channel correlations that are estimated at each transmission point, and

the resulting computed precoding weights. Since the transmit channel correlation does not change with

fast fading and the precoding weights are non-frequency dependent, the capacity requirements on the

backhaul connection is rather limited, however, the latency has to be sufficiently low in order to cope

with scheduling updates.

2.2.2 Decentralized non-codebook based coordinated beamforming

The second concept is a distributed solution for the coordinated multi-cell multi-antenna minimum power

beamformer design problem with single-antenna users [TPK09b] [TPK09c]. The minimum power

beamformers are obtained locally at each BS relying on limited backhaul information exchange between

adjacent BSs. Hence, this concept operates in a decentralized manner in contrast to the concept described

in Section 2.2.1 above.

The original minimum power beamformer design problem is reformulated such that the BSs are coupled

by real-valued inter-cell interference terms. The coupled interference terms are handled by taking local

copies of the terms at each BS and enforcing consistency between them. Thus, the coupling in the interference terms is transferred to coupling in the consistency constraints, which can then be decoupled

by a standard dual decomposition approach leading to a distributed algorithm. The proposed method is

able to guarantee feasible solutions even if the interference information is outdated or incomplete, at the

possible cost of increased sum power. In addition, the proposed approach allows for a number of special

cases, where the backhaul information exchange is reduced at the cost of somewhat sub-optimal

performance.

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History New

Duplexing mode TDD

Clustering mode (1) Static

Clustering mode (2) Network centric

Codebook-based No

Data exchanges: users data No

Data exchanges: Channels Impulse

Responses

No

Data exchanges : others?

Data exchanges rate: slow or fast

Real-valued inter-cell interference

terms

Fast rate

2.2.2.1.1 Description

Consider a cellular system that consists of BN BSs with TN transmit antennas and K user each with

single receive antenna. A set U with size UK includes all users active at the given time instant,

while a subset Ub U includes the users bk U allocated to BS b , B,...,1 Nb . The index of serving

BS for user k is denoted as kb . The signal ky received by the user k consists of the desired signal,

intra-cell and inter-cell interference, and it can be expressed as

k

bb k

kbkbkb

ki

ibkbkbkbkbkbk naaay

k bkb

kkkkkk

U

,,,

\U

,,,,,, xhxhxh

where the vector TC,N

kb x is the transmitted signal from the b ’th BS to user k , kn ~ ),0( 0NCN

represents the additive noise sample with noise power density 0N , and T1,, C

Nkbkba

h is the channel

vector from BS b to user k with large-scale fading coefficient kba , . The elements of kb,h are

normalized to have unitary variance. The transmitted vector for user k is generated at BS b as

kkbkb d,, mx , where TC,N

kb m is the pre-coding vector and kd is the normalized complex data

symbol.

Let TC,N

kb m be an arbitrary transmit beamformer for the user k from BS b . By denoting the inter-

cell interference term from BS b to user k as kb,ζ , and relaxing the term as 2

U

,,,2,ζ

bi

ibkbkbkb a mh ,

the SINR formula can be written as

K

ki

ibkbkb

bb

kb

kbkbkb

k

kb

kkk

k

kkk

aN

a

\U

2

,,,2

,0

2

,,,

ζ mh

mh (2.10)

where the index of the serving BS for the user k is denoted as kb .

The system optimization objective is to minimize the total transmitted power subject to fixed user-

specific SINR constraints kk . This problem can be formulated as

B

U

2

2,

2,

2

U

,,,

k,

1

,...,1,

,Uζ

k s.t.

min B

NbP

bka

P

b

k

kb

bkb

i

ibkbkb

k

N

b

b

b

b

m

mh

(2.11)

where the variables are B,...1, NbPb , KkN

kb ,...1,C T, m and bk bkb ,Uζ , . Vector ζ is

defined including all the inter-cell interference terms as

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T

UU,1U,UU1,1U1,BBBBB111

ζ,...,ζ,...,ζ,...,ζ

NNN NNζ , where bb k UU . The second constraint

guarantees that the interference generated from a given BS b cannot exceed the user specific thresholds

bkb k Uζ , .

Observe that BSs are coupled in the SINR constraints by the terms kb,ζ . If the interference terms were

fixed, the problem would decouple and the transmitted power could be separately minimized at each BS.

The coupled SINR constraints are addressed by introducing local auxiliary variables bζ , as well as,

additional consistency constraints that require the local variables to be equal. This results in the following

optimization problem:

b

NbP

bka

bk

P

b

b

k

kb

bb

kb

i

ibkbkb

bkb

k

N

b

b

b

b

,

,...,1,

,Uζ

U , s.t.

min

B

U

2

2,

2,

2

U

,,,

1

B

ζζ

m

mh

(2.12)

where the variables bP , KkN

kb ,...1,C T, m and b

ζ are local for each BS b , and bk is as k but

with bkb,ζ instead of kb,ζ .

In order to obtain a distributed algorithm, a dual decomposition approach is taken where the consistency

constraints are relaxed by forming the partial Lagrangian as

B

U

2

2,

2,

2

U

,,,

1

T

1

,...,1,

,Uζ

U , s.t.

min BB

NbP

bka

bk

P

b

k

kb

bb

kb

i

ibkbkb

bkb

k

bN

b

b

N

b

b

b

b

m

mh

ζζν

(2.13)

where the variables are B,...1, NbPb , KkN

kb ,...1,C T, m and b

ζ , and bν are consistency prices

for each BS b . For fixed bν , the distributed version of the problem reduces to

B

U

2

2,

2,

2

U

,,,

T

,...,1,

,Uζ

U , s.t.

min

NbP

bka

bk

P

b

k

kb

bb

kb

i

ibkbkb

bkb

k

bbb

b

b

m

mh

ζν

(2.14)

where the variables are the same as in the previous problem. The resulting convex sub-problems can be

locally and independently solved as SOCPs in each BS b with the knowledge of consistency prices bν

(see, e.g., [TPK09a] for more details). The master problem for the dual decomposition is

B

1

T maximize

N

b

bbbg ζνν (2.15)

where the variables are bbν , and where bbg ν is the dual function achieved as the minimum value of

the partial Lagrangian for given bν . This can be solved iteratively with the following updates [Boy04]

[PC06]:

btttt bbb ,1 ζζνν (2.16)

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where t is the iteration index, is a positive step-size and B

B...1 1B N

NttNt 1ζζζ is the

average of all BS-specific inter-cell interference vectors, which need to be exchanged between coupled BSs. Since the original problem is convex, the algorithm is guaranteed to converge to the optimal

(centralized) solution, where bb ζζ , if the step size is sufficiently small [Boy04] [PC06], and the

channels kbkb ,, h remain constant during the iterations.

The intermediate iterates tbζ in the dual decomposition do not necessarily result in a feasible

solutions, i.e., kk for some k . However, a feasible set of beamformers kb,m can be always

achieved by using the average vector tζ for each BS. In this case, one additional sub-problem per BS

has to be solved. Thus, a feasible set of kk can be guaranteed even if the update rate of tbζ

between BSs is slower than the channel coherence time, at the possible cost of increased sum power.

The dual decomposition approach allows for a number of special cases, where the backhaul information

exchange is reduced at the cost of somewhat sub-optimal performance. Some possible scenarios are listed below:

BS-specific inter-cell interference constraint, bbkb k Uζζ ,

One common constraint for all BSs (within a given joint processing area), bkkb ,ζζ , .

Zero-forcing for the inter-cell interference, bkkb ,0ζ , .

In time-correlated fading with high mobility and/or with low backhaul information exchange rate the

interference terms kb,ζ may become quickly outdated. This may result in a high peak in the transmitted

power as the interference constraints are mismatched with the actual channel realizations. The ZF solution

( bkkb ,0ζ , ) can be always calculated in each BS based on the available local information. In some

time instants, the ZF solution would result in a lower sum power than the distributed solution relying on

the backhaul information exchange. In such a case, BS b simply sets ZF−mode(b) state active and sends

a message to its neighbours. Consequently, the operation is reverted to the normal mode as soon as the

resulting transmitted power is again below the ZF mode. The distributed algorithm with ZF mode

selection is summarized in Appendix A.2.

2.2.2.2 Expected performance or benefits

A detailed performance evaluation of the proposed concept is provided in Appendix A.2. Some of the

main results from Appendix A.2 are presented in this section. Table 2-2 presents the average sum power

of 4,2,4,, TB NNK system for 0 and 10 dB SINR target per user. Different coordinated

beamforming cases and two zero-forcing approaches are compared with coherent multi-cell beamforming

case at the cell edge.

Table 2-2: Main performance results of decentralized coordinated beamforming

Transmission scheme Average sum power

[dB]: 0dB SINR target

per user

Average sum power

[dB]: 10dB SINR target

per user

Coherent multi-cell

beamforming

-1.45 9.71

Coordinated beamforming: user-

specific interference constraint

3.90 19.44

Coordinated beamforming: BS-

specific interference constraint

4.20 19.96

Coordinated beamforming:

common interference constraint

4.88 23.96

Zero-forcing for inter-cell

interference

8.92 24.81

Zero-forcing for both intra- and

inter-cell interference

15.21 25.21

As expected, the coherent multi-cell beamforming greatly outperforms the coordinated beamforming

cases at the cell edge. All the three coordinated beamforming cases with inter-cell interference constraints

have very similar performance. Thus, the loss from sub-optimal signaling is minor. The coordinated

beamforming cases require about 5-6 dB more power than the coherent case in order to meet the 0 dB

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SINR target. There is a large gain from the optimal intra-cell beamformer design (zero-forcing for inter-

cell interference) as compared to the channel inversion (zero-forcing for both intra- and inter-cell).

The loss from sub-optimal signaling increases significantly for the case with one common constraint

when the SINR target is 10 dB. However, the loss is still minor in the BS-specific constraint case. Also,

the gain from the optimal intra-cell beamformer design is greatly reduced as compared to the channel

inversion. In general, the difference between the zero-forcing and coordinated beamforming cases with

inter-cell interference constraints is reduced significantly. A more detailed performance evaluation with

wide range of numerical examples is provided in Appendix A.2.

2.2.2.3 Expected requirements on signalling and measurements

The objective of this proposal is to reduce the traffic required between the cooperating BSs so that full

CSI need not be shared and that precoders are computed locally by each BS. Information about the

allowed inter-cell interference levels taken in different cells must be exchanged between adjacent BSs.

However, a master-slave structure between adjacent BSs might be required if sophisticated user-to-BS allocation algorithms are used.

2.2.2.4 Expected requirements on architecture and protocols

The studied concept does not require tight frequency synchronization or sharing of user data between

BSs. Furthermore, the required information exchange rate between BSs can be slow if the channels are

slowly varying [TPK09b]. However, it is anticipated that existing 3GPP X2 type backhaul interface might

not be able to support the requirements of this concept.

2.2.3 Codebook-based coordinated beamforming

Coordinated beamforming aims at avoiding collisions of beams originating from neighbour cells. The

third coordinated beamforming concept studied in Winner+ is based on the codebook-based precoding

which already exists in LTE Rel-8. The main idea is to make use of a report from the UE of a Precoding

Matrix Index (PMI) indicating either the most interfering (MI) beam, or the least interfering (LI) beam

received from an interfering cell. The serving cell communicates to coordinated interfering cells the time-

frequency resources that will be used for transmission to the scheduled UE, together with the MI/LI-PMI

reported by this UE. These pieces of information will then act as constraints for the coordinated cells'

schedulers, which should try as much as possible to avoid/favour the reported MI/LI-PMI on the

associated resources for their own transmission.

This concept was presented in section 2.1.3.1.2 in [WIN+D14]. This section proposes a practical solution

to implement this concept in a decentralized way (i.e. without a central control entity), and presents

preliminary results to assess its performance.

The table below summarizes the features of the concepts that are relevant for the system concept work.

History Continuation of the work in D1.4,

Section 2.1.3.1.2

Duplexing mode FDD and TDD

Clustering mode (1) Dynamic

Clustering mode (2) User-centric

Codebook-based Yes

Data exchanges: users data No

Data exchanges: Channels Impulse

Responses

No

Data exchanges : others

Data exchanges rate : slow or fast

Request to be master including an indication of the resources on which the

UE will be served, and the PMIs to

avoid/favour.

Each time a cell-edge UE is scheduled

2.2.3.1 Description

The proposed solution is described in the following, with a particular emphasis on the following points:

UE measurements and reports

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Cluster formation

Implementation of scheduling restrictions

UE measurements and reports

Each UE determines

the PMI from its serving cell that maximizes the useful signal received power

the PMI(s) from its dominant (long-term) interfering cells that either

o maximize the interference power. This type of PMI will be called Most Interfering PMI

(MI-PMI) in the following;

o or minimize the interference power. This type of PMI will be called Least Interfering

PMI (LI-PMI) in the following.

If only single-layer beamforming is considered, it can be shown that the MI/LI-PMI from cell I

maximizing/minimizing the interference power after MRC reception can be determined as follows:

2)()()()((I) minargmax/argPMIMI/LI SSHIHI

ii

uHHv

Where )( I

iv is the i-th precoding vector at interfering cell I, )( I

H is the channel matrix from interfering

cell I, )(S

H is the channel matrix from the serving cell and )(S

u is the preferred precoding vector from

the serving cell.

At least one LI/MI-PMI is reported to the serving cell for each dominant interferer (the actual number of

interferers to consider being possibly configured by the network). Several MI/LI-PMIs can be reported in

order to provide more flexibility/information to the scheduler, at the price of an increase radio feedback

overhead.

In addition, the UE reports periodically to its serving cell the cell identifiers of its dominant interferers.

The dominant interferers can be determined on a long-term basis in order to limit the feedback overhead.

Cluster formation

The clusters are formed as follows: when a cell scheduler selects a UE identified as being at the cell edge

(which can be determined from UE measurements, e.g. the Reference Signal Received Quality

standardized for LTE), the cell sends a message to the cells reported by the UE as being the most

interfering, in order to request to become their master. Such a message will be referred to as a "master

request" in the following.

The master request is accompanied with the index of the beam whose use should be avoided/preferred at

the slave cell, as well as the indication of the resources for which the constraint will apply. Note that the

master requests can be sent several transmission time intervals (TTIs) in advance if the latency of the

communication links between the BSs is not low enough. This imposes that the scheduler takes decisions

several TTIs before the actual transmission. Note that such delay still allows channel-dependent

scheduling provided the UE mobility is low, which is the situation primarily envisioned for CoMP in

LTE-Advanced.

Obviously, the master/slave role of a cell can depend on the frequency resources: on some resources the

cell could be master, whereas it would be slave on some others. For the sake of simplicity, we consider

implicitly only one set of resources in the following, where a cell can be either master over all the

resources, or slave over all the resources.

In case of reception of master requests from several potential master cell, or if a candidate master receives

a master request from another cell, then a contention resolution procedure has to be applied in order to

determine the master/slave role of each cell. Such a contention procedure is out of the scope of this study,

but it should be designed in order to ensure fairness between the cells.

Note that although it is possible that one cell be the slave of several masters, increasing the number of

masters increases the constraints on the scheduler, which may severely limit the scheduler flexibility and

thus impact the system performance. Therefore, in this study we assume that one cell can be slave of only

one master.

Implementation of scheduling restrictions

Once the master/slave roles have been established, the scheduler of the slave cell applies the restriction

communicated by the master cell:

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If the coordination mode is so that the most interfering beam(s) is indicated to the slave cell, the

latter tries as much as possible to serve a UE that requests to be served in another beam.

If the coordination mode is so that the least interfering beam(s) is indicated, the slave cell tries as

much as possible to serve a UE that requests to be served in this beam.

The most interfering beam avoidance constraint is the easiest to satisfy, since it puts the lowest

requirements on the scheduler: consider a codebook with 16 codewords; if one is forbidden, 15

codewords remain allowed. It is therefore much easier to find a UE requesting to be served in one among

15 beams, than one requesting to be served in one particular least interfering beam. If the number of

reported beams is increased (at the price of a larger feedback overhead), the feasibility of satisfying the

two constraints gets closer. Nevertheless, the most interfering beam approach remains more advantageous

even in the case of several reported beams, since in case it is not possible to find a UE willing to be served in one of the allowed beams, relaxing the constraints (e.g. forbidding only the N dominant beams

out of M reported beams, with N<M) will still provide some gains compared to no coordination. On the

contrary, such a constraint relaxation is not possible with the least interfering beam approach.

In the case where no UE can be found to meet the scheduler constraints (even with constraints relaxation),

two options are possible:

1. Ignoring the constraints and serving any UE, like without coordination

2. Serving no UE at all, i.e. silencing the cell on the coordinated resources.

Option 1 leads to the same instantaneous performance as in the no coordination case, whereas option 2

ensures the protection of cell-edge UEs, at the price of a potential cell throughput reduction since some

resources are not used part of the time.

Additional considerations

If Resource Quality Indication Reference Signal (RQI-RS) is used, as proposed in [3GPP09], no

confirmation/acknowledgement is needed from the slaves to the master in order to allow for proper link

adaptation.

The coordination process requires no central control entity since it relies on a master/slave principle

between cells. Moreover, even negotiation between the cooperating cells is not necessary provided an

efficient contention resolution mechanism is used, thus enabling a low latency of the coordination

mechanism. Indeed, in that case the cell receiving the master request either accept the request and

behaves as requested, or do not accept the request: the performance will then be identical to the no

coordination case.

2.2.3.2 Expected performance or benefits

This section provides preliminary results about the gains of coordinated beamforming according to the

proposed method versus uncoordinated beamforming. The considered scenario is similar to 3GPP case 1

[3GPP25814] with full buffer traffic model. A simplified class III (Snapshot/quasi-static based) system-level simulator has been used, with the following simplifications:

Only Time Division Multiple Access (TDMA) is modelled (i.e. no OFDMA, or OFDMA with

only one physical resource block);

Frequency-flat fast fading channel coefficients are randomly drawn according to a unit-variance Rayleigh law, with no spatial nor time correlation;

No HARQ retransmission;

Perfect link adaptation;

Perfect channel estimation;

The user instantaneous spectral efficiency is computed from the post-receiver SINR via the

Shannon formula.

More details on the simulation conditions are given in Appendix A.3.

The table below summarizes the results obtained for the most interfering beam approach, for 1 and 3 MI-

PMI. Note that feedback of 1 and 3 MI-PMI for 3 coordinated cells leads to multiply the PMI feedback

overhead of LTE Rel-8 by 3 and 7, respectively. In case no UE is found to meet the scheduler constraints,

the constraints are ignored.

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Table 2-3: Performance results for codebook-based beamforming (CBF) (MI-PMI approach).

No coordination CBF with 1 MI-PMI CBF with 3 MI-PMI

Cell spectral efficiency (Bit/s/Hz) 4.49 4.43 (-1%) 4.62 (+3%)

Cell-edge user spectral efficiency

(Bit/s/Hz), measured at the 5%-

tile of the user throughput

cumulative distribution function

(CDF)

0.16 0.18 (+13%) 0.19 (+18%)

Coordinated beamforming with the proposed scheme provides only moderate gains in cell-edge

performance compared to non-coordinated beamforming in a realistic coordination setup (3 coordinated

cells, no central control entity): +13% for 1 MI-PMI and +19% for 3 MI-PMI. No significant change in

cell throughput is observed. Note that these results are only preliminary, and in particular use a very

simple scheduler. A more complete performance evaluation using a full LTE-Advanced simulator will be

reported in deliverable [WIN+ D4.2].

2.2.3.3 Expected requirements on signalling and measurements

The requirements on signalling and measurements are as follows:

Measurements required at the UE:

o identification of its N (configurable by the network) dominant long term interferers –

can be updated with a low frequency, depending on the UE velocity;

o MI/LI-PMI(s) from its N dominant long term interferers – has to be done with the same

periodicity as the identification of the preferred beam from the serving cell.

Feedback from the UE to its serving cell:

o N (configurable by the network) dominant long-term interferer identifiers – low-

frequency update;

o MI/LI-PMI(s) from each of the N dominant long term interferers - same periodicity as

PMI feedback.

Coordination messages from the candidate master to the BSs managing the prospective slave

cells:

o master request message including an indication of the resources on which the UE will

be served, and the PMIs to avoid/favour - each time a cell-edge UE is scheduled.

2.2.3.4 Expected requirements on architecture and protocols

Requirements on architecture include inter-BS communication links with sufficiently low latency in order

to exchange the master request messages. A contention resolution mechanism is needed for the

master/slave role attribution.

2.2.3.5 Conclusion

This section has described a practical way to achieve coordinated beamforming in a decentralized way, in

the sense that no central control entity is needed. The proposed studied approach is based on codebook-

based beamforming. Compared to Rel-8 LTE, additional feedback of PMI(s) for each considered

coordinated interfering cell is required. In addition, fairly low-latency communication links are needed

between BSs in order to convey master request messages. Preliminary results obtained from a simplified

simulator have shown moderate gains compared to non-coordinated beamforming (cell-edge performance

gains of +13% for 1 MI-PMI and +19% for 3 MI-PMI, with no significant cell-throughput gain/loss). Note that these results have been obtained with a very simple scheduler which did not include any

optimization to account for the coordination. Evaluation results of this scheme in a complete LTE-

Advanced class III simulator will be provided in deliverable [WIN+ D42].

2.3 Joint processing with relaxed requirement

One of the major drawbacks related to the implementation of joint processing as the number of users and

BSs increases is the large signaling overhead required for the inter-base information exchange and the

amount of feedback needed from the users. Therefore, one of the main challenges is the design of

efficient algorithms and principles that could reduce the complexity requirements. To achieve this goal,

one of the areas of research is leading to solutions that restrict the joint processing to a limited number of

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BSs or areas in the system. In these approaches, the network is typically divided into clusters of cells, and

the joint processing schemes are implemented within the BSs included in each cluster. The cluster

formation can be static [Ven07] [BH07], if the clusters remain fixed in time or dynamic [PGH08].

2.3.1 Performance of joint processing schemes under varying CSI requirements

In this contribution, different CoMP transmission schemes are characterized and compared within a static

cluster of BSs. Notice that the cluster of BSs under consideration can be formed using the approach of

contribution in section 2.3.2. The performance of the proposed schemes is evaluated over the cluster area,

in order to analyze the impact of a non-uniform distribution of users. This study is carried out as a first

step towards designing adaptive CoMP transmission schemes that could support mobile users. Further

details of this investigation can be found in Appendix A.4

History New

Duplexing mode FDD (TDD)

Clustering mode (1) Static

Clustering mode (2) PJP: user-centric

Codebook-based No

Data exchanges: users data -CJP: Yes

-PJP: Only within transmitting BSs

-DJP: Only during the scheduling phase

Data exchanges: Channels Impulse

Responses

-CJP: Yes

-PJP: Only within transmitting BSs

-DJP: No

Data exchanges : others

Data exchanges rate slow or fast

- CJP and PJP: precoding weights

- DJP: exchange of interference level

experienced by the user

2.3.1.1 Description

We consider a static cluster of BSs. Within the cluster, three different joint processing schemes are

considered, which result in different requirements both in terms of inter-base information exchange and

amount of feedback from the users:

Centralized Joint Processing (CJP): global CSI is available at the transmitter side, and the BSs

within the cluster jointly perform the power allocation and the design of the linear precoders.

Partial Joint Processing (PJP): a particular case of the CJP scheme, it defines different stages of

coordination between BSs. Coordination degrees are obtained arranging an active set or subset

of BSs for each user in the cluster area. Hence, a user only receives its data from the subset of

BSs included in its active set [BPG+08].

From the system point of view, three benefits are provided: feedback reduction (users only feed back channels with an acceptable quality), lower inter-base information exchange (user data is

only needed in the BSs included in its active set) and efficient distribution of power (power is

saved from poor quality channels). However, this joint processing scheme introduces multi-user

interference in the system, since less CSI is available at the central unit to design the linear

precoding matrix. It should be pointed out that a similar approach has also been proposed in

[PBG+08].

Distributed Joint Processing (DJP): BSs are only aware of their local CSI. Therefore, the

precoding and power allocation are locally implemented at each BS (distributed), but the user

may receive its data from several BSs (joint processing) depending on its given channel

conditions. This approach requires a multi-base scheduling technique to assign users to BSs

under a joint processing assumption.

2.3.1.2 Expected performance or benefits

The aim of this contribution is to further characterize by means of simulations some parameters related to the centralized, partial and distributed joint processing schemes, such as the average sum-rate per cell,

the uniformity over the cluster area of the metric under consideration, the robustness of the scheme, the

total transmitted power in the system and the backhaul and signaling requirements. The motivation

behind this analysis is the need to consider the impact of the mobility of the users. This user mobility

implies that the system configuration cannot be static in time, and that the cluster of BSs may need to

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decide which is the best joint transmission scheme depending on the current users requirements (e.g.,

quality of service or service delay constraints) and the system resources (e.g., available transmit power or

backhaul constraints due to the system load).

Simulations are performed over a cluster of 3 BSs, each one equipped with an array of 3 antennas, and

considering 3 single-antenna users (see Figure A-8). Simulation results, described in Appendix A.4, show

that the differences between the schemes arise in interference-limited scenarios. The centralized joint

processing scheme outperforms the remaining schemes at the cost of higher backhaul and signalling

requirements. On the other hand, the partial joint processing scheme shows a trade-off between the

backhaul and signaling requirements and the achieved average sum-rate per cell, that is, its performance

improves as the coordination degree between BSs or the threshold value increases. The backhaul and signalling requirements of this scheme are evaluated by means of the average number of BSs that are

included in the active set of a user for different degrees of coordination between BSs. Finally, the

distributed joint processing scheme improves its performance as the system becomes interference-limited.

Regarding uniformity and robustness aspects, transmission schemes implying a joint design of the linear

precoding matrix (central and partial joint processing schemes) achieve a higher uniformity of the average

sum-rate per cell over the cluster area, especially in the interference-limited scenarios. Moreover, these schemes also show a higher robustness when computing the evaluation metric, that is, they decrease the

standard deviation of the evaluation metric regardless of the position of the user over the cluster area.

2.3.1.3 Expected requirements on signalling and measurements

Centralized Joint Processing (CJP). Assuming that global CSI is available, this approach

requires a central unit to perform the linear precoding design and the power allocation. This central unit can be an additional network element associated to the cluster of BSs, or one of the

BSs of the cluster can act as a central unit. Each user needs to feedback the estimated CSI related

to all the BSs in the cluster to its primary base station, which can be defined as the one that

provides the highest channel gain. Then, the inter-base station exchange allows to gather in the

central unit the global CSI and the user data, in order to perform the joint processing.

Partial Joint Processing (PJP). In this approach, the user only receives its data from the BSs included in its active set. Therefore, the amount of user data that needs to be exchanged between

BSs and/or the central unit is reduced. In order to define the active set of BSs for a given user,

the user estimates the average gain of the received channels, one from each base station, and

defines its reference link or strongest channel, associated to a given base station. Then, the user

compares the channel gains related to the remaining BSs with the reference link, and includes

these BSs in its active set only if their channel gains are above a relative threshold, with respect

to the strongest channel. By doing so, BSs related to poor quality channels do not transmit to the

user and the cluster becomes partially coordinated. The threshold value is specified by the

cluster, and different degrees or stages of coordination can be obtained by modifying its value.

Distributed Joint Processing (DJP). Each base station only needs local CSI in order to design

the lineal precoding matrix and the power allocation. However, in a first step, global CSI is

required to perform the multibase scheduling mechanism. Depending on the system

requirements, this process can be carried out by a central unit (external or related to one base

station), or can be performed using decentralized approaches [PHG08]. Backhaul overhead is

significantly reduced (both the exchange of user data and CSI are reduced).

2.3.1.4 Expected requirements on architecture and protocols

The centralized joint processing scheme requires a central unit to perform the linear precoding design and

the power allocation. The partial joint processing scheme decreases the amount of data that needs to be

exchanged between the BSs and/or the central unit, but still requires the use of a central unit. Finally, the

distributed joint processing approach requires in a first step to perform a centralized multibase scheduling

mechanism and the exchange between BSs of the interference level experienced by the user.

2.3.2 Joint processing with reduced backhaul requirement by MAC coordination

In this paragraph an approach for maximizing the weighted sum-rate is proposed, in a downlink

transmission with multiple cells, i.e. a joint processing solution. A central unit, based on the scheduler

requirements and on the channel estimates, jointly forms the clusters, selects the users and calculates the

beamforming coefficients and the power allocations. Such an approach can be seen as an extension of

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[PGH08], with the difference that users and clustering are jointly selected in order to maximize the

weighted sum-rate.

In this document a particular embodiment of the proposal using a zero-forcing beamformer is presented,

even if it is worth noting that the proposal applies to other beamformer techniques as well.

The proposed technique allows a significant reduction of signaling in the backhaul due to data sharing

between cooperating base stations, while achieving a high fraction of the full coordination performance.

Duplexing mode TDD/FDD

Clustering mode (1) Dynamic

Clustering mode (2) Network centric

Codebook-based Works with or w\o codebook

Data exchanges: users data Yes

Data exchanges: Channels Impulse

Responses

Yes

Data exchanges : others

Data exchanges rate : slow or fast

Scheduling coefficients, precoding

weights

Fast rate

2.3.2.1 Description

Downlink transmission is considered. Let N be the number of single-antenna base stations in the system,

K the number of users in the network, 1, , K the quality of service weights associated to the different

users. The channel from the n-th base station to the k-th user is modelled as

knkn kn knh d

(2.17)

where kn is the fast fading coefficient, is the path-loss constant, knd is the distance in km between n-

th base-station and the k-th user, is the path-loss exponent and kn models the shadowing term.

Moreover, letnkw indicate the precoding weight used for the k-th user at the n-th base-station.

The N base stations are grouped in subsets of N

LB

clusters, where B represents the maximum

dimension of a given cluster. Coordination is allowed between base stations belonging to the same

cluster, whereas base stations belonging to different clusters are not coordinated. The clusters are disjoint,

i.e. a given base station cannot belong to more than one cluster. The base station clusters are created in a dynamic way, in other words at each time interval1 t the sets of coordinated base stations are generated in

order to maximize a given objective function.

We define , 1, ,lC t l L as the set of base station indexes belonging to the lth cluster at the time

interval t and , 1, ,lU t l L as the set of user indexes scheduled for transmission in a given cluster at

the time interval t. We define 1 , , LC t C t as a base station clustering at the tth time interval.

Assuming that the kth user at the tth time interval is scheduled for transmission in the l th cluster, the

signal received by the kth user can be written as

,ll l

l l

k kn nk k kn nj j

j U t j kn C t n C t

kn nj j k

j U tl l n C t

y t h t w t d t h t w t d t

h t w t d t n t

(2.18)

where the first term is the useful signal, the second term is the interference due to the signals sent to the users in the same cluster than user k, the third term is the interference due to the signals coming from

other clusters, and the last term is the additive white Gaussian noise.

1 In this paragraph the notion of time slot is somehow generic, and it does not refer to any LTE-related definition.

The reason for this choice is that we want to keep this framework as much general as possible, leaving eventually

to the system designer the choice of selecting the best time-frame for a practical implementation of this algorithm.

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Let’s for the moment drop the dependence on the time interval t, and focus on the lth base station cluster.

Let be l a mapping between every element in

lC and a corresponding element in the set 1, , lC , and

let l a mapping between every element in

lU and a corresponding element in the set 1, , lU . The

vector of signals received by the lU users scheduled for transmission in the lth cluster can be written as

'

l l l l l y H Wd n (2.19)

where l l

l knk nh

H ,

l ll nkn k

w

W , l

l kkd

d and '

ln is a term representing the white

Gaussian noise and the interference coming from the other clusters.

TheError! Reference source not found. technique proposed in this paper does not rely on a particular

method for the choice of the precoding matrixlW , and can be applied with both full and partial channel

state information. Nevertheless, as an example of a particular embodiment, in the following lW will be

obtained by using a ZF approach, i.e. such that l l

l l U UH W I , where

l lU UI is the

l lU U identity

matrix. Assuming l lU C the ZF matrix can be chosen to the pseudo-inverse of

lH

1

H H

l l l l

W H H H . (2.20)

Then the received signal at the kth user becomes ' ,k k k ly d n k U , and the rate achievable by the k-th

user belonging to the l-th cluster is 2 2

log 1 k

nk

dP

where 2

kd kP E d

is the power allocated to that

user. The problem of finding the power allocation that maximizes the weighted sum rate under ZF

beamforming subject to per-base station power constraints can be formalized as:

1 1 2 2,

2

, , , , , max log 1

0,

,

k

d lkl nk

k

k

l

d

l L L kP k U

k U

d l

nk d n l

k U

PR U U C C

P k U

w P P n C

(2.21)

where nP is the power constraint at the nth base station of the lth cluster. This is a convex problem which

in general can be solved by using an interior point method. In order to lower the computational

complexity, in this paper a simplified solution obtained by normalizing the water filling power allocation

is considered, obtained under a sum-power constraint 1

lC

n

n

P

between the lC base stations belonging to

the lth cluster, in order to satisfy the per-base power constraints.

A star network topology is assumed, that can represent the case of multiple base stations connected with a

central unit or the case of multiple base stations connected to the network, where one of them acts as

central unit. Scheduling, base station clustering, calculation of the beamforming coefficients and power

allocation are realized in the central unit.

The proposed technique can be summarized as follows:

Phase I. Each base station sends the channel estimates to the central unit.

Phase II. Based on the channel state information and on the scheduling requirements, the central unit

jointly creates the clusters of collaborating base stations, schedules the users in these clusters and calculates the beamforming coefficients and the power allocation.

Phase III. The central unit sends to the base stations beamforming coefficients, power allocation, indexes

of the coordinated cells and indexes of the selected users. At this point, the base stations belonging to the

same cluster need to share the data of the selected users between them.

With respect to full network coordination, the proposed technique allows the reduction of signaling due to

data sharing, while requiring the same amount of signaling due to channel estimates exchanges. Anyway,

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under the assumption of low to average Doppler conditions, the backhaul bandwidth required for sharing

the data between cooperating base stations is much higher than the one required for updating the channel

estimates at the central unit.

Assuming ZF precoding, the problem of jointly clustering, user selection and power allocation in a given

time interval0F0F

2 can be formalized as

1 1

1 1, , , , ,

1

max , , , , ,

, , , 1, ,

, , , 1, ,

L L

L

l L LU U C C

l

k j

k j

R U U C C

U U k j k j L

C C k j k j L

(2.22)

The objective function 1 1

1

, , , , ,L

l L L

l

R U U C C

is a function of both the base station clusters and of

the users scheduled in each cluster. Under the ZF precoding assumption the optimum

1 1, , , , ,L LU U C C must at the same time minimize the inter-cluster interference and select a quasi-

orthogonal set of users to be scheduled in each cluster.

The two constraints are related respectively to the assumption of non-overlapping clusters and to the

assumption that each user cannot be served at the same time by base stations belonging to the same cluster.

The optimal solution requires a brute force search over the sets of users and possible base station clusters.

In the following a sub-optimal approach based on the idea of restricting the search space is proposed. This approach consists in two different stages: an off-line stage, where the different clusterings are generated,

and an on-line stage, where the best clustering is chosen as a function of the scheduler requests and on the

channel state information available at the central node.

Off-line phase. The candidate clusterings are chosen off-line taking into account path loss and shadowing

(or more in general average user distribution and average channel estimates). For example for a system

with 7 cells and B=4, the following three candidate base station clusterings could be chosen based on

average measurements: {1234}, {567} - {2467}, {135}- {2356},{147}.

On-line phase. At each time interval t the central unit estimates the weighted sum-rate achievable for

each cluster. This sum-rate estimation involves the choice of a candidate set of users to be scheduled with

a brute force user selection or with a greedy user selection technique and the calculation of the power

allocation that maximizes the weighted sum-rate. Finally, the clustering that maximizes the weighted

sum-rate and the associated set of users, beamforming coefficients and power allocation are used for transmission in the tth time interval.

2.3.2.2 State of the art

Inter-cell interference in theory can be completely eliminated by full network coordination [HFV06].

Unfortunately, full network coordination is not easy to be implemented in real systems. The main problem to face is the amount of backhaul overhead required for signaling and data transmissions.

Different approaches have been considered in order to limit the coordination to only a subset of the cells

in the system. In [MF07a] and [MF07b] an approach has been considered for uplink and downlink

transmissions such that the users are divided in groups using orthogonal resources. Joint detection can be

used only between users belonging to the same group. Weak users (i.e. users at the edge of the cells) are

grouped together and the base-station coordination is realized starting from the weak users until the constraint on the backhaul is achieved. The grouping is realized considering only average channel state

information, without exploiting instantaneous dynamics of the channel. In [Ven07] a BS selection

algorithm is presented that refers to the uplink problem. The goal is to minimize the power in order to

achieve an equal-rate requirement. Power allocation, receive (linear) beamforming and cluster assignment

are jointly realized using an extension of the algorithm proposed in [RTL98]. The main limitation of this

work is the lack of diversity with respect to changing channel conditions. In [PGH08] a dynamic

clustering technique is considered for uplink transmissions in order to maximize the weighted sum-rate.

At each time interval one user per cell is selected using the round robin scheduling. At that time interval

and for those specific selected users the algorithm chooses the best bases in order to serve those users

using joint combining.

2 As already mentioned before, the dependence on the time-interval t is implicit.

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2.3.2.3 Expected performance or benefits

We refer to Appendix A.5 for a detailed description of the simulation results. We anticipate that the

performance will be measured in terms of CDF of the average per-cell rate, and per-user rate. We will

show that the proposal gives an advantage on both average per cell-rate and on cell-edge rate.

2.3.2.4 Expected requirements on signalling and measurements

The proposed technique allows a significant reduction of signaling in the backhaul due to data sharing

between cooperating base stations. As explained before, this reduction is obtained by dynamically

selecting the coordination region.

It can be applied with different type of beamformers, so the level of uplink feedback is different for the

different beamforming techniques.

2.3.2.5 Expected requirements on architecture and protocols

The proposal needs a logical centralized entity to perform the joint clustering, scheduling and

beamforming. On the other hand this centralized entity can be implemented in a distributed way.

2.3.3 A generalized method for joint design of linear transceivers with CoMP

transmission

A general method for joint design of the linear transceivers with coordinated multi-cell processing subject

to per-BS power constraints is proposed for multiple antenna receivers. Two specific system optimization

objectives are considered. In the first, the minimum weighted SINR per data stream is maximized, which results in SINR balancing at the optimal solution. In the second, the weighted sum rate is maximized.

Both optimization problems are non-convex for multiple antenna receivers and only locally optimal, but

still efficient, solutions can be found.

This work is a continuation to the work in [WIN+D14] and [TPK09a], where a generalized method for

joint design of the linear transceivers was proposed for SINR balancing case with single antenna

receivers. This is now extended to consider multiple antenna receivers as well as another optimization

objective, i.e., maximizing the sum rate.

The proposed generalized method for solving different optimization problems with coordinated BS

processing can accommodate the following special cases:

Coherent multi-cell beamforming with per BS power constraints, which requires a full phase synchronism between all cooperating BSs [TCJ08a].

Coordinated single-cell beamforming case, where all the transceivers are jointly optimized while

considering the other-cell transmissions as inter-cell interference (similar to solution in [BO01]).

Any combination of above two, where the number of jointly transmitting BSs may vary between

users and/or streams.

In the coherent multi-cell beamforming case, each data stream may be transmitted from multiple BSs over

a virtual MIMO channel. This requires a full phase synchronism between all BSs. In addition, a large

amount of data needs to be exchanged on the coordination link, i.e., complete channel knowledge of all

jointly processed BS-user links need to be conveyed to the central controller and user data as well as

precoding weights need to be made available at the cooperating BSs.

In the coordinated single-cell beamforming case, each data stream is transmitted from a single BS. Hence,

a full carrier phase synchronism between transmitting BSs is not required. Furthermore, a lower amount

of data needs to be exchanged on the coordination link. In the coordinated single-cell beamforming case,

a user is typically allocated to a cell with the smallest path loss. Near the cell edge, the optimal user/beam

allocation strategy may also depend on the time varying properties of the channel. Hence, large gains

from fast user/beam allocation algorithms are potentially available for the cell edge users. The optimal BS

assignment per user/beam requires an exhaustive search over all possible combinations of user/beam

allocations. This is clearly computationally prohibitive for a large number of users and BSs. Therefore,

sub-optimal heuristic allocation algorithms are proposed. A detailed description of the proposed beam

allocation algorithms are provided in Appendix A.6.

The presented generalized method requires full channel knowledge between all BS-user links. Thus, the

solution represents an absolute upper bound for the less ideal solutions with incomplete channel

knowledge.

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Since the proposed generalized CoMP transmission algorithm is able to perform any scheme between

joint processing and coordinated beamforming it enables an easy design of adaption algorithms that can

switch between joint processing and coordinated beamforming. Using this kind of adaptation algorithms

the high requirements on pure joint processing can be relaxed. Since the generalized method is designed

for a fixed cluster it is independent from clustering approaches. Hence, it can be efficiently utilized in

different clustering methods, e.g., path loss based clustering.

History Continuation of the work in D1.4,

Section 3.3.2.2

Duplexing mode TDD

Clustering mode (1) Static

Clustering mode (2) Network centric

Codebook-based No

Data exchanges: users data Coherent multi-cell beamforming: Yes

Coordinated beamforming: No

Data exchanges: Channels Impulse

Responses

Yes

Data exchanges : others

Data exchanges rate : slow or fast

Precoding weights

Fast rate

2.3.3.1 Description

Consider a cellular system that consists of BN BSs and K users. Each BS b and each user k are

equipped with TN and k

N R antennas, respectively. A set U with size UK includes all users active

at the given time instant, while a subset UU b includes the users allocated to BS bkb U, . Each user

k can be served by kM BS’s which define the joint processing set kB for the user k , and

B1,...,BB Nk . The signal vector kN

kRCy of user k consists of the desired signal, intra-cell and

inter-cell interference. It is expressed as follows

k

b i

ibkbkb

b ki

ibkbkb

b

kbkbkbk

k bkk

aaa nxHxHxHy B\B U

,,,

B

,,,

B

,,,

where the vector TC,N

kb x is the transmitted signal from the b ’th BS to user k , kn ~ ),0(R0

kNNCN I

represents the additive noise sample vector with noise power density 0N , and TRC,,

NN

kbkbka

H

TRCNN

k

is the channel matrix from BS b to user k with large-scale fading coefficient kba , . The

elements of kb,H are normalized to have unitary variance.

The transmitted vector for user k is generated at BS b as kkbkb dMx ,, , where kmNkb

TC,M is the

pre-coding matrix, T,,1 ,..., kmkk kddd is the vector of normalized complex data symbols, and

k

NMNm kk RT ,min denotes the number of active data streams.

The focus is on linear transmission schemes, where the BN transmitters send S independent streams,

URTB ,min

k kNNNS per transmit dimension. Per data stream processing is considered, where for

each data stream Sss ,...,1, the scheduler unit associates an intended user sk , with the channel matrices

s

NN

kb bsk

sB,C

RT

,

H . Note that more than one stream can be assigned to one user, i.e. the cardinality

of the set of scheduled users, Ssks ,...,1|U is less than or equal to S .

Let TC,N

sb m and skN

sR

Cw be arbitrary transmit and receive beamformers for the stream s. The

SINR of the data stream s can be expressed as

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S

sii b

ibkbskbs

b

sbkbsskb

s

i

ss

s

s

aN

a

,1

2

B

,,H

,

2

20

2

B

,,H

,

mHww

mHw

(2.23)

The general system optimization objective is to maximize a function Sf ,...,1 that depends on the

individual SINR values for each stream s . This can be formulated as

Ss

NbP

s

aN

a

f

s

b

s

sb

sS

si b

ibkbskbs

b

sbkbskb

S

b

i

ss

s

ss

,...,1,1

,...,1

s.t.

,...,max

2

B,

S

2

2,

,2

1,1

,,H

,

2

20

2

,,H

,

1

w

m

mHww

mHw

(2.24)

where the variables are TC,N

sb m and skN

sR

Cw , Ss ,...,1 and bS includes all streams allocated to

BS b , i.e., bsb ks U|S . sb,m can be further split into sbsbsb p ,,, vm , where TC,N

sb v and

sbp , are the normalized transmit beamformer and the allocated power for the stream s . The weights

Sss ,...,1,0 , are used to prioritise the data streams of different users differently.

Two different system optimization objectives are considered.

Minimum weighted SINR maximization, ssSsSf 1,...,11 min,...,max

[TPK09a].

Weighted sum rate maximization,

S

s

ss

S

ssS

sf

1

21

1 1log1log,...,max

[TCJ08a].

Solutions for these optimization problems are presented in detail in Appendix A.6.

Notice that another approach for the problem of maximizing the minimum SINR per user/stream, based

on the uplink-downlink duality theorem, was considered for coordinated beamforming case in section

2.2.1. In contrast to that, the generalized approach in this section, which can accommodate any scenario

between joint processing and coordinated beamforming, utilize the conic optimization [WES06] on the

beamformer design. The non-convex problem is divided into convex sub-problems that can be optimally

solved by using standard convex optimization tools. However, the global optimality cannot be guaranteed due to the non-convexity of the original problem.

2.3.3.2 State of the art

A generalised method for joint design of linear transceivers for SINR balancing case subject to per-BS

power constraints with single antenna receivers was presented and analysed in [WIN+D14] and

[TPK09a]. The method can accommodate any scheme between coherent multi-cell beamforming and

coordinated single-cell beamforming. It is assumed that full CSI between all terminals and BSs is

communicated from the BSs to the central controller or master BS so that the precoders are computed in a centralized manner. Furthermore, the precoders need then to be communicated back to each BS involved.

2.3.3.3 Expected performance or benefits

Performance of the proposed concept is evaluated in detail in Appendix A.6. Some of the results from

Appendix A.6 are presented herein. Table 2-4 presents the ergodic sum rate of users at the cell edge for

1,2,2,2,,, RTB k

NNNK and 2,2,2,2 systems with sum rate maximization algorithm at 20 dB

single link signal to noise ratio (SNR). The following transmission schemes are compared:

1. Coherent multi-cell beamforming. 2. Coordinated beamforming with optimal beam allocation (exhaustive search)

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3. Non-coordinated beamforming with optimal beam allocation (exhaustive search). The inter-cell

interference is neglected in the precoder design.

Table 2-4: Main performance results of generalized CoMP transmission method

Transmission scheme Ergodic sum rate

[bits/s/Hz]: 1R k

N

Ergodic sum rate

[bits/s/Hz]: 2R k

N

Coherent multi-cell beamforming

15.95 23.38

Coordinated beamforming

(optimal beam allocation)

10.36 17.15

Non-coordinated beamforming

(optimal beam allocation)

5.47 8.17

It can be seen that coherent multi-cell beamforming greatly outperforms all the non-coherent cases. The

difference between coherent beamforming and coordinated beamforming in terms of ergodic sum rate is

about 6 bits/s/Hz at the cell edge for both configurations of the receiver antennas, 1R k

N and 2R k

N .

This is due to the fact that the coherent multi-cell beamforming can fully eliminate the inter-cell

interference. Obviously, there is a trade-off between performance and complexity since using coordinated

beamforming the amount of data to be exchanged on the coordination link is reduced. Furthermore, a full

carrier phase synchronism between BSs is not required. It is also shown in Table 2-4 that the performance

of the coordinated beamforming is significantly better, i.e., almost 5 and 9 bits/s/Hz, than that of non-

coordinated beamforming case for 1R k

N and 2R k

N , respectively. A more detailed performance

evaluation with wide range of numerical examples is provided in Appendix A.6.

2.3.3.4 Expected requirements on signalling and measurements

The coherent multi-cell beamforming sets high requirements for signalling and measurements. A large

amount of data needs to be exchanged on the coordination links, i.e., complete CSI of all jointly

processed BS-user links to the central entity, and user data as well as precoding weights from the central controller to the cooperating BSs. Furthermore, tight phase synchronization across the cooperating BSs

and centralized controllers is required. Therefore, high speed links, i.e. optical fibres or dedicated radio

links, are required between cooperating BSs and the central controller.

Coordinated beamforming and beam allocation has looser requirements on coordination and backhaul,

i.e., tight frequency synchronization and sharing of user data between each BS are not required. However,

full channel knowledge of all jointly processed links is still needed for the ideal interference avoidance.

Hence, a centralised resource management mechanism, i.e., central controller or master BS, is still

needed.

2.3.3.5 Expected requirements on architecture and protocols

The proposed method requires a centralized resource management mechanism. Furthermore, the required

information exchange rate between the cooperating BSs and the central controller is high.

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3. Investigations on relaying in the framework of CoMP

The introduction of Relay Nodes which are controlled by the network allows to use them as part of a

CoMP system. The RNs can be used to extend the actual coverage or to densify the actual network to

enhance the user throughput at the cell edge. Relay nodes are connected to a base station via over-the-air

in-band links (e.g. specific control channels or in-band backhaul, depending on the relay type), enabling a

tight coordination but at the price of a possible delay between the coordinated nodes. Hence, coordinated relaying schemes are a particular way to implement CoMP, which has to account for the specificities of

relays regarding the coordinated processing: the delay inherent to the 2-hops transmission between a base

station and a user terminal (UT), and/or the potential errors affecting the first hop.

In year 1, different aspects of coordinated relaying schemes were investigated, e.g. coding schemes and

schedulers taking relaying into account. The conclusion was that it is worthwhile to further investigate

relaying in the framework of CoMP. Hence, in this section various cooperative relaying schemes considered for year 2 are proposed. The first innovation considers a cooperative scheme, which combines

cooperative base station transmission and relaying. The second innovation considers distributed LDPC

coding for a Decode and Forward type relay..

History Continuation of the work in D1.4,

Section 3.3.2.2

Duplexing mode TDD

UL/DL UL or DL

3.1 Impact of interference on design and performance of relaying protocols

Although relaying is a promising concept it still needs to be clarified to which extent the increased

interference level reduces the actual benefits of relaying. In this section, we extend the interference channel by additional relay nodes in order to investigate the influence of interference on the design and

performance of relaying protocols.

Figure 3-1: Relay-assisted interference channel with two communication pairs and one relay node

supporting each pair.

This section presents system level results for a protocol, which combines the advantages of coordinated

base station transmission and relaying. In order to discuss this protocol, we introduce the relay-

interference channel as introduced in [RFL09] with two communication pairs (with source nodes s1 and

s2, and destination nodes d1 and d2), each supported by one relay node (r1 (resp. r2) supports the

communication between s1 (resp. s2) and d1 (resp. d2)). Each relay node is underlying the half-duplex

constraint, which implies that it is either transmitting or receiving on a particular time-frequency resource. It further considers backhaul links C{1,2} and C{2,1} between source nodes, which are used to coordinate

their transmissions. Figure 3-1 illustrates the channel. In order to explain the employed schemes, we

introduce the interference and broadcast channel in the following.

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3.1.1 The Interference Channel

Figure 3-2 The standard Gaussian interference channel (f1 and f2 denote the individual encoders

and g1 and g2 denote the individual decoders). Message W1 and W2 are independently encoded and

each terminal transmits the message X1 and X2, respectively, At both destination the messages W1

and W2 are decoded using the channel outputs Y1 and Y2, respectively.

The general interference channel consists of N source nodes and M destination nodes. However, for the

sake of readability we focus in the following on the case N = M = 2, where two transmitters and two

receivers are considered. Consider Figure 3-2, which shows the standard form of the Gaussian

interference channel (IC) (note that any interference channel with N = M = 2 can be modeled by the

standard form).

So far the best known inner bound of the (general) interference channel has been derived by Han and Kobayashi in [HK81], where the first transmitter divides its message in two parts W(1,1) and W(1,D)

(similarly the second transmitter uses W(2,2) and W(2,D)). While the first part W(1,1) is only decoded by the

first receiver, W(1,D) is decoded by both receivers and called common message. The purpose of a common

message is to reduce the interference for the private message. Another basic element is the joint decoding

of both common messages and the own private message. Even though the rate region of Han-Kobayashi

(HK) is the best inner bound known so far, it is computationally complex to determine. The complete HK

region is the convex hull over all possible power assignments, which is difficult to determine in real-time

for each fading situation. Since complexity is a driving factor we are interested in less complex methods,

which still provide sufficient performance figures.

One such method has been presented by Etkin et al. in [ETW06], [ETW08], which has been proven to be

within 1 bit per channel use (bpcu) of channel capacity while being much simpler than the full HK coding approach. Assume the channel at both terminals is given by

Their basic idea is to introduce a decoding order and align the interference caused by each transmitter

with the noise power at the non-intended receiver, i. e., both terminals choose the private message power

such that

holds. Compared to the full HK approach, each terminal at first decodes both common messages jointly

and then each one decodes its own private message.

3.1.2 The Broadcast Channel

We further detail the discussion with the Gaussian broadcast channel (BC). The BC models the case

where one source communicates with multiple destinations. Each decoder at a particular destination

works independently of all other decoders such that no joint processing is possible. Costa introduced in

[Cos85] the idea of Dirty Paper Coding (DPC), which is the capacity achieving approach and is able to

provide the same capacity in case of interference known at the transmitter as if there were no interference.

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Figure 3-3 The multiple antenna Gaussian broadcast channel with only private messages and

perfect channel knowledge at the encoder

Figure 3-3 shows the two-antenna Gaussian BC. First, the two private messages W1 and W2 are jointly

encoded giving their corresponding transmit vectors X1 and X2, respectively, as well as the channel input

vector X = X1+X2. Obviously both antenna transmissions are interfering with each other. But knowing the channel matrix at the encoder, we can subsequently apply DPC on each user data stream in order to

alleviate the interference of all previously encoded user streams. However, this approach requires

significant computational efforts and channel knowledge at the transmitter. The Gaussian IC, introduced

in the previous section, considers two transmitters, which do not have common CSI and data. Consider

now the Gaussian BC with per-antenna power constraint, which models a Gaussian IC where transmitters

are connected and able to jointly encode their channel input. Compared to the Gaussian BC, where a

duality with the Gaussian multiple access channel (MAC) can be exploited, the Gaussian BC with per-

antenna power constraint is more difficult to handle. In this section, we use the suboptimal approach

Zero-Forcing DPC (ZF-DPC) [CS03, KF+06] which uses the LQ-decomposition of the channel matrix

and has deterministic, polynomial complexity. More specifically, the channel matrix is expressed as

H=LQ, where L is lower triangular matrix and Q is a unitary matrix. By precoding with the Hermitian of Q, the channel is transformed is lower triangular form. Similar to SIC using VBLAST after QR post-

processing, DPC is used to pre-cancel the interference at the transmitter. This results in a set of

interference-free single-user links. Furthermore, the ZF-DPC approach satisfies per-antenna power

constraints.

3.1.3 The Relay-Assisted Interference Channel

One way to examine the relay-interference channel as illustrated in Figure 3-1 is to treat it as a cascade of

two BCs or ICs subsystems, which interfere with each other, i.e., we still consider the signal from the

source nodes received by both destinations. The first channel is from sources to relays (S → R) and might

exploit backhaul links between both sources in order to coordinate their transmissions. Depending on the

respective coding schemes in this first channel, the used encoding scheme for the second channel from

relays to destinations (R → D) must be selected. In case one of both channels is considered to be a BC,

the transmitting nodes exploit common information to coordinate their transmissions. When treating one of both stages as interference channel, the transmitting nodes do not exploit common information but

might employ interference mitigation techniques such as Han-Kobayashi coding [HK81] or time-division

duplex. We showed in [RFL09] that the best choice is to use DPC on the link between base stations and

relay nodes, and to use HK coding on the link between relay nodes and user terminals. Furthermore, it

turned out to be the best choice to use an adaptive approach, where users close to the base station are

served directly by base stations, and all other users are served using relay nodes. In the following, we

apply this idea to the WINNER system model defined by deliverables [WIN2D6137] and [WIN2D6112]

(and channel models defined in [WIN2D111] and [WIN2D112]) and evaluate the protocol’s performance

in the wide-area and Manhattan-area scenario.

3.1.4 Considered Protocols and Constraints

This section’s analysis considers four protocols, which are detailed in the following.

Conventional, direct transmission

The reference protocol of our analysis is a conventional direct transmission protocol, which divides the

overall usable bandwidth into an edge band and center band. Edge band resources are used to serve users

at the cell edge with high inter-cell interference. Furthermore, this protocol reserves a center band, which

is used by all cells to serve users with low inter-cell interference. The assignment of users to edge and

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center band uses the expected long-term SINR, which is assumed to be perfectly known. Furthermore,

each BS is equipped with four antennas and therefore able to serve up to four users on the same time-

frequency-resource using ZF-DPC.

Multi-Cell MIMO Transmission

In order to improve the spectrum reuse, we additionally consider a multi-cell MIMO transmission

protocol where, in our case, three base stations are cooperatively transmitting (in this case the setup in

Figure 3-1 has to be appropriately modified). In order to coordinate their transmission all base stations are

connected by an unlimited backhaul, which provides the means to perfectly exchange CSI and the

individual messages at the base stations. In this particular scenario, only multi-cell MIMO is used and no

relays are used. This protocol is one way to implement the CoMP cooperation described in further detail

in the previous section.

As discussed in the previous part, we use ZF-DPC in order to guarantee a deterministic complexity. The

assignment of user terminals to the individual base stations is done based on the path loss between base

station and user terminal. Throughout the following analysis we assume that each base station has perfect

CSI of the compound channel matrix. Since each BS is equipped with four antennas, three BS can serve

up to 12 UEs on the same resource using ZF-DPC.

Relay-Only

We consider in our analysis a protocol where all users are served using relay nodes and Etkin-Tse-Wang

(ETW) coding [ETW06], [ETW08] based on path loss information. In order to ensure efficient

interference mitigation each user is assigned to the relay node with the smallest path loss towards this

user. Then the relay node with the second-smallest path loss towards this user is chosen and selected for

the ETW coding. Hence, we always group two users and two relay nodes such that they cooperate using

ETW coding. The expected bottleneck in such a system is the in-band feederlink between base station and

relay node. In order to improve the performance of the in-band feederlink, we consider that at most three

base stations form one cluster and cooperatively transmit to their assigned relay nodes. Compared to the

previously described multi-cell MIMO protocol the signaling overhead is reduced (only the power levels of the individual messages must be exchanged), because relay nodes do not move and have line-of-sight

(LOS) towards the base station, which increases the coherence time/frequency. Furthermore, the user

grouping is significantly simplified as only a small number of fixed relay nodes is considered.

Mixed Protocol using Relays and Multi-Cell MIMO Transmission

In this setup users are served either using the multi-cell MIMO protocol or using relay nodes and ETW coding. Based on the path loss to the individual nodes (a penalty for the lower transmission power of

relay nodes is included), each user is assigned to either a base station or a relay node. In this mixed

protocol either base stations or relay nodes transmit within a certain part of the spectrum, but not both.

This part does not consider the case where two relays have common message information and exchanged

CSI, i.e., we do not use the backhaul link between both relay nodes. In case we treat S → R as IC and

employ HK coding, the commonly decoded information can be used for a distributed DPC and in case we

treat S → R as BC, the S → R stage degrades to a multicast channel because both relays must receive the

same amount of information (CSI and data messages). Nevertheless, the necessity to exchange the

transmitted messages and CSI as well as to synchronize their transmissions will require a high-capacity

feeder link between sources and relay nodes, which is usually the bottleneck in a wireless communication

system with relays.

3.1.5 Numerical Results

This section compares the performance of the previously introduced protocols using

the 5%-ile throughput θ5% defined by the throughput achieved by at least 95% of all users, and

the median throughput θ defined by the throughput achieved by at least 50% of all users

In this section, we analyze the urban wide-area and Manhattan-area scenario. In the former we consider

one central site, which is surrounded by two tiers of overall 18 interfering sites. In the case of Manhattan-

area, we use overall 44 cells but only considering the results for the three inner cells. Furthermore, the

number of users per cell may vary as we keep the number of overall users constant and ensure that in the

case of wide-area on average 90 Users per km2 are randomly placed according to a uniform distribution

and 250 Users per km2 in the case of Manhattan-area. (in both scenarios this equals to about 25 users per cell on average).

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The overall area is partitioned into rectangles of equal size. In case of the wide-area scenario they are of

size 30m × 30m, and in case of the Manhattan-area scenario they are of size 10m×10m. For each user the

corresponding spatial block (x, y) is determined and the average throughput θ(x, y) = Et {θ(x, y, t)}

over all users, which have been placed in this block, is used to determine the number of resources. Using

a fair scheduler the number of resources assigned to a user is proportional to 1/θ (x, y). The fact that we

use the average throughput reflects that we try to achieve a good tradeoff of high system throughput and

fairness. In order to achieve an improved fairness within the system, it would be more suitable to use the

5% quantile. In this way, a user with a high throughput variance gets even more resources in order to

improve the 5% quantile of throughput at the expense of other users’ performance.

Our analysis does not consider any user mobility. Furthermore, the Manhattan-area scenario considers

users placed both on streets and within buildings. Such a uniform distribution might not be realistic, but

allows for insights in the performance improvements through relaying.

Figure 3-4 shows the CDF of the user throughput, i.e., the probability that an arbitrary user achieves a

certain throughput. The conventional protocol with direct transmission achieves only a 5%-ile throughput

of about 0.1Mbit/s and a median throughput of about 1Mbit/s. By contrast, multi-cell MIMO transmission is able to significantly improve the performance: θ5% = 0.6Mbit/s and θ = 3.5Mbit/s, which not only

implies an improved system throughput but also that users with low data rates benefit from these

improvements. The deployment of two additional relay nodes and application of the relaying-only

protocol improves the median throughput θ = 3Mbit/s) but reduces the performance of the worst users θ5%

= 45 kBit/s), which results in a step of the CDF. Relays only improve the channel conditions for those

users at the cell edge and users close to the relay node. If only relay nodes are used and no direct BS-UT

transmission, the worst users are placed closely to the base station. Contrary to the relaying-only protocol,

the mixed protocol is able to improve both median throughput θ = 4.8Mbit/s) and worst-user performance

θ5% = 0.8Mbit/s). This time users close to a base station are served using multi-cell MIMO transmission

and users at the cell edge are assigned to relays. Surprisingly, if we deploy eight relay nodes and use the

relaying-only protocol, we do not perform much better than with two relay nodes and using the mixed approach where θ = 8Mbit/s and θ5% = 1.1Mbit/s). There are two reasons for this: in-band feederlink and

the requirement that you can only transmit as much as in the relay buffer (a very high-data rate feederlink

might be able to support higher data rates than the buffer).

.

Figure 3-4 Throughput for the wide-area scenario using in-band relays

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Figure 3-5 Throughput for the Manhattan-scenario and in-band feederlinks

Figure 3-5 shows the results for the Manhattan-area scenario. We can again see that multi-cell MIMO

improves the performance compared to a conventional system. The 5%-tile throughput of multi-cell

MIMO is θ5% = 5 kBit/s and the median throughput is θ = 300 kBit/s. Compared to the previous scenario

the difference between worst and median throughput performance is much higher, which implies that a

major part of the users has very bad channel conditions towards the base stations, primarily those located

indoor. The same effect can be seen if only two relay nodes are deployed outdoor (θ5% = 3 kBit/s and θ =

500 kBit/s for the mixed protocol). Only four relay nodes (two outdoor and two indoor relays) are able to

improve performance figures for both worst user and median user performance (θ5% = 0.3Mbit/s and θ =

1.8Mbit/s). We can further see that in case no direct transmission is used the performance is the same as

for the mixed protocol, which implies that only a few users benefit from multi-cell MIMO transmission.

For practical deployments this implies that it suffices to only use relay nodes in micro-cellular

deployments, which reduces complexity and power consumption.

Figure 3-6 Throughput over area. Arrows indicate the main lobe direction and triangles indicate

the relay nodes.

Among others, relaying helps to provide a more uniform power distribution, which is reflected by a more

uniform throughput distribution as shown in Figure 3-6. Of course, the throughput around the base station

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and relay nodes is higher. While the throughput around the base station quickly declines due to the

decreasing LOS probability, the slope around each relay node is much lower. Furthermore, the peak

throughput around the base station is higher than around the relay nodes due to the higher transmission

power. The lowest throughput is achieved at the cell edge and between relay nodes in one cell. In both

areas the reasons are the high path loss and the high inter/intra-cell interference.

The mixed protocol supports multi-cell MIMO transmission as well as relaying, and the node assignment

is done based on the path loss. From Figure 3-6 we can see that the probability, that a user terminal is

assigned to a relay node, increases towards the relay nodes, and the area where users are assigned to a

relay node (with high probability) is much larger than the area where user terminals are assigned to a base

station. Cell edge users are primarily served by relay nodes, which implies that the improved path loss conditions outweigh the lower transmission power of relay nodes.

Figure 3-7 Throughput-CDF and comparison of correct (fast-fading based) ETW coding (dashed

lines) and path loss-based ETW

Figure 3-7 compares the performance of

ETW coding based on perfect fast-fading-CSI at the relay node,

a binary ETW coding with possible two power assignments (either only private or common

messages), and

TDMA between relay nodes and user terminals. We can conclude from this figure that there are only minor performance improvements if fast-fading CSI

is used. The usage of path loss information significantly reduces the necessary signaling overhead and

makes ETW coding feasible. However, the results also reveal that a binary ETW coding, where either

only private or common messages are used, is able to achieve almost the same performance. This

suggests that it is sufficient to quantize the power assignment level with 1 bit. TDMA between relay

nodes and user terminals does not achieve the performance of ETW coding. Compared to TDMA, ETW

coding needs no central scheduler, which coordinates the assignment of resources to each relay node

based on the global path loss information. The power assignment for ETW coding can be done at each

relay node based on the path loss between the relay and the assigned user terminals.

3.2 Distributed LDPC coding for the single relay channel

After the authors in [SEA03a], [SEA03b] introduced the concept of cooperative diversity, many papers

proposed cooperation protocols for relay channels, that can be classified into two major categories,

namely amplify-and-forward (AF) and decode-and-forward (DF).

In the AF protocol the relays scale the signal received from the source and forward it to the destination

without other treatment. Thus, such a cooperation scheme employs a unique error correcting code, coding being only performed by the source, and decoding only by the destination.

In DF protocols the relay decodes the signal and, assuming successful decoding, it recovers the codeword

(information and parity bits) sent by the source. Two main scenarios are then possible:

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[DF-1] The recovered codeword, or part of it (for instance, only the information bits) is forwarded to the

destination. Thus, the forwarded information is (part of) the information originally transmitted by the source, and the destination will benefit from the channel diversity, which improves the

signal to noise ratio on (part of) the received transmission.

[DF-2] The relay computes a new sequence of parity bits (different from the sequence of parity bits in

the recovered codeword), which is forwarded to the destination. In this case, the forwarded

information is not contained in the one originally transmitted by the source. However, the destination will benefit from the coding diversity, consisting on the knowledge of the extra

information received from the relay. We refer to this scenario as distributed coding or coded

cooperation.

The above scenarios are illustrated here below (I = information bits, P = parity bits):

Figure 3-8. Examples of cooperation scenarios under the DF protocol

The most known example of distributed coding is the one of a distributed Turbo-code (TC): the source

and the relay encode the signal using a convolutional code (the relay also has to interleave the received

signal), allowing the destination to decode the received signal using a parallel-concatenated TC [ZV03].

History New

Duplexing mode FDD or TDD

UL/DL UL/DL

3.2.1 State of the Art

LDPC codes for the single relay channel have been mostly investigated under the first DF scenario

described above. The second DF scenario (distributed coding) is, from the coding theory point of view,

closely related to incremental redundancy. Some approaches have been already proposed in the literature,

which are mainly based either on serial or parallel code concatenation ([KAA04], [CBS+07], [HD07],

[RY07]).

Serial concatenation

Consider two LDPC codes C1 and C2 with parameters (K1, N1) and (K2, N2), where Ki denotes the code

dimension and Ni denotes the code length. Assume that N1 = K2, and that the two codes are systematic

(the last condition is only assumed for simplicity). Then, these codes can be used within a distributed

coding scenario as follows:

The source encodes the sequence of information bits I, of length K1, using the code C1. Since C1

is systematic, the encoding operation generates a sequence of parity bits P1, of length N1-K1, and

the codeword (I, P1) is broadcasted to the relay and the destination.

The relay decodes the received signal and recovers the codeword (I, P1), which is then encoded

using the code C2. Since C2 is systematic, the encoding operation generates a sequence of parity

bits P2, of length N2-K2, which is forwarded to the destination.

R

S D (I, P)

(I, P) I

R

S D (I, P1)

(I, P1) P2

(a) scenario DF-1 (b) scenario DF-2

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Thus, the destination receives 21

~,

~,

~PPI , representing noisy versions of the transmitted sequences (I, P1,

P2). If H1 and H2 denote the parity check matrices of the codes C1 and C2, the noisy received signal

21

~,

~,

~PPI can be decoded at the destination, by using the matrix H obtained by the concatenation of H1

and H2, as shown at Figure 3-8 (a).

Parallel concatenation

Consider two LDPC codes C1 and C2 with parameters (K1, N1) and (K2, N2), where Ki denotes the code

dimension and Ni denotes the code length. Assume that K1 = K2 = K and that the two codes are systematic

(the last condition is only assumed for simplicity). Then, these codes can be used within a distributed

coding scenario as follows:

The source encodes the sequence of information bits I, of length K, using the code C1. Since C1

is systematic, the encoding operation generates a sequence of parity bits P1, of length N1-K, and

the codeword (I, P1) is broadcasted to the relay and the destination.

The relay decodes the received signal and recovers the codeword (I, P1). It re-encodes the

information sequence I using the code C2. Since C2 is systematic, the encoding operation

generates a sequence of parity bits P2, of length N2-K, which is forwarded to the destination.

Thus, the destination receives 21

~,

~,

~PPI , representing noisy versions of the transmitted sequences (I, P1,

P2). Let H1 and H2 be the parity check matrices of the codes C1 and C2. Then the received signal

21

~,

~,

~PPI can be decoded at the destination, by using the matrix H obtained by the concatenation of H1

and H2, as shown at Figure 3-8-(b). Alternatively, the destination can attempt to decode H1 and H2

separately, exchanging periodically extrinsic information concerning information bits between the two

decoders.

Figure 3-9. Code concatenation

Obviously, many other scenarios may be considered for the above constructions: for instance, the relay can forward (I, P2) instead of P2 only, which improves the signal to noise ratio on information bits, and

also provides the extra information P2 to the destination.

It should also be noted that we consider the problem of designing LDPC codes that can be advantageously

used for coded cooperation, but our goal is not to discuss cooperation protocols (Orthogonal or Non-

Orthogonal DF, etc.) or multiplexing schemes of bit streams from source and relay (time or frequency

division multiplexing ).

From the code design point of view, the serial or parallel concatenation of two LDPC codes presents several weaknesses. It is well known that the correction capacity of LDPC codes is strongly related to the

code irregularity, which describes the distribution of the non-zero entries in the parity-check matrix.

Moreover, the density of the non-zero entries in the parity check matrix plays also a very important role

by itself, and codes with lower rates must be sparser than those with higher rates. However, this basic property is violated by the code concatenation construction, since matrix H (with lower rate) contains

both matrices H1 and H2 (with higher rates). Another drawback of the concatenation construction is that

the matrix H inherits all the cycles of H1 and H2, which can be normally avoided for codes with lower

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rates. As a consequence, the performance of concatenated codes is degraded with respect to “properly

designed” (unconcatenated) LDPC codes with the same rate.

Stated in a slightly different manner, code concatenation does not allow the joint design of matrices H1

and H, where H is the (serial or parallel) concatenation of H1 by some other matrix H2, such that the

corresponding codes perform close to their channel capacities. On the other hand, the success of the DF cooperation scheme depends on the performance of both H1 and H, since that successful decoding is

required both at the relay and the destination.

3.2.2 Cooperative coding using extended LDPC codes

The purpose is to address the problem of designing capacity approaching LDPC codes for coded

cooperation. We say that the code C, of length N, is an extension of the code C1, of length N1 N, if any

codeword 1

,,1 Nxx of C1 can be extended to a codeword NN xxx ,,,,11 of C. Obviously,

(serial or parallel) code concatenation is a particular case of the above definition. Extended codes can be

used within a distributed coding scenario, for instance as follows:

The source encodes the sequence of information bits using the code C1. The encoded codeword

1

,,11 NxxX is then broadcasted to the relay and the destination.

The relay decodes the received signal and recovers the codeword 1

,,11 NxxX , which is

then extended to a codeword NN xxxX ,,,,11 of C. Then, the extra information

contained in X , that is NN xxXX ,,\ 11 1 , is forwarded to the destination.

A construction method of extended codes has been elaborated, which avoids code concatenation, and allows more degrees of freedom to optimize the joint design of H1 and H. The proposed method also

enables a higher decorrelation between the irregularities (and densities) of matrices H1 and H, making

possible the design of capacity approaching codes by density evolution methods.

3.2.3 Expected benefits

To illustrate the expected benefits, Figure 3-10 presents the asymptotic performance derived by density

evolution methods using Gaussian approximation. The additive white Gaussian noise (AWGN) relay

channel is considered, with fixed source-to-destination SNR (either -1dB – dotted curves, or -0.5dB –

solid curves); the relay successfully decodes the received signal. The SNR of the relay-to-destination link

is shown on the horizontal x-axis. Black, red and green curves correspond to the distributed coding scenario (scenario DF-2, Figure 3-2-(b)) as follows: black curves matrices (H1, H) are obtained by the

proposed design, red curves matrices (H1, H) are obtained by parallel concatenation, and green curves

matrices (H1, H) are obtained by serial concatenation. Finally, the blue curves represent the performance

of the scenario DF-1 (Figure 3-2-(a)). It should be noted that the performance shown does not correspond

to optimized codes; regular codes in all the above scenarios are used.

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Figure 3-10 Comparison of the asymptotic performance of several DF schemes

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4. Conclusion

Considering that future cellular wireless systems will need to provide high data rate services for a large

number of users without the use of an increased signal bandwidth, a possible strategy to reduce the

performance limiting inter-cell interference is identified in Coordinated MultiPoint systems (CoMP).

CoMP refers to a system where the transmission and/or reception at multiple, geographically separated antenna sites is dynamically coordinated in order to improve system performance. CoMP is already being evaluated as one of the most promising techniques for increasing capacity of new radio access systems in

several standard organisations. In the framework of 3GPP, CoMP is identified as a key concept in the

LTE-Advanced study item [3GPP36.814]. CoMP approaches are also considered in the WiMAX

standardisation framework, see e.g. [MHM+08] for a classification of the related contributions.

In year 1, WINNER+ studied different aspects of CoMP extensively. These included system

architectures, different approaches and algorithms for performing the coordination, and the requirements

in terms of measurements, signalling, backhauling constraints, etc. these put on the system. The work is

reported in [WIN+D14]. Much attention was put on so-called joint processing/transmission schemes, in

which the transmission to a particular UE originates from multiple transmission points. It was found that

these schemes have potential to provide significant performance gains, however at the price of high

requirements on the backhaul links in terms of latency and capacity since user data, CSI, and precoding

weights need to be shared among the transmission points. Furthermore, different aspects of coordinated

relaying schemes were investigated, e.g. coding schemes and schedulers taking relaying into account. The

conclusion was that it is worthwhile investigating relaying in the framework of CoMP further.

Considering these conclusions from year 1, the focus in year 2 was set on CoMP solutions that have

relaxed requirements in terms of backhauling and complexity compared to the full blown joint processing

schemes that were studied during year 1. In line with 3GPP, these schemes can be split into two

categories “Coordinated Beamforming”, and “Joint Processing”. Both categories have been considered in

this second year. For coordinated beamforming schemes, the transmission to a particular UE takes place

from a single transmission point, but scheduling and beamforming weights are coordinated in order to

reduce interference. In the joint processing case, which is more demanding in terms of backhauling since

users data are present at each cooperating BS, focus was put on relaxed requirements schemes. In both cases, clustering (i.e. determining which BSs will cooperate) showed to be key to algorithmic

performance.

Simulations scenarios were selected in order to allow comparisons between various proposed schemes. Thus respective benefits of Joint Processing and Coordinated Beamforming with reference to a baseline

non CoMP scenario can be evaluated. Attention was given not only to average throughput gains, but

specifically to gains for cell edge users, whose quality improvement is one of the main motivations of

CoMP techniques. To complete this comparative study, future research should include the impact of

imperfect channel estimation on algorithmic performance.

The work reported in this deliverable about CoMP tries to pave the way to a real feasibility of CoMP

schemes, both introducing and extensively studying coordinated beamforming schemes and aiming to

ease the effective implementation of joint processing techniques. It’s worth noting and well known that if

low-latency exchanges are needed between cooperating BSs, then the corresponding CoMP schemes will

not be able to be deployed everywhere but only in the areas where a suitable backhaul is present, or they

will require important investments from the operator to upgrade the backhaul. This could somehow limit the practical usability of the techniques. For each technique referred in this document, requirements

concerning amount of data exchange and data exchange rate were highlighted qualitatively (see

introductory table for each section). Upper layers algorithm and proper scheduling management schemes

are important features as well in this framework; they are extensively analyzed in [WIN+D15].

When it comes to relaying in the framework of CoMP, various cooperative relaying schemes were

considered. The first innovation considers a relay-assisted interference channel with two communication

pairs and one relay node for each pair. Various cooperative and non-cooperative schemes are compared.

The second innovation considers distributed LDPC coding for a Decode and Forward type relay.

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In both areas studied in this deliverable much work is still ongoing in close cooperation with the

standardization consortia and considering the main outcomes that will come from them. Evaluations of

the proposed schemes and further investigations are foreseen, relaxing ideal assumptions and aiming to

the definition of as much widely accepted as possible coordinated systems.

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[YSL+08] C. I. Yeh, Y. S. Song, S. J. Lee, B.-J. Kwak, J. Kim, and D. S. Kwon, "Frame Structure

to Support Inter-cell Interference Mitigation for Downlink Traffic Channel using Co-

MIMO and FFR", Contribution to IEEE 802.16m, IEEE C802.16m-08/017r1 (ETRI),

Jan. 2008.

[ZD04] H. Zhang and H. Dai, “Cochannel interference mitigation and cooperative processing in downlink multicell multiuser MIMO networks”, EURASIP Journal on Wireless

Communications and Networking, 2004.

[ZV03] B. Zhao and M.C. Valenti , “Cooperative diversity using distributed turbo codes”, Virginia Tech Symposium on Wireless Personal Communications, Blacksburg, VA,

June 2003

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A. Appendix

A.1 System level performance evaluation of coordinated beamforming and

joint processing

In Section 2.2.1 a centralized non-codebook based scheme was described as an example of a coordinated

beamforming concept. In this appendix we will provide a detailed system level performance evaluation of

this scheme, and also compare it to the performance of a joint processing scheme based on zero-forcing

(ZF) precoding that was presented and initially evaluated in [WIN+D14]. The goal is to give some hints

on the achievable performance with different CoMP approaches, and some indications on in which

scenarios the approaches are most suitable. One interesting question is whether the increased system

complexity in terms of feedback overhead and backhaul requirements posed by joint processing pays off

in terms of system performance.

A.1.1 Simulation setup

The simulations were performed with a radio network simulator with a regular cell plan and hexagonal

cell layout. Three different macro scenarios are considered; the first two of them are the ITU scenarios

Urban Macro and Rural Macro as defined by ITU-R in [ITURM2135]. Then, results are also given for the

so-called 3GPP Case 1, which is an urban macro scenario defined in [3GPP25814]. For full details of the scenarios, the interested reader is referred to the documents cited above, whereas a condensed summary

of the used models and assumptions is given in the following.

In all scenarios, a regular cell plan and hexagonal cell layout is assumed. The studied deployment

comprises 19 sites, each with three sectors (cells) per site, which means in total 57 cells are simulated. A

wrap-around technique is used to avoid border effects. The differences between the scenarios are mainly

related to the site-to-site distances, antenna heights, user mobility and locations, etc., and are summarized

in Table A-1. Simulation assumptions common for all scenarios are provided in Table A-2.

Table A-1: Scenario specific parameters

ITU Urban Macro ITU Rural Macro 3GPP Case 1

Carrier frequency 2 GHz 800 MHz 2 GHz

Site-to-site distance 500 m 1732 m 500 m

BS antenna height 25 m, above rooftop 35 m, above rooftop 32 m, above rooftop

UE speed 30 km/h 120 km/h 3 km/h

User distribution Randomly and

uniformly distributed

over area, 100%

outdoor in vehicles

Randomly and

uniformly distributed

over area, 100%

outdoor in high-speed

vehicles

Randomly and

uniformly distributed

over area, 100% indoor

Outdoor-to-indoor

penetration loss

n/a n/a 20 dB

Outdoor-to-in-car

penetration loss

9 dB 9 dB n/a

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Table A-2: Simulation assumptions

Transmission

bandwidth

10 MHz

Subcarrier

spacing

15 kHz

Number of

subcarriers

320

Frequency reuse 1

Number of cells 57

Number of users

per cell

10 (on average)

Wrap-around Yes

Interference modelling

All links modeled

Transmission

schemes

Reference non-CoMP system:

Codebook-based precoded adaptive rank MIMO [DPS+08],

4x2 co-polarized antennas, two groups of BS antennas, 10 λ spacing between

groups, λ/2 spacing within groups, MMSE-SIC UE receiver.

Joint processing CoMP:

Joint transmission over 9 sectors/cells (3 sites), zero-forcing precoding, dual stream,

4x2 co-polarized antennas, 10 λ spacing at BS, MMSE-SIC UE receiver.

Coordinated beamforming CoMP:

Coordination over 9 sectors/cells (3 sites), single stream per user, SDMA with up to

4 users per cell, 4x2 co-polarized antennas, λ/2 spacing at BS, MMSE-SIC UE

receiver.

Coding Practical turbo codes

Modulation QPSK, 16QAM, 64QAM

Link adaptation Non-ideal, based on delayed feedback

Data traffic model Full buffer

Three different system configurations are simulated, all based on 4x2 antenna configurations. As

reference, a non-CoMP system based on LTE release 8 is considered. In this case, each sector is equipped

with a clustered antenna array comprising four elements divided into two groups separated 10

wavelengths and with half a wavelength separation within the groups. The transmission scheme is

codebook-based precoded adaptive rank MIMO according to LTE release 8 [DPS+08]. The second

system configuration employs joint processing CoMP based on zero-forcing (ZF) precoding. Each sector

is equipped with a four-element uniform linear antenna array with 10 wavelength element separation.

Finally, the third system configuration is also a CoMP system, but now based on coordinated

beamforming. In this configuration, the sectors are equipped with four-element uniform linear antenna

arrays with half a wavelength element separation. In all system configurations, the UEs have two antenna elements separated half a wavelength and employs an ideal Minimum Mean Square Error (MMSE)

receiver with additionally Successive Interference Cancellation (SIC) functionality.

The layout for the reference non-CoMP system is depicted in Figure A-1a, where each cell (a numbered

hexagon) acts independently. To reflect the fact that each cell acts independently, each cell is depicted in

a different color. The locations of the sites are represented by red circles. In Figure A-1b the layout for the

CoMP system is illustrated. For simplicity, static, pre-defined CoMP clusters are considered. Each CoMP

cluster in the system is formed by grouping 9 neighboring cells (or sectors) served by 3 sites, resulting in

coordination clusters of 9 cells (i.e. the transmissions within these 9 cells are coordinated). The cells

belonging to the same coordination cluster are depicted in the same color.

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a) Reference layout (each cell acts independently) b) CoMP layout, coordination clusters of 9 cells

Figure A-1: Cell layouts used in the simulations. Note that the site-to-site distance is the same in

both cases (i.e. the cell size is the same), and that wrap-around (not shown here) is used in the

simulations in order to avoid border effects.

All users are assumed to have full buffers, and the average number of users per cell is 10. The users are

uniformly distributed across the simulated area, and each user moves at a constant scenario-specific

speed, cf. Table A-1.

There are three available modulation schemes (QPSK, 16QAM, and 64QAM) and six different channel

code rates (1/10, 1/3, 1/2, 2/3, 3/4, and 8/9). The transport format is selected to maximize the expected

throughput, but the link adaptation is non-ideal in the sense that it is based on delayed feedback. Packet

decoding error probability is modeled according to a mutual information based link-to-system interface

[BAS+05].

The simulations assume perfect channel and interference estimation at the UE. The OFDM transmission

is further modeled as perfectly orthogonal and any potential inter-symbol or inter-carrier interference

caused by channel time dispersion exceeding the cyclic prefix is neglected. Overhead such as reference

signals, e.g. for channel and interference estimation, or protocol headers are neither accounted for. It is

further assumed that all necessary channel knowledge required by the CoMP schemes is available at the

BS. However, feedback and reporting delays are modeled.

The used performance measures are the cell spectral efficiency measured in bps/Hz/cell, and the cell edge

user spectral efficiency (the 5th percentile of the normalized user throughput) measured in bps/Hz. The

first one is focused on the system performance, while the second one may be described as a user centric

performance measure and/or the fairness in the system when put in relation to the cell spectral efficiency.

A.1.2 Simulation Results

Table A-3 shows the results achieved for the 3GPP Case 1 scenario. As can be expected in this low-

mobility scenario, it can be seen that the joint processing based on ZF precoding works very well. It

achieves a cell spectral efficiency of 3.81 bps/Hz/cell, which can be compared to that of the non-CoMP

system which is 2.56 bps/Hz/cell. Also the cell edge user spectral efficiency is improved from 0.074 to

0.108 bps/Hz. Both these improvements are in the order of 50% in relative terms. Coordinated

beamforming does also perform reasonable well here; it achieves a cell spectral efficiency of 3.01

bps/Hz/cell, which is 15-20% better than the non-CoMP system. However, the cell edge performance is

only slightly better, 0.078 bps/Hz compared to 0.074 bps/Hz,

The results for ITU Urban Macro are shown in Table A-4. In this case the UE speed is 30 km/h, and it can be seen that this hits on the performance of the ZF based joint processing transmission scheme. As can be

seen, both the cell spectral efficiency and the cell edge user spectral efficiency of joint processing based

on ZF is actually slightly lower than that of the non-CoMP system based on LTE release 8. This is of

course due to the fact that short-term channel state information needed for the ZF precoding gets

outdated, hence resulting in that the applied precoding weights are invalid. Coordinated beamforming, on

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

1

2 3

4

5 6

7

8 9

10

11 12

13

14 15

16

17 18

19

20 21

22

23 24

25

26 27

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the other hand, is robust to user mobility and works well in this scenario. The simulated cell spectral

efficiency is 1.97 bps/Hz/cell and the cell edge user spectral efficiency 0.053 bps/Hz, compared to 1.32

bps/Hz/cell and 0.035 bps/Hz for the non-CoMP system. This translates into a relative gain over the non-

CoMP system in the order of 50%, both in cell spectral efficiency and cell edge user performance. It can

be noted that this relative gain over the non-CoMP system is higher than in 3GPP Case 1, which most

probably is explained by the fact that we now have outdoor users, and also a line-of-sight (LoS)

component in the channel model, which together makes it easier to exploit the directivity properties of the

beamforming.

Finally, Table A-5 shows results for the ITU Rural Macro scenario where the users are moving at 120

km/h. It can be seen that the joint processing based on ZF breaks down even further, and that the performance now is far below that of the non-CoMP system. Again, it is demonstrated that coordinated

beamforming is robust to user mobility, and the gain over the non-CoMP system is significant. It achieves

a cell spectral efficiency of 2.64 bps/Hz/cell compared to 1.52 bps/Hz/cell for the non-CoMP system,

which is a relative gain in the order of 70%. In cell edge user spectral efficiency the gain is approximately

40%, an improvement from 0.050 bps/Hz to 0.071 bps/Hz.

Table A-3: System level performance results for 3GPP Case 1

No CoMP Joint processing based

on ZF precoding

Coordinated

beamforming

Cell spectral efficiency

[bps/Hz/cell]

2.56 3.81 3.01

Cell edge user spectral

efficiency [bps/Hz]

0.074 0.108 0.078

Table A-4: System level performance results for ITU Urban Macro

No CoMP Joint processing based

on ZF precoding

Coordinated

beamforming

Cell spectral efficiency

[bps/Hz/cell]

1.32 1.20 1.97

Cell edge user spectral

efficiency [bps/Hz]

0.035 0.026 0.053

Table A-5: System level performance results for ITU Rural Macro

No CoMP Joint processing based

on ZF precoding

Coordinated

beamforming

Cell spectral efficiency

[bps/Hz/cell]

1.52 1.05 2.64

Cell edge user spectral

efficiency [bps/Hz]

0.050 0.019 0.071

A.1.3 Conclusions

In this appendix we have carried out dynamic system level simulations of two different CoMP

approaches. One of the approaches is joint processing based on zero-forcing precoding that was

introduced in [WIN+D14], while the second approach is coordinated beamforming as described in

Section 2.2.1. The results show that joint processing achieves best performance in low mobility scenarios,

but that coordinated beamforming also achieves gains over the reference non-CoMP system configuration

in these scenarios. With higher user mobility, already at 30 km/h, the performance of joint processing

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degrades and is similar to that of the reference non-CoMP configuration, while coordinated beamforming

is robust to user mobility and provides significant gains.

Hence, the main conclusion is that coordinated beamforming type of shemes has the potential to provide

good performance in low mobility scenarios, however, not as good as joint processing type of schemes.

On the other hand, coordinated beamforming type of schemes is much more robust to user mobility. This,

in combination with the fact that coordinated beamforming type of schemes require lower feedback

overhead and put less demanding requirements on the backhaul connections in terms of latency and

capacity than joint processing type of schemes, suggests that coordinated beamforming schemes are

promising CoMP schemes to be realized in practical networks.

Finally, it should be emphasized that the results here are based on relatively ideal assumptions and that

the performance might change as more impairments are added and when practical implementation

limitations are taken into account. Nevertheless, the conclusions should remain the same since the

impairments and implementation limitations are expected to hit harder on the joint processing type of

schemes than on the coordinated beamforming type of schemes. It should also be pointed out that the

considered schemes in this study should be seen as exemplary configurations and not necessarily suitable

for implementation in a real system. However, in order to provide overall guidelines wrt technology

potential they fulfill their purpose.

A.2 Further details and performance evaluation of decentralized coordinated

beamforming

A comprehensive description and some main performance results of decentralized CoMP transmission

were given in Section 2.2.2. In this appendix, a summary and a detailed performance evaluation of

decentralized coordinated beamforming algorithm are presented. At the end, conclusion is drawn.

A.2.1 Decentralized coordinated beamforming algorithm

The decentralized coordinated beamforming algorithm with ZF mode selection is summarized in the

following.

1) Initialize 0t and set bb 0ν to some values, e.g., 0ν 0b .

2) Solve locally the distributed problem and transmit the resulting bζ to adjacent BSs.

3) Calculate the average inter-cell interference vector tζ and update local consistency prices

1tbν .

4) Use tζ in the distributed problem to get a feasible set of beamformers kkb ,m and the

minimum power bP .

5) Calculate ZFbP and ZF

,kbm using kkb 0ζ , .

6) IF bb PP ZF THEN

IF ZF−mode(b) is not active THEN

Set ZF−mode(b) active and send a message to the adjacent BSs

ELSEIF ZF−mode(b) is active THEN

Set ZF−mode(b) inactive and send a message to the adjacent BSs

7) IF any ZF−mode(b) b is active THEN

Use beamformers kkb ZF,m

ELSE

Use beamformers kkb ,m

8) Set 1 tt and go to step 1.

A.2.2 Numerical results

A simplified multi-cell transmission scenario with frequency flat fading with Jakes Doppler spectrum and

uncorrelated channels between antennas is considered, where 4K single antenna users are served

simultaneously by 2 BSs. We denote the maximum Doppler shift as df and the signaling period for the

exchange of bζ between BSs and ST . The number of antennas at each BS is 4T N . The simulation

scenario is depicted in Figure A-2. For simplicity, we assume that the users are divided into BN groups

where the users have identical large scale fading coefficients kba , , i.e., aaaaa 4,23,22,11,1 and

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the path gain to noise ratio is normalised to 102 Na . Furthermore, the distance between different user

groups is kept at identical values. The distance is defined by a parameter that fixes the ratio of path

losses between the different user groups. For example, 21,2

23,2

23,1

21,1 aaaa . When the parameter

is fixed at 0 dB, all K users are located exactly on the cell border. On the other hand, cells are

completely isolated when . In the coordinated single-cell beamforming case, each stream is

transmitted from a single BS. A user k is always allocated to a cell with the smallest path loss,

kbb

a ,B

maxarg

.

Figure A-2: Simulation scenario

First, the sub-optimality of the cases with reduced signalling in a static scenario ( 0df ) with random

channel realizations are studied. The following coordinated beamforming cases are compared:

1. Optimal user-specific interference constraint kb,ζ

2. BS-specific interference constraint, bbkb k Uζζ ,

3. Common interference constraint, bkkb ,ζζ ,

4. Zero-forcing for the inter-cell interference, bkkb ,0ζ , (optimal intra-cell beamformer design)

5. Zero-forcing for both intra- and inter-cell interference (channel inversion)

The ZF approaches are possible in the considered scenario since KN T .

Figure A-3 and Figure A-4 illustrate the average sum power of 4,2,4,, TB NNK system as a

function of , to meet 0 dB and 10 dB SINR constraints, respectively. Each user has an equal SINR

constraint. The coherent multi-cell beamforming greatly outperforms all other simulation cases when the

distance between different user groups is finite. At the cell edge, the coordinated beamforming cases

require 5-6 dB more power than the coherent case in order to meet the 0 dB SINR target, as seen in

Figure A-3. All three coordinated beamforming cases have very similar performance. Thus, the loss from sub-optimal signaling is minor. The distance does not have any impact on the zero-forcing cases since

no interference is allowed towards the other cell users. However, there is a large gain from the optimal

intra-cell beamformer design ( bkkb ,0, ζ ) as compared to the channel inversion.

The loss from sub-optimal signaling increases significantly for the case with one common constraint

when the SINR target is 10 dB. However, the loss is still minor in the BS-specific case. Also, the gain

from the optimal intra-cell beamformer design (with ZF for the inter-cell interference) is greatly reduced

as compared to the channel inversion. In general, the difference between the zero-forcing and coordinated

beamforming cases is reduced significantly.

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Figure A-3: Average sum power of 4,2,4,, TB NNK system for 0 dB SINR target.

Figure A-4: Average sum power of 4,2,4,, TB NNK system for 10 dB SINR target.

Figure A-5 and Figure A-6 demonstrate the cell edge ( 0 dB) performance of the distributed algorithm

in a time-correlated fading scenario as a function of dfTS to meet 0 dB and 10 dB SINR constraints,

respectively. The results for the distributed algorithm include cases with and without the ZF transmission

possibility. Figure A-7 compares the time evolution of the distributed algorithm with the centralized and

zero-forcing cases. The results demonstrate that the distributed algorithm performs nearly as well as the

centralized solution even at relatively high velocities, especially with low SINR targets. Note that 1

S 10dfT with 2S T ms and with 2 GHz center frequency corresponds to the velocity of 27 km/h.

For the 10 dB SINR target (and higher), high velocities or low signalling rates cause occasional high

power peaks. This is reflected in the increased average power. This can be at least partially alleviated by enabling the ZF mode possibility as illustrated in Figure A-6.

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Figure A-5: Average sum power of 4,2,4,, TB NNK system for 0 dB SINR target.

Figure A-6: Average sum power of 4,2,4,, TB NNK system for 10 dB SINR target.

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Figure A-7: Time evolution of the distributed algorithm with 0 dB SINR target, 05.0S dfT

A.2.3 Conclusion

A decentralized solution for the coordinated multi-cell multi-antenna minimum power beamformer design

problem was proposed. The beamformers can be obtained locally at each BS relying on limited backhaul

information exchange on the allowed inter-cell interference levels between adjacent BSs. The method is

able to guarantee feasible solutions even if the interference information is outdated or incomplete. The

proposed approach allows for a number of special cases, where the signaling is reduced at the cost of

somewhat sub-optimal performance. The numerical examples demonstrated that a near-optimal performance could be achieved even with significantly reduced backhaul information exchange and with

relatively high velocities and/or low signaling rates. Occasional high peaks in the transmitted power due

to outdated interference terms can be alleviated by switching to interference nulling mode when

necessary.

A.3 Simulation conditions details for codebook-based coordinated beamforming

This appendix provides more details on the simulation conditions for the results presented in section 2.2.3

The table below summarizes the simulation conditions.

Table A-6: Simulation conditions for codebook-based coordinated beamforming.

Parameter description Value

Cellular Layout Hexagonal grid, 19 sites, 3 sectors per site

Inter-site distance (ISD) 500 m

Traffic model Full buffer

Number of UEs per cell 10 in average

Number of snapshots & TTIs per simulation 100 snapshots, 700 TTIs per snapshot

Node B

Transmission power 46 dBm

Ante

nnas

Number of TX antennas 4

Antenna gain plus cable loss 14 dBi

Antenna pattern

25,

7012min

2 dB ( is angle in degrees)

70-degree sectored beam.

Propagation

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Pro

pag

atio

n

Path loss R10log6.371.128 dB ( R is distance in km)

Slow

fading

Standard deviation 8 dB

Correlation between

sites 0

Penetration loss 20 dB

Inter-cell interference Fully modelled, using the central cell method

UE

Antenna pattern 0 dBi

Number of RX-antennas 2 with MMSE spatial combining

Channel estimation Ideal

Noise figure 9 dB

Scheduler

Policy Round Robin

MIMO schemes

MIM

O

MIMO schemes Codebook-based beamforming (Single-User

MIMO) using LTE Rel-8 codebook

Cells coordination

Cluster size 3 cells: the master and the 2 dominant long-

term interferers of the UE served by the

master

Number of reported interfering beams 1, 3

Coordinated beamforming variant Most interfering beams

The coordination procedure described in section 2.2.3.is modelled as follows: at each TTI, scheduling

decisions are first performed as in the case of no coordination. The cells serving cell-edge UEs are then

identified as candidates for being a master. For each candidate master cell, the cluster is formed with the 2

dominant long-term interferers of the served UE, provided those interfering cells are not member of a

cluster already (as slave or master). Note that for the cluster formation, the candidate master cells are

picked in random order in order to ensure that the master role is fairly distributed among the cells.

A.4 Performance investigation of joint processing schemes considering area

coverage.

In this appendix, further evaluations of the joint processing schemes introduced in Section 2.3.1 are provided.

A.4.1 Centralized joint processing

Consider a cluster of K BSs, each one equipped with tN antennas, where M single-antenna users are

using a particular orthogonal dimension. When joint processing between BSs is allowed, the data to each

user is simultaneously transmitted from multiple BSs. In this case, a total of tK N antennas transmit

coordinately in the system, where tK N M . In this contribution, joint processing between BSs is

implemented by means of a joint linear precoding design and power allocation. Then, the received signals

at the M users can be expressed by means of a vector y of size 1M , as:

y = HW Px + n (A.1)

In the above expression, the matrix H of size tM K N includes the channel vectors of the system:

1

TT T

M H = h … h ,

where NtK

m C 1h stands for the channel between the m th user and the K BSs. Notice that nmC

denotes the set of m n complex matrices.

In the transmitter side, the joint processing is reflected in the choice of the W and P matrices. The

precoding matrix W of size tK N M contains the precoders designed for each of the users:

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1 M W = w … w ,

where 1w NtK

m C is the precoder for the mth user. In this case, the precoder matrix W is obtained

using a ZF approach, that is, M MHW = I , where M M

I is an identity matrix of size M M .

Since tK N M , the ZF matrix is the pseudo-inverse of the channel matrix:

1

H H

W H HH (A.2)

In the above expression, H

and 1

stand for the conjugate transpose and matrix inversion

operations, respectively.

The power allocation matrix P is a diagonal matrix of size M M . In this case, the maximum

available transmit power at each base station is restricted to a maxP value. For simplicity, equal power

allocation is performed by means of the expression for matrix P given in [ZD04]:

max

21, , ( )min

M Mk K k

F

P

P I

W

(A.3)

where ( )k

W are the rows of matrix W related to the kth base station, and F

stands for the Frobenius

norm of a matrix. It should be pointed out that this power allocation is suboptimal, since it typically

results in only one base station meeting the maximum transmitted power requirement with equality, and

hence, the remaining 1K BSs transmit below the maxP value.

Finally vector x of size 1M includes the precoded information symbols and n is the receiver noise

with variance 2

, which is spatially and temporally white and is also uncorrelated with the signals.

Under these assumptions, and assuming coherent multibase reception, the evaluation metric under consideration is the average sum-rate per cell:

2

1

1log 1 SINR

M

H m

m

C EK

(A.4)

where the SINR for the mth user is given by the following expression:

2

2 2

1

SINRm m m

m M

m i i

ii m

p

p

h w

h w

(A.5)

In the above expression, mp is the power allocated to the mth user, that is, 2( , )m mmp P .

A.4.2 Partial joint processing

The partial joint processing scheme introduces a certain level of multi-user interference in the system due

to the limited CSI available in the central unit. This multi-user interference contribution can be defined by

analyzing the expression of the signal received by one user. Assume that mBS is the active set of BSs that

give service to the mth user, whereas kM is the set of users that are served by the kth base station. Note

that the cardinality of any mBS is such that 1 mBS K , whereas the cardinality of any kM ranges

between 0 kM M . Hence, for the mth user, the received signal can be expressed as a sum of the

signal of interest, multi-user interference and noise:

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( ) ( ) ( )

( ,:) (: , )

( ) ( ) ( ) ( ) ( ) ( )

( ,:) (: , ) ( ,:) (: , )

1 1

,

m

jk

m m

k k k

m m m m m

k BS

MMk k k j j j

m i i i m i i i

k BS i ij BSi m

m

y p x

p x p x

n

H W

H W H W (A.6)

where mBS is the complement set of mBS .

( )

( ,:)

k

mH stands for the mth row of the tM N matrix ( )k

H ,

which includes the columns of matrix H related to the kth base station (in the case of ( )

(: , )

k

mW , the same

applies to the rows of matrix W ). Similarly, ( )k

mp is the power allocated to the mth user from the kth base

station. In the above expression, it is assumed that the channel coefficients ( )

( ,:)

j

mH included in the multi-

user interference term cannot be estimated by the mth user, since those BSs are not included in mBS .

Therefore, this term represents the multi-user interference contribution that remains in the system when

the partial joint processing scheme is implemented by the cluster of BSs.

A.4.3 Distributed joint processing

The distributed joint processing scheme assumes a local per-base station design of the linear precoding

matrix and power allocation, since only local CSI is available at each base station. Hence, the cardinality

of the set of spatially separated users that can be served by each base station in the cluster is reduced to

tN . Notice that in a multicarrier scenario, tN users can be served per subcarrier.

Joint processing between BSs is still allowed. In the system model, there are K BSs and M users, with

tK N M . Since each base station can give service only to tN users in the spatial domain, the problem

of assigning users to BSs under a joint processing assumption arises. This multibase scheduling problem

has been previously considered in [SGH08], where low-complexity algorithms have been proposed in

order to optimize a given objective function.

In this contribution, the multibase scheduling problem is solved as follows: kM includes the set of tN

users that present the highest channel gain with respect to the kth base station. This approach is similar to

the active set procedure proposed for the partial joint processing scheme. However, the decision process

of determining which base station transmits to each user is now moved towards the base station, and solved in a distributed manner. As shown in [SGH08], this multibase scheduling solution results in

different degrees or stages of coordination in the cluster depending on the distribution of the users over

the cluster area and the system parameters, i.e., each of the M users can be served by a number of BSs

that ranges from zero to K . Hence, the distributed joint processing scheme implies that a certain number

of users in the cluster may remain without service and then, some sort of fairness mechanism would be

additionally required.

The signal received by one user can still be modeled with the expression proposed for the partial joint

processing scheme, where the linear precoding matrix ( )k

W is the pseudo-inverse of the channel matrix

( )kH and the power allocation is performed equally dividing the maximum available transmit power

maxP into tN users.

A.4.4 Numerical results

We consider a cluster of 3K BSs, each one equipped with an array of 3tN antennas, and 3M

single-antenna users. The objective of the simulations is to characterize and compare the performance of

the centralized joint processing (CJP), partial joint processing (PJP) and distributed joint processing (DJP)

schemes under different evaluation metrics and a non-uniform distribution of users in the cluster area, see

Figure A-8. The cluster radius and height are 500R and 433h meters, respectively.

The channel vector between the mth user and the kth base station is modeled as:

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'

mk mk s p h h ,

where the shadow fading is a random variable described by a log-normal distribution, 0,8s dBN , the

pathloss follows the 3GPP LTE model, 10 ( )148.1 37.6 logp mkdB r , and

'

mkh includes the small-

scale fading coefficients, which are i.i.d complex Gaussian values according to 0,1CN .

In the simulations, a grid of possible user locations is defined over the cluster area. Then, the users are

uniformly placed over a small area around each location, ( ,x x y y , with 16x R and

16y h ), and different metrics are evaluated. The results are averaged over 500 independent channel

realizations for each position of the grid. Different system SNR values are also simulated (reference value

for one user located at the cell-edge), in order to define noise and interference-limited scenarios.

Figure A-9 and Figure A-10 show the average sum-rate per cell for different transmission schemes in a

noise and interference-limited scenario, respectively. Results are plotted versus the normalized distance from one base station, [Distance/R] (the symmetry of the cluster area guarantees that no information is

lost when changing the 3-D plot into a 2-D plot). “PJP-10dB”, “PJP-20dB” and “PJP-40dB” plots stand

for the results of the PJP scheme when threshold values of 10, 20 or 40 dB are simulated. For comparison

purposes, results for the conventional single-base station transmission scheme, “1BS”, are also included.

Results labeled with “2BSs” are obtained when 2 BSs from the cluster transmit to each user. It should be

noticed that this is a particular case of the PJP scheme.

Comparing both figures, it can be seen that the differences between the simulated transmission schemes

arise in interference-limited scenarios (Figure A-10). The CJP scheme clearly outperforms the remaining

schemes (the “PJP-40dB” achieves the same performance since almost 3 BSs are jointly transmitting to

each user when the threshold value is fixed to 40 dB) at the cost of higher backhaul and signalling

requirements. On the other hand, the PJP scheme shows a trade-off between the backhaul and signaling requirements and the achieved average sum-rate per cell, that is, its performance improves as the

coordination degree between BSs or the threshold value increases. If we compare the performance of the

PJP curves with the “2BSs” scheme results, we can conclude that allowing a different number of BSs to

transmit to each user, depending on the user channel conditions, can also improve the average sum-rate

per cell, since the flexibility of the system is increased. Finally, it should be noticed that the DJP scheme

is not a solution for noise-limited environments. However, its performance is close to the “2BSs” and

“PJP-10dB” cases for interference-limited scenarios.

From a practical point of view, the major drawback in the spatial domain of the DJP scheme is the

limitation in the maximum number of served users per base station, which may require an additional

fairness mechanism depending on the requirements of the users. However, this can be easily done by optimizing the multibase scheduling technique, or by exploiting the subcarrier allocation.

Another interesting conclusion is the fact that each transmission scheme shows a different behavior when

taking into account aspects of fairness or uniformity of the evaluation metric over the cluster area.

Transmission schemes implying a joint design of the linear precoding matrix (CJP and PJP with

threshold 20 dB), achieve a higher uniformity of the average sum-rate per cell over the cluster area,

especially in the interference-limited scenarios as shown in Figure A-10.

On the other hand, these schemes also show different levels of robustness when computing the evaluation

metric. In this case, the increased robustness of the CJP scheme and the PJP scheme with

threshold 20 dB is revealed as a lower standard deviation of the evaluation metric regardless of the

position of the user over the cluster area, e.g., for a system SNR of 15 dB, the DJP scheme shows a 50 %

worse standard deviation over the average sum-rate per cell and less uniformity over the cluster area than

the CJP scheme.

Figure A-11 and Figure A-12 show the average number of BSs that are included in the active set of a user

in the PJP scheme for a threshold value of 10 and 20 dB, respectively (when the threshold value is fixed to 40 dB, we obtain a flat surface where the 3 BSs are always included in the active set of the user). This

parameter can be seen as a rough estimation of the backhaul and signalling requirements of the PJP

scheme. The figures confirm that the backhaul and signaling requirements of the PJP scheme depend both

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on the threshold value and the user position over the cluster area, whereas they remain fixed for the CJP

and DJP transmission schemes. This result is of great importance since the threshold value may

dynamically change in time in order to fulfill the given system or user requirements. As a last remark, it

should be pointed out that also the transmitted power per base station of the PJP scheme depends on the

threshold value, e.g., for a threshold value of 10 dB, in average, a 14.14 % of the total transmitted power

in the system is saved when compared to the CJP and DJP schemes. This value decreases to 5.74 % when

the threshold value is set to 20 dB.

Figure A-8: The cluster area under consideration is the shadowed area close to the cell-edge of each

cell.

Figure A-9: Average sum-rate per cell versus the normalized distance from the base station

[Distance/R] for a system SNR of 0 dB.

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Figure A-10: Average sum-rate per cell versus the normalized distance from the base station

[Distance/R] for a system SNR of 15 dB.

Figure A-11: Average number of BSs in the partial joint processing scheme transmitting to each

user versus user position in the cluster area. Threshold value = 10 dB.

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Figure A-12: Average number of BSs in the partial joint processing scheme transmitting to each

user versus user position in the cluster area. Threshold value = 20 dB.

A.5 Joint processing with reduced backhaul requirement by MAC coordination

A system simulator has been developed with 19 single antenna base stations and wraparound. Each single-antenna user is dropped with uniform probability inside each cell. Fairness is guaranteed by a

proportional fairness scheduler. In other words, the quality of service weights 1, , K are the

reciprocal of the users’ average windowed rates. The reference SNR is defined as the SNR at the cell

vertex. The channel has been modelled considering Rayleigh and path loss effect. The values of the main

parameters used for the simulation are summarized in Table A-7

Table A-7. Main parameters used in the simulations.

N (number of base stations) 19

center to edge distance 1km

number of users per cell 30

number of base station clusterings 30

reference SNR 15dB

path loss exponent 4.5

prop. fair forgetting factor 0.01

-4000 -3000 -2000 -1000 0 1000 2000 3000 4000

-4000

-3000

-2000

-1000

0

1000

2000

3000

4000

1

2

3

4

5

6

7

8

9

10

11

1213

14

15

16

17

18

19

distance [m]

dis

tance [

m]

x

xx

x

x x

x

x

x

x

x

x

x

x

x

x

x

x

x

Figure A.13 Example of a clustering and of the corresponding user scheduling.

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In Figure A.13 an example of a clustering and of the corresponding user allocation is shown for a given

frame and for the case B=4. Cells with the same colour belong to the same cluster, and the positions of

the users scheduled for transmission are denoted by a bold x.

In Figure A.14 and Figure A.15 the performance of the proposed algorithm is shown respectively in terms

of average rate per cell and average rate per user (sorted from the worst to the best user) for the case B=10, which corresponds to a 50% reduction in the number of base stations sharing the data of the users

scheduled for transmission in a given frame.

Four different techniques are compared: non-cooperating base stations (dotted line), static coordination

(dash-dotted line), i.e. 2N

LB

clusters of cooperating base stations are kept fixed during all the

simulation. In each cluster the users are selected for transmission using a proportional fair scheduler, dynamic coordination (dashed line), full coordination, i.e. all the N base stations cooperate together and

up to N users are scheduled for transmission in each frame with a proportional fair approach.

0 2 4 6 8 10 120

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

x [bit/s/Hz]

P[a

vera

ge r

ate

per

cell<

x]

no coord.

static coord.

dynamic coord.

full coord.

Figure A.14 CDF of the average rate per cell.

The proposed dynamic coordination technique performs near to the full coordination case, while almost

halving the number of cells that cooperate in a given frame. As a matter of fact, in the full coordination

case the number of cooperating base stations is 19, while in the dynamic coordination case the two

clusters of cooperating base stations are composed by 10 and 9 base stations. Such a reduction in the

number of base stations that cooperate at the same time allows a reduction of about 50% in terms of the

number of users whose data needs to be shared in order to allow joint transmission. Furthermore the

static coordination technique offers only a slightly improved edge-of-cell performance with respect to the

non-coordinated case (left part of Figure A.15), at the price of a reduced average rate per cell (Figure A.14). The tail of the static coordination curve in Figure A.15 is due to the attempt to serve the users that

are at the edge of two base stations belonging to two different clusters.

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0 100 200 300 400 500 6000

0.1

0.2

0.3

0.4

0.5

sort(user index)

avera

ge p

er-

user

rate

[bit/s

/Hz]

no coord.

static coord.

dynamic coord.

full coord.

Figure A.15. Average rate per user vs user index (sorted from the worst to the best user).

A.6 Further details and performance evaluation of generalized CoMP

transmission method

A detailed description and some performance results of generalized CoMP transmission were given in

Section 2.3.3. This appendix presents supplementary details for beamformer design with coordinated BS

processing and introduces some beam allocation algorithms as well as presents a detailed performance

evaluation of the proposed concept. At the end, conclusion is drawn.

A.6.1 Beamformer design with coordinated BS processing

Details for solving two different optimization problems, i.e., weighted SINR balancing and weighted sum

rate maximization are provided. First, weighted SINR balancing is considered where the objective is to

keep the SINR per data stream s in fixed ratios in order to guarantee fairness between streams/users, i.e.

0 ss , and 0 has to be maximized subject to per BS power constraints. This can be formulated as

maximization of the minimum weighted SINR per stream:

,...,1, s.t.

minmax

2

2,

2

1,1

,.H

,

2

20

2

,.H

,1

,...,1

b

i

ss

s

ss

Ss

Bbsb

S

si b

ibkbskbs

b

sbkbskbs

Ss

NbP

aN

a

m

mHww

mHw

(A.7)

where the variables are TC,N

sb m and skN

sR

Cw , Ss ,...,1 . The SINR balancing problem is not

jointly convex in variables sb,m and sw . However, for a fixed Sssb ,...,1,, m it has a unique solution

given by the linear minimum mean square error (LMMSE) receiver which provides the maximum SINR

for stream s [TPK09a]. Furthermore, for a single antenna receiver or a fixed sw , optimization problem

is quasi-convex in sb,m [TCJ08b]. Thus, it can be solved by using the bisection method [Boy04]

presented in Algorithm 2 in [TCJ08b]. The constraints of the feasibility problem presented in Algorithm 2

in [TCJ08b] can be expressed as a generalized inequality with respect to the second-order cone [Boy04].

Finally, a solution for the SINR balancing problem can be found by using a coordinate ascent method

[Ber03], i.e., at each iteration, the objective is maximized with respect to one set of variables sw (or

sb,m ) while considering the other set fixed. In other words, alternating between calculation of sw using

LMMSE receiver and Algorithm 2 in [TCJ08b] until convergence for fixed sb,m and sw , respectively.

More details can be found in [TCJ08b, Algorithm 1]. The block coordinate ascent method converges to

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the global optimum if the problems solved at each step have unique solutions [Ber03]. The optimal

objective value 0 for fixed sw is indeed unique, but the resulting Sssb ,...,1,, m is not guaranteed to

have a unique solution in general, due to the quasi-convexity of the original problem [TCJ08b].

Therefore, global optimality of the above method can only be guaranteed for the fixed and single-antenna

receiver cases.

Now, weighted sum rate maximization problem is considered for coordinated multi-cell transmission subject to per BS power constraints. The problem can be formulated as follows

Ssp

NbPp

Ss

paN

pa

sbss

b

s

sbsb

s

S

si b

ibibkbskbs

b

sbsbkbskb

s

S

s

s

b

i

ss

s

ss

s

,...,1,0,1,1

,...,1

,...1 , s.t.

1max

,22

B,

S

2

2,,

,1,1

2

,,,H

,

2

20

2

,,,H

,

1

vw

v

vHww

vHw

(A.8)

where the variables are TC,N

sb v , skN

sR

Cw , R,sbp and Rs , Ss ,...,1 and

T,B

T,1 ,..., sss

svvv is the normalized transmit vector over the joint processing set sB for the stream s .

The optimization problem is not convex, and, hence, the problem of finding the global optimum is intrinsically non-tractable. However, the problem can be maximized with respect to different subsets of

variables by considering the others fixed. A detailed analysis of the problem solving algorithms are given

in [TCJ08a] for coherent multi-cell MIMO transceivers. An efficient iterative solution was proposed

where each sub-step was solved as SOCPs or geometric programs [BKV07]. Even though each sub-

problem was optimally solved, only local optimal solutions could be found due to the non-convexity of

the original problem. Now, the same method can be extended to the general coordinated multi-cell

transmission by modifying the constraints of all sub-problems presented in [TCJ08a, Chapter III]. For the

sake of clarity and space, the exact details are omitted in this report.

A.6.2 Beam allocation algorithms

In the coordinated single-cell beamforming case ( ss 1B ), each stream is transmitted from a single

BS. In such a case, a user sk is typically allocated to a cell b with the smallest path loss. Near the cell

edge, however, the optimal beam allocation strategy may also depend on the time varying properties of

the channel kb,H . Thus, large gains from fast BS assignment (cell selection) algorithms are potentially

available for the cell edge users. As the optimal BS assignment per beam requires an exhaustive search

over all possible combinations of beam allocations and re-computation of the algorithm for each

allocation, sub-optimal allocation algorithms are needed in practice.

The allocation problems have often been addressed for systems with single-antenna users. When the users

have multiple receive antennas, the signal space of each user has multiple dimensions allowing for multiple beams to be allocated per user. This further complicates the allocation problem. Since the

transmit beamforming vectors , and thus, the corresponding receive beamforming vectors at each user are

affected by the set of selected users, it is impossible to know the actual receiver structure at the

transmitter before the final beam allocation. An obvious candidate for an intelligent initial guess of the

receiver matrix, and the one used in the proposed algorithms, is the optimum single user receiver, i.e., the

dominant left singular vectors of H,,,, kbkbkbkb VΛUH . Hence, each user channel kb,H is decomposed

into TR ,min NNk

virtual single-antenna users (eigenbeams) with corresponding channel gains

TR,,,,,,,,,,, ,min,...,1, NNlaak

Hlkblkbkbkb

Hlkbkblkb vHuh

where lkb ,,u , lkb ,, and lkb ,,v correspond to l th column (or diagonal term) of kb,U , kb,Λ and kb,V ,

respectively.

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The aim of any beam-to-BS allocation algorithm is to select such BSs that the resulting beamformers

mutually interfere as little as possible while providing large beamforming gains towards the intended

users. A set of heuristic allocation algorithms for the coordinated beamforming case are considered in the

following:

1. Greedy selection: The algorithm consecutively selects at most R Tmin ,kk

N N channels from

the total set of R Tmin ,kk

N N channels. First, the strongest channel among all channels

lkblkb

,,,,

maxarg h is selected. Subsequently, on each step of the selection process, the channel with

the largest component orthogonal to the previously selected set of beams is chosen.

2. Maximum eigenvalue selection: The eigenvalues of the virtual channel vectors lkb ,,h are simply

sorted and at most TN streams are allocated per cell. Spatial compatibility with other channels is

not considered.

3. Eigenbeam selection using max rate criterion: A simplified exhaustive search is carried out over

all possible combinations of beam-to-BS allocations, where the beamformer sb,m for each

stream s is matched to the virtual channel vector ss lkb ,,h i.e., blkbsb P

ssST,,, vm . For

each allocation, the receivers sw and the corresponding SINR values Sss ,...,1, are

calculated. Finally, the selection of the optimal allocation is based on the maximum rate

criterion, i.e.,

S

s

slkb 1

2,,

)1(logargmax .

4. Eigenbeam selection using maxmin SINR criterion: Same as above except that the selection is

based on maxmin SINR criterion, i.e., sSslkb

,...,1,,

minargmax

.

It is worth noting that the usage of the greedy approach is rather limited since it can only be used when

TNS . Thus, it cannot be applied to the interference limited scenarios, i.e., TNS .

Figure A-16: Simulation scenario for 4 users and 2 BSs

A.6.3 Numerical results

A simplified multi-cell transmission scenario with frequency flat fading is considered where 42K

users with 21R N receive antennas are served simultaneously by 2 BSs. For simplicity, the BSs were

assumed to have an equal maximum power limit TP , i.e. bPPb T . The SNR for each user k was

based on the smallest pathloss among BN BSs and defined as 02,

BT maxSNR NaP kb

bk

. In the

simulations, the elements of the channel matrices kb,H were modelled as i.i.d. Gaussian random

variables. The simulation scenario is depicted in Figure A-16. For simplicity, we assume that the users are

divided into BN groups where the users have identical large scale fading coefficients kba , , i.e.,

aaaaa 4,23,22,11,1 and the path gain to noise ratio is normalised to 102 Na . Furthermore,

the distance between different user groups, as well as, SNR per user were kept at identical values. The

distance is defined by a parameter which fixes the ratio of path losses between the different user

groups. When the parameter is fixed at 0 dB, all K users are located exactly on the cell border. On the

other hand, cells are completely isolated when .

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We study the achievable sum rate of the SINR balancing and the sum rate maximization algorithms both

with per BS power constraints and with equal weighting of streams ss 1 . The following cases were

compared by simulations:

1. Coherent multi-cell MIMO transmission ( ss BB ) with per BS power constraints.

2. Coordinated single-cell transmission ( ss 1B )

o Exhaustive search over all possible combinations of beam allocations.

o Fixed allocation, i.e., user sk is always allocated to a cell b with the smallest path loss.

o Maximum eigenvalue selection.

o Eigenbeam selection both with max rate and maxmin SINR allocation criteria.

o Greedy selection (can be applied when TNS ).

3. Non-coordinated single-cell transmission where the inter-cell interference is neglected in the

precoder design.

4. Single-cell transmission with TDMA, i.e., without inter-cell interference.

The inter-cell interference is omitted in the precoder design both in the non-coordinated transmission case

and in the TDMA case, and thus the resulting beamformers are identical for both cases. In the TDMA

case, the transmission is time multiplexed between the BSs, and hence the reception is interference free.

In Figure A-17, the sum rate maximization (solid) and the SINR balancing (dashed) algorithms are

compared in 1,2,2,4,,, RTB k

NNNK scenario in terms of the ergodic sum rate as a function of the

distance between different user groups, and for 20 dB single link SNR. As expected, the sum rate

maximization largely outperforms the SINR balancing in all simulated cases. The most notable increase

in sum rate can be observed in the coordinated single-cell beamforming case. This is due to the fact that

the sum rate maximization algorithm ends up assigning zero powers to some users and hence inherently

avoids the spatial overload.

It was already shown in [WIN+D14] and [TPK09a] that in the case of SINR balancing algorithm the

coherent multi-cell beamforming greatly outperforms all the non-coherent cases, e.g., coordinated/non-

coordinated single-cell beamforming. This is also the case for the sum rate maximization algorithm. This

is due to the fact that the coherent multi-cell beamforming can fully eliminate the inter-cell interference

unlike the single-cell beamforming methods. However, the sum rates of coherent and non-coherent schemes become asymptotically equivalent as the distance approaches the infinity, i.e., there is no gain

from the coherent coordinated multi-cell processing.

Figure A-17: Ergodic sum of user rates of 1,2,2,4,,, RTB k

NNNK system with SINR balancing

and sum rate maximization algorithms at 20 dB single link SNR

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Figure A-18: Ergodic sum of user rates of 1,2,2,2,,, RTB k

NNNK and 2,2,2,2 systems with

sum rate maximization algorithm at 20 dB single link SNR

Figure A-18 shows the ergodic sum rates of systems 1,2,2,2,,, RTB k

NNNK (dashed) and 2,2,2,2

(solid) for the sum rate maximization algorithm at 20 dB single link SNR. The system with 1R k

N is

not interference limited since TNS whereas the system with 2R k

N can be spatially overloaded

( TNS ) at the cell edge. However, as mentioned previously, the sum rate maximization algorithm

inherently avoids the spatial overload by assigning zero (or near to zero) powers to some streams. Hence,

using two receive antennas instead of one large gains are available to all simulation cases as can be seen

from Figure A-18.

In Figure A-19, coordinated single-cell beamforming with different beam-to-BS allocation algorithms

with the aim of sum rate maximization are compared in 2,2,2,2,,, RTB k

NNNK scenario at 20 dB

single link SNR. Near the cell edge, the optimal beam allocation strategy depends heavily on the

properties of channel realizations between BSs and users. Hence, large gains from different beam

allocation algorithms are available for the cell edge users. It can be seen that the channel dependent beam

allocation is beneficial within about 5 dB region around the cell edge. Otherwise, a beam should be

allocated to a cell with the smallest path loss.

Sub-optimal (heuristic) beam allocation algorithms perform relatively well at the cell edge compared to

the optimal case, i.e., exhaustive search. In particular, a simple maximum eigenvalue based selection

algorithm performs nearly the same as the optimal case.

Note that coordinated single-cell beamforming with any beam allocation algorithms as well as coherent

multi-cell beamforming cases asymptotically achieve the sum capacity in 2,2,2,2,,, RTB k

NNNK

system. This is due to the fact that the two-cell scenario reduces to a two totally isolated single-cell single

user cases when the approaches infinity, and thus, the optimal power allocation and beamforming are

easy to achieve.

In Figure A-20, optimal beam allocation algorithm with coordinated/non-coordinated single-cell

beamforming cases are compared to the same beamforming cases but where a single UE is served from a

single BS only. The simulation scenario is the same as previously. It can be seen that for

2,2,2,2,,, RTB k

NNNK system optimal beam allocation algorithm provides approximately a 2 dB

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and a 1 dB gain with coordinated and non-coordinated beamforming, respectively. Thus, it is beneficial

for the system if a single user with multiple receiver antennas can be served from multiple nearby BSs.

Figure A-19: Ergodic sum of user rates of 2,2,2,2,,, RTB k

NNNK system with different beam-

to-BS allocation algorithms at 20 dB single link SNR

Figure A-20: Ergodic sum of user rates of 2,2,2,2,,, RTB k

NNNK system with different beam-

to-BS allocation algorithms at 20 dB single link SNR

A.6.4 Conclusion

A generalized method for joint design of the linear transceivers with coordinated multi-cell processing

subject to per-BS power constraints was proposed for two different optimization objectives, i.e., the

weighted SINR balancing and the weighted sum rate maximization. The generalized method can

accommodate a variety of scenarios from coherent multi-cell beamforming across a large virtual MIMO

channel to a single-cell beamforming with inter-cell interference coordination and beam allocation. The

performance of SINR balancing and sum rate maximization algorithms with different multi-cell

transmission methods (coherent/non-coherent and coordinated/non-coordinated) with optimal and sub-

optimal beam allocation algorithms was numerically evaluated. The numerical results showed that the

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sum rate maximization algorithm largely outperforms the SINR balancing in terms of ergodic sum rate in

all simulated cases. This is obviously due to the fact that the sum rate maximization algorithm ends up

assigning zero powers to some beams and hence inherently avoids the spatial overload. The numerical

examples demonstrated that the coherent multi-cell beamforming greatly outperforms the non-coherent

cases especially at the cell edge. However, it was shown that the coordinated single-cell beamforming

with interference avoidance and dynamic beam allocation has a good performance-complexity trade-off.

In particular, the coordinated single-cell beamforming with sub-optimal, but simple, beam allocation

algorithm, i.e., maximum eigenvalue selection, performed relatively well with reduced complexity

compared to the optimal beam allocation scheme. Furthermore, significant performance gains are

available when using multiple receive antennas.