An automatic Procedure for Neighbor Cell List Definition in Cellular Networks

1st IEEE WoWMoM Workshop on Autonomic Wireless AccesS 2007 (IWAS07) - 18 June, 2007

An automatic Procedure for Neighbor Cell List Definition in Cellular Networks

Flavio Parodi, Juan Li, Jose Pradas – Helsinki University of TechnologyMikko Kylväjä, Gordon Alford – Nokia Siemens Networks


Outline

• Introduction• Challenges of future wireless networks

• Introduction of self-management

• Neighbor Cell List (NCL) management: the impact of self-configuration

• Proposed algorithm for NCL configuration• Overview

• Coverage area approximation• Class 1 algorithms

• Class 2 algorithms

• Class 3 algorithms

• Network adaptation to the new cells• Centralized approach

• Distributed approach

• Conclusions


Introduction

• In future years, network operators will have to face an increasing number of technological challenges:

• The simultaneous operation of different technologies (GSM, (E)GPRS, WCDMA, Next Generation Mobile Network)

• The need to provide a widespread coverage with high bandwidth requirements (i.e. Home base stations)

• A tremendous increase in the number of operated network nodes

• A diversified range of services, potentially requiring seamless connectivity

• Furthermore, developed countries will be characterized by an almost saturated market, with strong competition among operators forcing a reduction in tariffs

• In such a context, management systems are a potential bottleneck for a successful network operation


Introduction

• The reduction of operating expenditures (OPEX) and management complexity are vital for network operators in the years to come

• More efficient and autonomous management systems are crucial to achieve such targets

• The application of autonomic self-management functionalities would allow for

• A reduced need for human control and intervention• The capability of adaptation in presence of new nodes (self-configuration)• The possibility of increasing performances, by optimizing network

parameters (self-optimization)• A more efficient and well-timed report, diagnosing and fixing of

malfunctions (self-testing and self-healing)

• At present, 3GPP LTE standards define the support for the the self-configuration and self-optimization functionalities [1]:

• The self-configuration takes place in the pre-operational state• The self-optimization takes place during the operational state


NCL self-configuration

• An automatic NCL self-configuration feature would allow for

• A reduction of site visits related costs

• An easier and more reliable hardware installation

• The proposed algorithm can be subdivided in two different parts• The first one aims at configuring the NCLs of a newly deployed BS

• The input of such part is the planned NCL or, if the latter is not available, a pool of network information

• Once such part of the algorithm has been performed, all the cells connected to the new BS have their own NCLs

• The second part performs the adaptation of the neighboring cells to the new node

• The information contained in the NCLs is used to notify the neighbors of the presence of the new BS

• At the end of the algorithm, the NCLs in the neighboring cells are up-to-date, i.e. they include the cells controlled by the new BS

• The algorithm is executed by a self-configuration manager, to be potentially implemented in either a centralized or a distributed fashion


Proposed algorithm: NCL configuration

• The NCL configuration is carried out separately for each one of the new cells

• The implementation reflects two alternative scenario• The NCLs were planned before the deployment

• This is likely to happen for the installation of multiple BSs

• We assume the NCLs are stored in a network database, each of them associated with the respective cell by means of an identifier

• The NCLs are not available• The self-configuration functionality calculates the NCLs on-line (i.e. during the

self-configuration process)

• This approach may be appropriate when deploying a single/few BSs

• We assume a network database contains various network information: BSs’ coordinates, antenna parameters (main lobe direction, beam width, radiation pattern), radiated power values


NCL configuration: flowchart

2. Get a list of all N neighbors i, i=1,…,N within a range R from the new cell

3. Approximate coverage areas of the new cell and of a generic cell i in the retrieved list

Add cell i to the NCL

yes

no

i = i + 1

i = N ?

4. Detect overlap: is there overlap between new cell and cell i coverage areas?

Store NCL; end

yes

no

1. Query database: is the NCL available?

noyes


NCL configuration: coverage area approximation• In case the NCL is calculated during the self-configuration process, the

coverage areas of all potential neighbors are considered

• Two cells are identified as neighbors if their coverage areas overlap

• Depending on the available amount of information, the coverage areas can be approximated in different ways, each corresponding to a specific class of algorithms:

• Class 1 only geographical information available (cell coordinates)

• Class 2 coordinates and antenna parameters available

• Class 3 coordinates, antenna parameters and power values available


Coverage Area Approximation: class 1 algorithm• Neighbors are determined based only on distance

• Coverage areas cannot be determined due to the lack of coverage information

• All potential neighbors are included in the NCL

• The range value R is critical:• If R is too small, some neighbors could be missed

• If R is too big, some cells could be included in the NCL even if they are not neighbors of the new one

R

e2 e3

e1

d2 d3

d1

a2 a3

a1

b2 b3

b1

c2 c3

c1

Cells

a2, a3, b1, b2, b3, c1, c2, c3, d1, d2, d3, e1, e2, e3

are included in cell a1 Neighbor Cell List


Coverage Area Approximation: class 2 algorithms• Cell coordinates and antenna parameters are used to approximate coverage

areas

• Depending on the available antenna parameters, different ways of approximation can be considered:

• Class 2a: only main lobe direction circle approximation

• Class 2b: main lobe direction, beam width sector approximation

• Class 2c: main lobe direction, radiation pattern pattern approximation

• Since no information on radiated power is available, the maximum coverage range r is required as an input to the algorithm

• The same value of the diameter is used to approximate all coverage areas


Coverage Area Approximation: class 2 algorithms• The detection of overlap is carried out by checking if the coverage

area perimeters of two cells intersect

• Circle approximation: the perimeters intersectif the sum of the radiuses is less than the distance between the centers

• Sector approximation: the following intersectionsare checked: between sector sides, between sector side and circumference arc, between circumference arcs

r Approx. coverage area

Approx. coverage area

r

Oa1

Oi

ir/2 r/2

ia OOd 1

a1

a2 a3

AB

Oa1 Oi

P


Coverage Area Approximation: class 2 algorithms

• Pattern approximation: the perimeter of a potential

neighbor is approximated through a series of

segments. For the new cell, the segment linking

the center and a perimeter point is considered;

if it intersects one of the perimeter segment of

the potential neighbor, the coverage

areas overlap.

• The lack of knowledge about the radiated power values does not allow to fix the value of r, which has to be set based on heuristic rules

Pij Pij+1

Pa1k

Oa1

Ik

New cell’s coverage area border

i-th cell’s coverage area border


Coverage Area Approximation: class 3 algorithms• The availability of the radiated power value allows to fix the maximum

coverage range r, i.e. the size of the coverage areas• On the coverage area border, the following equation holds:

where Ptx indicates the radiated power, G the antenna gain, AF the antenna factor, LP the path loss, SMT the sensitivity of the mobile terminal

• Fixing the mobile terminal sensitivity, the path loss value at the perimeter of the coverage area can be calculated

• The Okumura-Hata formula is used to model the link loss• Once the path loss value is known, the maximum range is calculated

solving the O-H formula with respect to r [2]:

• As far as the coverage area shape is concerned, the same methods of approximation as algorithms 2 are used (and, therefore, the same rules for overlap detection apply)

MTtx SLPAFGP )(

rhhfLP bb 10101010 log)log55.69.44(22.1log82.13log9.333.46


NCL configuration: issues

• The NCL configuration algorithm provides the NCL for a new cell• In the autonomic framework defined in [3], the presented algorithm is

used to determine the neighbors of a cell once it joins the network

• The calculated NCL may include cells which are not actual neighbors• The best NCL is the one made up by only the actual neighbors of the new

cell

• The goal of the NCL definition algorithms is not to find the best NCL, as the following optimization phase should take care of dropping the neighbors with low HO share

• If the initial NCL includes all the actual neighbors, the best NCL can be found simply by pruning the unused NCL entries [3]

• The required computational load depends on the type of approximation for the coverage area


Algorithms for the network adaptation to the new cells• After the NCL is defined and stored in the new node, the network

should react to its presence

• The neighbors of the new BS should add the new cells in their NCLs

• The network database should also be updated with the new adjacencies, in order to keep the information aligned with the actual status of the network

• The following slides present an implementation for the NCL definition and network adjustment procedure in LTE networks

• We assume the network nodes are linked through an IP network

• Two possible approaches:• Centralized: the self-configuration functionality is exploited by a specific

network element

• Distributed: the self-configuration functionality is exploited by a module within each eNB


New eNodeB

New adjacency notification

ACK/NACK

4

5

6

Network Database

Neighboring eNodeB

Database query2

3 Database answer

Self-configuration element

NCL Definitionalgorithm

NCL request1

NCL response

7 Adjacency update 8 Authentication

Network adaptation: centralized solution


Centralized solution: overview

• The new node must be able to address the self-configuration element

• Pros:• The intelligence is located in a single, defined element

• Cons:• Potential increase in the signalling to the centralized node


New eNodeBIGMP

module

Self-configuration

module

Send multicast data

IGMP Membership_report

ACK/NACK

IGMP Membership_report

4

6

7

5

Network DatabaseNeighboring eNodeB

IGMP module

Self-configuration

module

NCL Definitionalgorithm

Database query1

2 Database answer

8 Adjacency update

3 Authentication

Network adaptation: distributed solution


Distributed solution: overview

• The proposed solution allows a multicast group to be created, including all the neighbors of the new node

• Efficient communication support for self-optimization

• To support multicast, the following protocols should be supported• IGMP (Internet Group Management Protocol)

• Multicast routing protocol, e.g. DVMRP (Distance Vector Multicast Routing Protocol ), PIM (Protocol-Independent Multicast )

• Reliable multicast protocol

• Pros:• Reduced need for signalling to network management layers

• Cons:• An intelligence is needed in the nodes

• Looser control on the self-configuration process


Conclusions

• A procedure for automatically defining the NCL and updating the adjacencies accordingly in the network was defined

• The procedure is carried out by a self-configuration functionality

• The considered procedure allows for a flexible approach to self-configuration

• The NCL can be retrieved from a database or calculated online, depending on the amount of available planning data

• The implementation can follow either a centralized or a distributed approach


References

[1] 3GPP TS 36.300 v8.0.0, Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial Radio Access Network (E-UTRAN); Overall description; Stage 2

[2] J. Laiho, A. Wacker, T. Novosad, editors. Radio Network Planning and Optimisation for UMTS, 1st ed., John Wiley & Sons, 2002.

[3] D. Soldani, G. Alford, F. Parodi, M. Kylväjä. An Autonomic Framework for Self-optimizing Next Generation Mobile Netwoks. IEEE, WoWMoM IWAS, Helsinki, Finland, June 2007.

[4] R.Guerzoni, I. Ore, K.Valkealahti, D.Soldani. Automatic Neighbor Cell List Optimization for UTRA FDD Networks: Theoretical approach and Experimental Validation, IWS/WPMC, Aalborg, Denmark, 2005

[5] 3GPP TR 25.912 v7.0.0, Feasibility study for evolved Universal Terrestrial Radio Access (UTRA) and Universal Terrestrial Radio Access Network (UTRAN)

[6] 3GPP TR 25.814 v7.0.0, Physical layer aspects for evolved Universal Terrestrial Radio Access (UTRA)

[7] 3GPP TR 23.822 v1.2.3, 3GPP System Architecture Evolution: Report on Technical Options and conclusions

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An automatic Procedure for Neighbor Cell List Definition in Cellular Networks