I. INTRODUCTION

Recently, the high-rate data transmission has been one

of key issues in wireless mobile communications. Various

classes of multimedia traffic need to be supported under

the wireless LAN (local area network) as well as cellular

environments. A number of approaches have been

considered to improve the performance of capacity and

spectral efficiency in wireless communication systems.

MIMO (multiple-input multiple-output) is an emerging

technology offering high spectral efficiency with the

increased link reliability and interference suppression

[1]~[4].

There are various categorized schemes of MIMO,

depending on the target performance characteristics.

Transmit diversity uses multiple transmit antennas to

enhance the link reliability by transmitting multiple copied

signaling in various ways [5]. Receive diversity is

reciprocal to the transmit diversity, where multiple receive

antennas are used to improve the error performance by

combining the received signals through multiple receive

antennas. Space-time coding is a popular solution for

diversity gain and/or coding gain, which can be easily

combined with all kinds of multiple antenna systems.

Space-time block coding has been already adopted in 3GPP

(3rd generation partnership project) standardizations, which

is characterized by its simple transmit and receive

structures for implementations [6]~[9]. Space-time trellis

coding is another type of space-time coding, achieving

diversity gain and coding gain at the cost of computational

complexity. Beamforming is a good candidate for

interference suppression and high capacity performance

with a long history of research work. At first, it was used

for military applications. Smart antenna exploits

beamforming to boost up the system capacity and reduce

the interference in cellular environments. Adaptive

beamforming keeps updating the complex weights of array

for an optimal SINR (signal-to-interference-plus-noise

ratio), while switched beamforming switches between the

predetermined beams selected from a library of weights

based on the received signal strength measurements.

Spatial multiplexing is the most advanced MIMO scheme.

1

In wireless mobile communications, the high-rate data transmission is significant to support different classes of

multimedia services. Multiple-input multiple-output (MIMO) is an emerging technology which offers high spectral

efficiency over wireless links. Recently, the industrial organizations have proposed their MIMO techniques in 3rd

generation partnership project (3GPP) standardizations. In 3GPP, various multi-antenna schemes are on active

discussions, especially when combined with high speed downlink packet access (HSDPA). In this paper, we investigate

the proposed MIMO techniques which have been agreed to be included in 3GPP MIMO working item technical report

(WI-TR).

Keywords: MIMO, 3GPP, HSDPA, MU-MIMO, SU-MIMO, Wireless scheduling

Sungjin Kim, Hojin Kim : Samsung Advanced Institute

Juho Lee: Samsung Electronics

Kwang Bok Lee: Seoul National University

An Overview of MIMO Technologies

for Enhanced 3GPP HSDPA

Sungjin Kim ·Hojin Kim ·Juho Lee ·Kwang Bok Lee

Lucent developed the Bell laboratories layered space-time

(BLAST) architecture, which may be categorized into vertical

BLAST (V-BLAST) and diagonal BLAST (D-BLAST) [1],

[10]. BLAST-based transmission schemes exploits spatial

multiplexing gain through different data streams on each

transmit antenna [1]. In V-BLAST, independent channel

coding is applied to each sub-layers, i.e. the data substream

corresponding to each transmit antenna. D-BLAST applies

independent coding across time as well as the antennas (sub-

layers) with high complexity. All sub-layers for both schemes

consist of the same rate code and the same order modulation,

of which the properties are modified in the enhanced schemes.

Based on the above basic MIMO technologies, a lot of

hybrid methods have been brought up for higher performance

gain. Most of them are some mixture of the basic MIMO

algorithms mentioned above. On the other hand, MIMO can

be also separated into two structures which are open-loop

(OL) and closed-loop (CL) architectures. In open-loop

MIMO (OL-MIMO), the transmitter has no channel

information for data transmissions, and hence fixed

transmit parameters are used. On the other hand, closed-

loop MIMO (CL-MIMO) exploits the channel state

information for transmissions [11], [12]. Using feedback

signaling, the transmitter adapts its parameters according

to channel conditions. The level of adaptation varies,

depending on the feedback information. CL-MIMO can be

easily combined together with adaptive modulation and

coding scheme for higher spectral efficiency. Thus, CL-

MIMO can achieve higher performance gain over OL-

MIMO at the expense of complexity. The performance of

CL-MIMO heavily depends on the channel conditions.

That is, if channel changes rather fast, feedback signaling

might be useless because the channel information at the

transmitter cannot follow up the current channel conditions.

In this case, OL-MIMO may be preferred for better

performance. CL-MIMO can be more applicable to slow

fading environments.

Most previous MIMO schemes are based on point-to-

point communications, i.e., single-user MIMO (SU-

MIMO). For the evaluation of system performance, the

multi-user environment needs to be considered [13], [14].

SU-MIMO systems focus on link performance without

any higher layer assumptions [15]. In multi-user MIMO

(MU-MIMO) systems, all users are coordinated for

communications by considering scheduling algorithms and

QoS (quality of service) requirements of each user. In the

case of CL-MIMO with multi-user communications, the

complexity is so high and a lot of research issues are brought

up, including feedback signaling, multi-user scheduling, and

transmit/receive optimization, etc [16], [17].

Recently, the industrial organizations have proposed

their MIMO techniques in 3rd generation partnership

project (3GPP) standardizations. In 3GPP, various multi-

antenna schemes are on active discussions, especially

when combined with high speed downlink packet access

(HSDPA). In this paper, we investigate the proposed

MIMO techniques which have been agreed to be included

in 3GPP MIMO working item technical report (WI-TR).

The rest of the paper is organized as follows. In

Section II, we examine the characteristics of SU-MIMO

and MU-MIMO showing different system architectures.

Section III investigates the capacity and the BER

performance of MIMO which are the fundamental issues

for all types of multi-antenna technologies, with

introducing some basic MIMO methods in Section IV,

where OL and CL schemes are compared. In Section V,

advanced MIMO technologies are presented, especially on

the current 3GPP MIMO candidates. We describe the

hybrid scheduling scheme for MU-MIMO in Section VI,

where system capacity can be shown to improve according

to scheduling methodologies. We conclude in Section VII.

2 Telecommunications Review·Vol. 14 No. 3·2004. 6

V-BLAST

Lucent, [29]

S-PARC

Ericsson, [26]

DSTTD-SGRC

Mitsubisi, [24]

RC-MPD

Nortel, [25]

Hybrid Scheme

MU-MIMO

R. Heath, [13]

PU2RC

Samsung&SNU

[22]

PU2RC +

PARC

Samsung&SNU

[23]

PARC

Lucent, [30]

D-STTD

TI, [25]

SU-MIMO

MU-MIMO with

space-time scheduling

Figure 1. History of MIMO technologies in 3GPP W-CDMA

An Overview of MIMO Technologies for Enhanced 3GPP HSDPA 3

II. CATEGORIZATION

MIMO schemes can be categorized into SU-MIMO

schemes and MU-MIMO schemes, as depicted in Figure 1,

which will be explained in detail in Section IV. Figure 2

illustrates one example showing both SU-MIMO system

and MU-MIMO system. SU-MIMO is a point-to-point

communication and so a single transmitter and receiver are

assumed. In this case, the coordination between antennas

is only possible for link performance. On the contrary,

MU-MIMO assumes multiple users for system design.

MU-MIMO is considered two types of scenarios, i.e.

point-to-multi-point and multi-point-to-point channel

environments. In the case of a point-to-multi-point

communications, a broadcast channel is modeled. Multiple

access channels are assumed for multi-point-to-point

communications.

III. PRELIMINARY ANALYSIS

1. System Model

In Figure 3 a broadcast MIMO system in wireless

mobile channels is illustrated, in which a radio base station

(Node B) communicates with K UEs. Each UE may have

the SIC (successive interference cancellation) receiver

structure for high performance gain and Mr receive

antennas, while the Node B has Mt transmit antennas [18].

The received signal for the kth user in a general

MIMO system may be expressed as

Esyk(t)=ˆ ---------Hk(t)T(t)x(t)+nk(t) (1)Nt

where Hk(t) is the Mr×Mt MIMO channel matrix from

the Node B to the kth UE(user equipment), T(t) is the Mt×Mt beamforming matrix at the transmitter, x(t) is the Mt×1 transmitted symbol vector, and nk(t) is a Mr×1

additive white Gaussian noise (AWGN) vector with

distribution CN(0,N0/2IMr). Es is the transmit energy of

the signal, and t is the index of the timeslot. The

appropriate scheduling and the beamforming, denoted by

matrix T(t) in (1), can be performed in Node B based on

the channel state information fed back from UEs,

especially for frequency division multiplexing (FDD)

systems. We investigate the performance of system

capacity in MIMO involving both single-user and multi-

user channels throughout this paper. In the next

subsection we present the achievable MIMO channel

capacity for single-user environment, for preliminary

understanding of the MIMO systems.

SU-MIMO

MU-MIMO

Figure 2. System configuration of SU-MIMO and MU-MIMO

antennas approaching infinity divided by the number of

antennas.

3. BER Performance

In the previous subsection, we reviewed the capacity

of MIMO channel. We now provide an analysis of the

performance of main MIMO schemes such as MLD

(maximum likelihood detection) [19], singular value

decomposition MIMO (SVD-MIMO) [2], and V-BLAST.

In the case of MLD, the transmitted symbol x(t) in (1)

is detected based on the maximum likelihood criterion as

Esx^ (t)+arg min‖yk(t)- ˆ---------Hk(t)x(t)‖2 (4)x(t) Nt

In [19], the detection error probability of the MLD at tth

time slot is closely approximated as

SNRpe(t)≈Q (ˆ2 -----------------χ2

2nr(t)) (5)

Knt

2. Channel Capacity

The channel capacity, which can obtained by optimal

MIMO transceiver, in SU-MIMO system is given by

Mt Es pmCO, k(t)= Σ log2(1+---------------------λm(Hk(t))) (2)m=1 N0

where the power distribution factor pm is set to 1/Mtin the OL case and can be found using water-pouring

algorithm in the CL case, and λm(A) is the mth nonzero

eigenvalue of AAH.

Later, we will show the capacities given by different

MIMO structures. The ergodic capacity for OL case may

be approximately represented by [20]:

Cn,nCMt, Mt

≅C1,1+(Mt-1)·lim ---------------- (3)n→∞ n

where C1,1 is the average capacity of SISO (single-

input single-output) Rayleigh channel, and other part is the

capacity of both the number of transmit and receive

4 Telecommunications Review·Vol. 14 No. 3·2004. 6

User

Select

AMC

AMC

Feedback

from UEs

MMSE

or

MMSE-SIC

MMSE

or

MMSE-SIC

T

K

Node-B

UE1

UEK

Figure 3. Broadcast MIMO systems

reason for this is that in such schemes the union bound is

determined by the weakest substream. We may have

further performance enhancement by using an independent

link adaptation per layer [11], [32].

Figure 4. compares the BER performance of these

MIMO schemes in the case of 2x2 MIMO channel.

IV. PROPOSED SCHEMES for 3GPP

In this section, we investigate advanced MIMO

solutions, mainly focusing on MIMO candidates in 3GPP

standardizations. In 3GPP, major industrial organizations

propose their schemes toward the official use of MIMO in

An Overview of MIMO Technologies for Enhanced 3GPP HSDPA 5

where Q(·) is the Q-function, and K is 1 or 2 representing themodulation order of BPSK and QPSK, respectively, andχ2

2nr(t) is a chi-square distributed random variable with 2nr

degrees of freedom.

On the other hand, the upper bounds of BER

performance of both SVD-MIMO and V-BLAST (no

ordering and for high SNR region) can be equally

represented as

1 SNR χ22-(nr-nt+1)(t)pe(t)≈-------Q(ˆ2 ------------------------------------------------------------- ) (6)

nt Knt nt

and are much poorer than the union bound of MLD. The

100

10-1

10-2

10-3

10-4

10-50 5 10 15 20 25

SNR(dB)

Figure 4. MIMO BER performance: 2x2-MIMO case

BER

SVD-MMO or V-BLAST

MLD

User1

UserK

11

M

V

M

Feedback Control

Figure 5. Transmitter of PU2RC architecture

{g}

{[SINR1....SINRM]}

Packet Data

Unit

(PDU)

User/Rate

Selection

Coding

Intereaving

Mapping

Coding

Intereaving

Mapping

Unitary

Basis

Trans-

formation

future wireless communication systems. As mentioned in

the previous sections, most techniques are based on the

mixture of basic MIMO algorithms for performance

improvement. We observe the system architecture of each

candidate and performance analysis.

1. PU2RC

As in Figure 5, PU2RC (per user unitary rate control)

was proposed by Samsung and SNU [22], [23]. PU2RC

uses spatial multiplexing to transmit data streams,

simultaneously to multiple users. Thus, multiple streams

are selected for transmissions to multiple users. All other

MIMO candidates are based on the single-user

6 Telecommunications Review·Vol. 14 No. 3·2004. 6

communications, while PU2RC is focused on multi-user

environments. More specifically, the transmissions are

precoded using a unitary matrix based on the singular

value decompositions of the MIMO channel. The unitary

basic matrix V at the transmitter is given by

V=[v1(gk)v2(gk) ... vM(gk)] (7)

which is the combination of the selected unitary basis

vectors from all UEs, where the selected unitary basis

vectors are all only from one UE or partly from several

UEs while total number of the vectors is fixed as M.

Data X1

X2

X3

X4

Figure 6. Transmitter of DSTTD-SGRC architecture

1

2

3

4

Encoder

Modulator

Encoder

Modulator

STTD

Encoder

STTD

Encoder

MCS

Selection

From UE

Multi-code

spreading

Multi-code

spreading

Multi-code

spreading

Multi-code

spreading

S/P

(DMUX)

14

12

10

8

6

4

2

0-2 0 2 4 6 8 10 12 14 16 18

lor/loc(dB)

Figure 7. DSTTD-SGRC: Single-user throughput at speed=30kmph with 4-slot feedback delay

Throughput (Mbps)

DSTTD-SGRCPARC-64 level

An Overview of MIMO Technologies for Enhanced 3GPP HSDPA 7

Furthermore, such selections are made on the space-time

multi-user diversity theory for maximum capacity

achievement. Each UEs optimum unitary basis matrix can

be different from each other and also different in Node-B’

s. Likewise, PU2RC is different from PARC since it uses

transmit weight matrix to transform input data vector and

supports spatial division multiplexing to utilize code-reuse

per user concept. Moreover, PU2RC is also distinguished

from PSRC (per stream rate control) since it provides the

efficient method of transmit weight matrix to support

MIMO SDMA (spatial division multiple access) by

restricting transmit weight matrix as unitary one in case of

the closed loop MIMO multi-user diversity.

2. DSTTD with SGRC

The schema of DSTTD (double space time transmit

diversity) with SGRC (sub group rate control) is shown in

Figure 6. DSTTD with SGRC was proposed by

Mitsubishi in 3GPP [24], noting that DSTTD without

considering SGRC had been proposed by TI [xx]. On the

other hand, DSTTD was originally proposed by Texas

Instruments in 3GPP, which was compared with PARC for

system performance.

DSTTD has no feedback signaling, resulting in

capacity degradation. Thus, Mitsubishi proposes the

improved version of DSTTD which is equipped with

adaptive modulations and feedback signaling for capacity

enhancement. Figure 7 describes the system performance.

3. Multipath Diversity (RC-MPD)

MPD (multi-path diversity) was proposed by Nortel,

as shown in Figure 8 [26]. MPD also uses spatial

multiplexing with rate control on each stream, namely RC-

MPD (rate control MPD). The difference is that each

stream is transmitted from two antennas with the

spreading codes differentiated by a delay of one chip

interval. MPD also uses space-time block coding as in

DSTTD. The system performance of MPD is described for

comparison with PARC in Figure 9.

4. Selective PARC

S-PARC (Selective PARC) is an extension version of

PARC, which was proposed by Ericsson in 3GPP as

shown in Figure 10 [27]. As the number of receive

antennas increases, S-PARC adaptively selects several

antennas from which to transmit, i.e., selects the best

mode, and further selects the best subset of antennas for

the chosen mode, where mode/antenna selection achieves

the highest information rate.

Generally speaking, the selective approach transmits

from fewer than the full number of antennas when

operating at lower SNRs and/or when the number of

receive antennas is less than the number of transmit

antennas and/or when the channel is heavily dispersive. In

Data

Bits

Channel

coding/

Modulation

MCS

selection

UE

Feedback

Figure 8. Transmitter of RC-MPD architecture

Spreading

Spreading

STTD

encoder+Tc

Ant1

Ant2S1

1----- -----√2

1----- -----√2

-S2*

-S1*

S2

S/P

8 Telecommunications Review·Vol. 14 No. 3·2004. 6

this way, excessive self-interference is avoided, and

diversity is obtained through both the channel and multiple

receive antennas. In S-PARC, the modulation and code

rate are assigned to the selected set of transmit antennas by

mode switching.

The system performance of S-PARC is described in

Figure 11. For instance, a 2 dB gain in SINR can be

observed at 2 Mbps. Though the gain is moderate, it is

interesting to note that the RX complexity can be reduced

since only one TX antenna is activated in this region.

5. TxAA based schemes

The Nokia proposal (closed loop MIMO with 4Tx and

2Rx) shown in Figure 12 is an extension of the closed loop

TxD used in Rel99 using receiver diversity [28]. Also,

another proposal (double TxAA) has been contributed

from LGE [29].

6. PARC - Enhanced V-BLAST

In this section, OL-MIMO and CL-MIMO are

described as 2 general schemes (i.e., V-BLAST and

30

25

20

15

10

5

01 2 3 4 5 6 7 8 9 10

Histogram:urban macro (4x4) 3km/h perfect channel

Throughput(Mb/s)

Figure 9. RC-MPD: Throughput comparison with PARC

Percentage %

PARC(4x4)

RC-MPD(4x4)

Power allocation,

Code allocation,

Queuing and traffic

information

Rx Feedback

Information

Antenna

Processor

M Active Signals

Figure 10. Transmitter of S-PARC architecture

Antenna 1

Antenna 2

Antenna N

User

Packet Data Unit

(PDU)

AMCS Decision

Controller

Encoding

Demultiplexing

Filtering

data streams are de-multiplexed to each transmit antenna

for transmission, and at the receiver the OSIC (ordered

successive interference cancellation) and MMSE

(minimum mean squared error) are used.

PARC (per antenna rate control) is a CL-MIMO as an

PARC, respectively) [31], [32]. When there is no channel

information available at the transmitter, the equal transmit

power is assigned for each antenna for optimal

performance. In Figure 13 (a), the transmitter and receiver

of V-BLAST architecture are described, where multiple

An Overview of MIMO Technologies for Enhanced 3GPP HSDPA 9

25

20

15

10

5

0-20 -10 0 10 20 30 40

4x2,3GPPTU Channel, 30% Pilot/Voice Power

lor/loc(dB)

Figure 11. S-PARC: Single-user throughput at speed=30kmph for 4x2 system on TU channel

Average Rate (Mbps)

S-PARC

PARC

CR-BLAST

RxDiv

SISO

Coded

Modulated

data-stream

Spread/scramble

W1 Pilot-1

Pilot-2

Σ

Σ

Σ

Σ

Pilot-3

Ant-1

Ant-2

Ant-3

Ant-4

Pilot-4

Weight Generation

Feedbackinformation

Figure 12. Closed loop MIMO with 4Tx and 2Rx

W2

W3

W1 W2 W3 W4

W4

enhanced version of V-BLAST, illustrated in Figure 13 (b)

including adaptive modulation and coding block for each

antenna based on Figure 13(a) [11]. In PARC, adaptive

modulation and coding schemes are used and the

predetermined combination sets of modulation level and

coding rate are provided at the receiver and fed back to the

transmitter based on the channel conditions. The feedback

capacity is limited and the perfect information of channel

information requires heavy loading of feedback signaling,

and hence the reduced set of feedback signaling

information is generated for limited feedback channel

capacity.

In Figure 14, we show the comparison of system

capacity in V-BLAST and PARC with different reception

schemes.

•V-BLAST with MMSE-OSIC, with MMSE-SIC, with

MMSE only•PARC with MMSE-SIC, with ZF-SIC, with MMSE

only and ZF only

where MMSE and ZF abbreviate the filtering using the

minimum mean square error criterion and Zero-forcing

criterion, respectively, and SIC and O-SIC abbreviate the

decoding process using the successive interference

cancellation procedure and the ordered-SIC, respectively.

For simplicity, we assume the antenna configuration

considering 4 transmit and 4 receive antennas in this

10 TELECOMMUNICATIONS REVIEW·제10권 6호·2000. 11~12월

Coding Interleave Demux

ANT1

ANT2

w1

w10

w1

w10

(a) V-BLAST (e.g. 2 Tx antennas)

Equal transmission

rates on each antenna

Packet

Data Unit

(PDU)

Coding

Interleaving

Mapping

Scrambling

Code

Scrambling

Code

Antenna 1

AntennaT

Coding

Interleaving

Mapping

Spreading Code 1

Spreading Code 2

Spreading Code C

(b) PARC

Figure 13. Transmitter of Basis MIMO schemes

DEMUX

analysis.

We investigate the instant MIMO capacities using the

distribution of SNR for each transmit antenna based on the

tight lower approximation described in [21]. The capacity

of PARC with MMSE-SIC approaches the optimal

capacity of the open-loop MIMO channel. Moreover, as in

the [11], the capacity obtained by using ZF-SIC in PARC

also very close to the capacity of PARC with MMSE-SIC.

Thus, we describe the capacity in this case as follows:

n SNRCPARC(ZF-SIC)= Σ log2(1+-------------·χ2

2n(n)n=1 n

≈CPARC(MMSE-SIC) (8)

where χ22n(n) denoting the post-detection signal-to-noise

ratio (SNR) of nth transmit antenna is a random variable

with the 2n-order chi-square distribution. N is the number

of transmit antennas. The capacity of PARC with ZF only

noting much poorer than the capacities of the previous

cases is denoted as

n SNRCPARC(ZF)= Σ log2(1+-------------·χ2

2(n))n=1 n

≦CPARC(MMSE) (9)

in which any diversity gains are never obtainable for all

transmit antennas.

The capacities of V-BLAST cases are highly dependent

on the SIC decoding whether it is using the ordering

process or not as well as the presence of the SIC entity in

the receiver, which is inline with the results on [11]. If there

is no SIC entity and no ordering process, which is the case of

V-BLAST with ZF only, the achievable capacity can be

represented as

CBLAST(MMSE)≤CBLAST(ZF)

≥N log2(1+min χ22(n)) , (10)

n=1..N

since we have known that all streams should be

transmitted with the same modulation and coding level in

V-BLAST cases. With SIC, the capacity improves as like

as in case of PARC with ZF-SIC so that it is again

expressed as follows:

CBLAST(MMSE-SIC)≥CBLAST(ZF-SIC)

≈N log2(1+min χ22n(n)) . (11)

n=1..N

Furthermore, its capacity increase much more by using the

An Overview of MIMO Technologies for Enhanced 3GPP HSDPA 11

12

10

8

6

4

2

00 1 2 3 4 5 6 7 8 9 10

Capacity of MIMO (4x4) for PARC and V-BLAST

SNR (dB)

Figure 14. Achievable capacities of basic MIMO schemes

Capacity (bits/s/Hz)

PARC (MMSE-SIC)BLAST (MMSE-OSIC)

BLAST (MMSE-SIC)PARC (MMSE-only)BLAST (MMSE-only)

PARC (ZF-SIC)

PARC (ZF-SIC)[Theory] OL-MIMO

[Theory] PARC (ZF-SIC)

[Theory] PARC (ZF-SIC)

ordering processing for the SIC decoding, then the

capacity is bounded by

CBLAST(MMSE-OSIC)≥CBLAST(ZF-OSIC)

≈N log2(1+ min ( max χ22n(i, n))) .

n=1..N i=1..N-n+1

(12)

where χ22n(i, n) is the independent and identically

distributed chi-square random variable distributed

identical independently for nth transmit antenna.

7. TPRC for CD-SIC MIMO

Transmit power ratio control (TPRC) was proposed by

SNU & Samsung [30]. To cancel out the effect of time-

domain interference signal, the code-domain interference

canceller, e.g. the code-domain successive interference

canceller (CD-SIC), may be a good choice rather than the

time-domain one because of its good performance and

simplicity. However, it should be carefully study the

properties of a canceller, especially those of the successive

interference canceller like one in the receiver of MIMO

with SIC (successive interference cancellation) systems, to

make use of its best advantages. In practice, the successive

interference canceller may cause unbalance of the post-

detection SINRs among the outputs of the detection

stages, e.g., the output SINRs of the different code

channels.

To compromise such problems, the following features

are applied to MIMO systems

•First, the Code-Domain SIC (CD-SIC), which is named

CD-SIC MIMO, and•Second, the Code-Domain Tx Power Ratio Control

(CD-TPRC), to take full advantage of CD-SIC, which is

named TPRC for CD-SIC MIMO.

The block diagram in Figure 15 shows the basic

physical layer structure of the TPRC for CD-SIC MIMO

for the HSDPA system, which is considering proposed

both CD-SIC and CD-TPRC in MIMO systems.

8. PARC and PU2RC with Scheduling

In this subsection, multi-user MIMO scheme with

scheduling is proposed for MIMO broadcast channel

12 TELECOMMUNICATIONS REVIEW·제10권 6호·2000. 11~12월

Input

data

Serial-to-

parallel

converter

Power Allocation

d1,1C1(t)

ˆP1/M

ˆPK/M

ˆPK/M

ˆP1/M

dK,1CK(t)

d1,MC1(t)

d^ 1,1

d^ K,1

d^ 1, M

d^ K, M

#1 #1

CD-SIC

with

MIMO

detection

Parallel-

to-

serial

converter

Output

data

#M #N

dK,MCK(t)

Figure 15. Basic physical layer structure of the TPRC for CD-SIC MIMO in the HS-DSCH

dk,m : data symbol

ck(t) : spreading code

Pk: code power

Power

AllocatorFeedback

channel

(Power Ratio)

Power

Calculator

Transmitter Channel Receiver

depicted in Figure 3. In previous sections, all solutions

excluding PU2RC scheme proposed by Samsung are based

on a single-user environment and a point-to-point

communication is considered for link performance. Note

that PU2RC scheme is considering the advantages of muti-

user channels, being explained in the previous section.

Practically speaking, multi-user communications need to

be assumed in cellular networks. Thus, scheduling

methodology is also considered because all users cannot

be served at the same time due to the limited resources

(e.g., the number of antennas, transmit power, etc.).

We consider two types of scheduling methods in

MIMO systems. Both techniques are based on multi-user

diversity. One of them is called MLT (maximum link

technique), in which each user employs spatial

multiplexing, and all transmit antennas are allocated to the

selected user according to the downlink channel

conditions. The other method is called IST (independent

stream technique), where all users compete independently

for each transmit antenna, and hence a user can be

assigned zero, one, or more antennas based on the link

channel conditions.

To consider the capacity of each scheduling method,

we, hereafter, denotes the SNR of the channel from the

mth transmit antenna to the kth UE as γk,m. The MLT

scheduler chooses the user satisfying kM=arg maxk Ck(t)

and so kM,m=kM for all m. Hence, the capacity can be

written as

Mt-1

CM(t)=max Ck(t)=max Σ cf (γk,m(t)) (13)m=0

Mt-1where Ck(t)= Σ cf (γk,m(t)).

m=0

In (13), cf (t)=log(1+x). Next, the capacity of the IST for

multiuser diversity is examined, especially when linear

receivers are included, while the capacity obtained with

SIC nonlinear receivers are observed in the following

subsection. In this case, the criterion for the IST

maximizes the capacity of each transmit antenna so that

the capacity is given by

Mt-1 Mt-1CIL(t)= Σ max cf (γL,k,m(t))= Σ cf (max γL,k,m(t)) (14)

m=0 k m=0 k

where we have used the simple equality

max cf (γL,k,m(t))=(max γL,k,m(t))k

and the user is selected for each transmit antenna if kIL,m=arg maxk γk,m(t) for m=0, 1, ..., Mt-1.

Consider choosing an appropriate multiuser diversity

technique for SIC receivers between MLT and IST. For

the MIMO systems using the SIC receivers, which are

nonlinear since cancellation processes such as SIC are

nonlinear, there is an obvious multiuser diversity scheme

to select the UE to receive information at a given time. If

the number of UE is greater than one, one may allow the

Node B to allocate antennas to different users in an

independent manner and not all to one user. The Node B

must then transmit information at a small enough rate so

that not only the desired UE can decode but also all other

interfered UEs are able to cancel, and the rate is

Mt-1CIS(t)= max Σ cf (min{γS,ki ,(oi )m

(t)}) (15){k

i},{o

i} m=0 i

where γS,ki ,(oi )m(t) denotes the SNR of the channel

between (oi)m antenna in the Node B to the kth UE, {ki} i

=0..L-1 is the set of the index of users receiving data

while L denotes the total number of receiving users in a

time slot, and oi is the vector representing ordering for the

SIC processing in the ith UE. Therefore, the MLT is the

best strategy in terms of the system capacity when the SIC

receivers are involved, contrary to the case of the linear

receiver.

As in previous subsection, MLT and IST schemes are

well combined with SIC and linear receivers, respectively

[18]. In a SU-MIMO environment, the SIC receiver

outperforms the linear receiver. It is also observed that in

a multi-user MIMO communications IST with linear

receiver outperforms MLT with SIC receiver when the

number of users gets large. Thus, we propose the hybrid

scheduling scheme which switches between the above two

scheduling algorithms by measuring the user capacities of

both scheduling systems. In our hybrid scheme, the user

capacities using both scheduling methods are measured

and compared to select either one of both scheduling

methods which achieves better performance.

An Overview of MIMO Technologies for Enhanced 3GPP HSDPA 13

Therefore, the maximum rate achieved by the hybrid

scheduler is CHB(t)=max{CIL(t),CMS(t)} since it chooses

a technique providing larger capacity. The user selected by

our proposed scheduler is kHB,m=arg maxk γL,k,m(t) for

each antenna m if CIL(t)＞CMS(t), otherwise kHB,m=arg

maxk CS,k(t) for any m. In brief, it is shown that the

capacity achieved by the hybrid scheduler is always better

than the other two schemes since the hybrid scheduler

selects the larger one between the outputs of the two

schedulers.

In the hybrid scheme, the required feedback

information is the received SNRs for both the MMSE SIC

receiver and MMSE linear receiver, respectively. The

Node B then decides one scheduling scheme according to

the total user capacity, obtained by its corresponding

receiver structure. Here, the feedback information is

doubled due to using the respective received SNRs

measured by two different scheduling schemes. One idea

to cope with increasing feedback is to allow the system to

decide the switching point by the number of scheduled

users. The capacity performance of hybrid scheme, which

has been investigated in [33] using the order statistics [34],

is observed in Figure 16.

In Figure 16, the capacities achieved by the different

types of feedback information (at SNR=10dB) are

presented. We note that two schemes with scalar feedback

described in Subsection 2 approach the capacity of the

optimal vector feedback for small number of transmit

antennas. Thus, the ergodic capacity with varying K (at

SNR=10dB) is illustrated, where we compare the

capacity achieved by the proposed hybrid scheduler, CHB,

with the capacities of MLT and IST, CMS with qSIC=1

and CIL with qBF=0, respectively. qSIC indicates the level

of SIC processing, 1 for perfect SIC and 0 for linear

reception (i.e. no SIC), and qBF is the level of the

beamforming processing, 1 for beamforming with perfect

knowledge of the channel information and 0 for no

beamforming. We observe substantial improvement in the

average throughput by allowing the Node B to change the

current scheduling method by measuring the capacities of

both rules. As expected, the proposed scheduler

apparently outperforms both the MLT and the IST, for any

number of users, K.

V. PERFORMANCECOMPARISON

In this section, we compare the performance of

PU2RC with the suggested feedback structure to that of

other schemes with similar protocol conditions, as in detail

described in below. We assume the power allocation

14 TELECOMMUNICATIONS REVIEW·제10권 6호·2000. 11~12월

16

15

14

13

12

11

10

9

8

7

100 101 102

Erogodic Capacities of Different MU-MIMO(4x4) Schemes

#Users (K)

Figure 16. HBT: Multiuser throughput comparing with MLT and IST schedulers

BPS/Hz

CIL: qBF=1

CIL: qBF=0

CHB:qBF=0, qSIC=1

CMS:qSIC=1

CMS:qSIC=0

Basis MatrixSelection

1 bit

SINR

Basis Vector Selection

2 bits

Selected SINR

5 bits

Conventional part

Table 1. One example of CQI format for PU2RC scheme

CQI for each UE

Additional part

policy as in open-loop MIMO concept such as equal

power transmission for the utilized transmit antennas.

In multi-user environments, a two-step approach may

be executed as follows. For example, as described in

Table 1, we consider the following scenario for

information feedback, assuming the system with 4 Tx

antennas for Node-B and 4 Rx antennas for each UE. One

bit is used to specify the unitary basis set, denoted as g,

while totally seven bits are allocated for SINR feedback

being used for AMC decision in the Node-B, where two

bits are used to denote one appropriate basis vector among

the elements of the specified basis set maximizing the

system capacity and another five bits are for the SINR

corresponding to the selected basis vector. It is

noteworthy that the five bits are fully compatible with the

SINR feedback signalling in the R5 HSDPA specification.

We have minimized feedback burden by transmission of

index values of unitary basis set and basis vector. Table 1

shows the configuration of the feedback channel format

(for uncoded data). Similar protocol conditions for other

schemes such as PARC with 4 SINR feedback signaling

for all 4 transmit antennas, S-PARC with 2 SNR feedback

An Overview of MIMO Technologies for Enhanced 3GPP HSDPA 15

16

14

12

10

8

6

4100 101 102

Capacities of the Linear MIMO(4Tx-4Rx) Schemes at SNR=10dB

# of Users

Figure 17. The capacity of PU2RC compared with the capacities of S-PARC-1 and PARC, all with linear receiver complexity

Capacity (BPS/Hz)

PU2RC (full 4 CQI)

PU2RC-1 (1 CQ)

PARC /(full 4 CQI)

SPARC-1 (1 CQ)

16

14

12

10

8

6

4100 101 102

Capacity of MIMO(4Tx-4Rx) on SNR=10dB

# Users

Figure 18. The capacity of PU2RC compared with the capacities of S-PARC-1 and PARC, where especially a nonlinear receiver is used for PARC

BPS/Hz)

PU2RC (full FB)

PU2RC-1

PARC /(full FB)

SPARC-1

signaling for only two selected transmit antennas,

assuming open-loop power allocations.

In Figure 17 and 18, the receivers of PARC and S-

PARC-1 are assumed to be non-linear and high complex,

while a linear receiver is simply used in PU2RC. In both

figures, it is shown that PU2RC with the proposed

feedback structure outperforms other two schemes by

significant throughput gains, even having much smaller

feedback burden as shown in Figure 18. Table 2 describes

the feedback bits for 4x4 MIMO used in Figure 19.

VI. CONCLUSIONS

In this paper, we presented the advanced MIMO

technologies agreed to be included in 3GPP HSDPA

MIMO TR. We described the two categorized MIMO

schemes such as PARC, MPD, DSTTD with SGRC,

TxAA based schemes for SU-MIMO, TPRC and PU2RC

for multi-user MIMO, respectively. General information

on MIMO capacity was also overviewed and preliminary

results were examined. For future work, the efficient

resource allocation scheme is required for SU-MIMO and

MU-MIMO communications with feedback channel.

Moreover, spatial channel model (SCM) needs to be

combined together with the above MIMO schemes for fair

comparisons in real system environments, where SCM is

an official channel model agreed in 3GPP and ITU.

ACKNOWLEDGEMENTThis paper has been supported in part by the Samsung

Advanced Institute of Technology (SAIT) and in part by

the Institute of New Media & Communications, Seoul

National University (SNU). The authors would like to

thank Lab-President Seung-Yong Park and Dr. KiHo Kim

both from i-Networking Lab at SAIT and Mr. Changsoon

Park from MCL at SNU

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[2] I. E. Telatar, ''Capacity of multiantenna gaussian channels,'' tech. rep., AT&T Bell Laboratories Internal Technical Memorandum 1995, published in the

16 TELECOMMUNICATIONS REVIEW·제10권 6호·2000. 11~12월

250

200

150

100

50

01 2 3 4 5 6 7 8 9 10

Number of UEs

Figure 19. The required feedback signaling for PU2RC compared with that of S-PARC-1 and PARC, function of the number of UEs

RFB(bits)

SPARC

PU2RC (full FB)

PARC

PU2RC-1

SPARC-1

SISO

Cases

SISO

PARC

S-PARC

S-PARC-1

PU2RC(full feedback)

PU2RC-1

Amount of feedback bits(K: number of users)

5*K

4*5*K

(5+4*5)*K

(2+5)*K

(1+4*5)*K

(3+5)*K

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[14] P. Viswanath, D. N. C. Tse, and R. Laroia,''Opprotunistic bemafomring using dumb antennas,''IEEE Trans. Inform. Theory, Vol. 48, No. 6, Jun. 2002, pp. 1277-1294.

[15] D. Chizhik, F. Rashid-Farrokhi, J. Ling, A. Lozano,''Effect of antenna separation on the capacity of BLASTin correlated channels,'' IEEE Communications Letters,Vol. 4, Issue 11, Nov. 2000, pp. 337-339.

[16] S. Toumpis, A. J. Goldsmith, ''Capacity Regions for

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[17] D. Lao, J. H. Horng and J. Zhang, ''Throughput Analysis for WCDMA System with MIMO and AMC,'' in Proc. of 14th PIMRC, Beijing, China., Sep. 2003, pp. 2276-2280.

[18] M. K. Varanasi, T. Guess, ''Optimum decision feedbackmultiuser equalization with successive decoding achieves the total capacity of the Gaussian multiple-access channel,'' Ailomar Conference, 1998, pp. 1405-1409.

[19] Zhu Xu and RD Murch, ''Performance Analysis of Maximum Likelihood Detection in a MIMO Antenna System,'' IEEE Transactions on Communications ,Vol 50, No. 2, Feb. 2002, pp. 187-191.

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fading channels,'' in Proc. of the IEEE ICC 2003, May 2003, pp. 2996-3000.

[21] Ozgur Oyman, Rohit U. Nabar, Helmut Bolcskei, and Arogyaswami J. Paulraj, ''Tight lower bounds on the ergodic capacity of Rayleigh fading MIMO channels,''IEEE Globecom 2002, Nov. 2002, pp. 1172-1176.

[22] Samsung and SNU, ''Text proposal for MIMO TR25.876 V1.2.1,'' 3GPP TSG-R WG1, R1#36 (04)0256, Malaga, Spain, 16th-20th Feb. 2004.

[23] Samsung and SNU, ''PU2RC Simulation Considering SPARC and 4TxAA mode1 Signalling,'' 3GPP TSG-R WG1, R1#36 (04)0362, Malaga, Spain, 16th-20th

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[26] Nortel, ''System level simulations for RC-MPD,''3GPP TSG-R WG1, R1#36 (04)0186, Malaga, Spain,16th-20th Feb. 2004.

[27] Ericsson, ''Selective Per Antenna Rate Control (S-PARC),'' 3GPP TSG-R WG1, R1#36 (04)0307, Malaga, Spain, 16th-20th Feb. 2004.

[28] Nokia, ''Closed Loop MIMO with 4 Tx and 2 Rx antennas,'' 3GPP TSG-R WG1, R1#36 (04)0206, Malaga, Spain, 16th-20th Feb. 2004.

[29] LG, ''Double TxAA for MIMO,'' 3GPP TSG-R

An Overview of MIMO Technologies for Enhanced 3GPP HSDPA 17

WG1, R1#36 (04)0222, Malaga, Spain, 16th-20th

Feb, 2004.[30] SNU and Samsung, ''Text proposal for TPRC for CD-

SIC MIMO,'' 3GPP TSG-R WG1, R1#36 (04)04255, Malaga, Spain, 16th-20th Feb. 2004.

[31] Lucent, ''Enhancements for HSDPA using multiple antennas,'' 3GPP TSG-R WG1, R1#15 (00)1057, Berlin, Germany, 22nd-25th Aug., 2000.

[32] Lucent, ''Increasing MIMO throughput with per-antenna rate control,'' 3GPP TSG-R WG1, R1#21 (01)0879, Turin, Italy, 27th - 31st Aug., 2001.

[33] S. Kim, H. Kim, Y. Moon, and K. Lee, ''EffectiveScheduling Scheme for Multiuser MIMO Systems withTransmit Beamforming and SIC Receivers,'' Acceptedto PIMRC 2004.

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18 TELECOMMUNICATIONS REVIEW·제10권 6호·2000. 11~12월

Sungjin Kim

Sungjin (James) Kim was born in Korea in 1969. He

obtained his Bachelor and Master of Engineering degree in

Electronics and Communications Engineering from the

College of Engineering, Hanyang University, Korea in

1994 and in 2000, respectively. He is now pursuing his

Doctor of Philosophy in Electrical and Computer

Engineering from the College of Engineering, Seoul

National University. In February 1994 he joined the i-

networking Lab, Samsung Advanced Institute of

Technology, and he is now a senior member of technical

research staff. Since 1999, he has been the Editor-in-Chief

of 3GPP (WCDMA standard) Transmit Diversity TR. His

research interests include the areas of transmit diversity

(TxD), multiple-input and multiple-output (MIMO),

wireless scheduling and adaptive signal processing for

3G+/4G wireless communications.

Tel.: 82-31-280-9222

Fax.: 82-31-280-9569

Email: kimsj@mobile.snu.ac.kr

An Overview of MIMO Technologies for Enhanced 3GPP HSDPA 19

Hojin Kim

Hojin Kim was born in Korea in 1973. He obtained

his Bachelor of Science in Electrical and Computer

Engineering from Purdue University, Indiana in 1997. He

received his Master of Science from the Electrical and

Computer Engineering at the University of Florida, Florida

in 2000. In 2000, he was with LG electronics institute of

technology as a research engineer. Since 2001, he has

been a research engineer at Samsung advanced institute of

technology. His research interests include MIMO, OFDM,

Ad-hoc network, and 3GPP standardization.

E-mail: hkim73@samsung.com

Tel:+82-31-280-9222

Fax:+82-31-280-9569

Kwang Bok Lee

Kwang Bok Lee received the B.A.Sc. and M.Eng.

degrees from the University of Toronto, Toronto, Ont.,

Canada, in 1982 and 1986, respectively, and the Ph.D.

degree from McMaster University, Canada in 1990. He

was with Motorola Canada from 1982 to 1985, and

Motorola USA from 1990 to 1996 as a Senior Staff

Engineer. At Motorola, he was involved in the research

and development of wireless communication systems. He

was with Bell-Northern Research, Canada, from 1989 to

1990. In March 1996, he joined the School of Electrical

Engineering, Seoul National University, Seoul, Korea.

Currently he is an Associate Professor in the School of

Electrical Engineering. He was a Vice Chair of the School

of Electrical Engineering from 2000 to 2002. He has been

serving as a Consultant to a number of wireless industries.

Since 2003, he has been a senior member of the IEEE. His

research interests include mobile communications,

communication technique covering physical layer and

upper layer. He holds ten U.S. patents and four Korean

patents, and has a number of patents pending.

Dr. Lee was an Editor of the IEEE JOURNAL ON

SELECTED AREAS IN COMMUNICATIONS, Wireless

Series in 2001, and has been an Editor of the IEEE

TRANSACTIONS ON WIRELESS COMMUNICATIONS

since 2002. And he is a co-chair of the ICC2005 Wireless

Communication Symposium. He received the Best Paper

Award from CDMA International Conference 2000 (CIC

2000), and the Best Teacher Award in 2003 from College of

engineering, Seoul National University.

Tel:+82-31-880-8415

Fax:+82-31-880-8215

Email: klee@snu.ac.kr

Juho Lee

Juho Lee received B.S., M.S., and Ph.D. degrees from

Korea Advanced Institute of Science and Technology

(KAIST), Daejon, Republic of Korea, in 1993, 1995, and

2000, respectively, all in electrical engineering. Currently, he

is a senior engineer of Samsung Electronics Co., Republic of

Korea, working on standardization of mobile

communications. His main interests include wireless comm.,

CDMA, multicarrier modulation (such as OFDM), MIMO,

and signal processing for wireless communications.

Email: juho95.lee@samsung.com

Tel:+82-31-279-5115

Fax:+82-31-279-5130