modifying adjacenct cells for 3g networks using detectedsetreporting

7/29/2019 Modifying Adjacenct Cells for 3G Networks Using DetectedSetReporting

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Self-optimizing Neighbor Cell List for UTRA FDD

Networks Using Detected Set Reporting

David SoldaniStrategy & Technology, Technology

Network System Research, Nokia NetworksP.O. Box 301, FIN-00045 Nokia Group, Finland

[email protected]

Ivan OreStrategy & Technology, Technology

Network System Research, Nokia NetworksP.O. Box 301, FIN-00045 Nokia Group, Finland

[email protected]

Abstract This paper examines the possibility of using detected

set reporting by mobile stations for automated neighbor cell listsperformance improvement. An efficient optimization procedureis presented. This includes a model for predicting performancesof detected cells as if they were included in the neighbor lists. Theproposed algorithm for automated optimization of neighbor listswas validated by means of trialing in a live WCDMA network.

Experimental results showed the described approach to be afeasible solution for self-optimizing 3G radio access networks. Infact, the method makes it possible to identify essential missing

neighbors and operating expenditures are dramatically reducedwith respect to walk/drive testing.

Keywords- Detected Set Reporting, DSR, Neighbor Cell List,Self-Optimization, Autonomic Communications, WCDMA

I. INTRODUCTIONThe optimization of the neighbor cell list (NCL) is one of

the most important tasks operators have to perform in order toprovide seamless mobility and satisfactory quality of end userexperience [1]. Part of this process aims at identifying potential

missing neighbors that are not defined in the current NCL(s).Typically this is done by drive/walk testing. This procedure istime consuming and needs to be repeated every time the systemdeployment scenario changes, e.g. due to variations in networktopology and/or configuration. Thus, alternative solutions,which utilize measurements made in the network elements, todrive testing, are much more desirable.

A simple method and system for neighbor cell list creationand validation was presented in [2]. In that work, we studiedautomated mechanisms to improve the adjacencies list throughidentification and addition of potential missing neighbors to the

NCL plan. The missing neighbors were analytically identifiedbased on the corresponding coordinates and antenna directions.

The main problem with this method was the length of theresulting list of potential missing neighbors (denoted as pool)and the statistical confidence on the parameter used for rankingthe cells. Therefore, due to the large pool size and rankinguncertainty, the optimization process required several iterationsto measure and evaluate the performance of all pool candidates.

In this work, we propose an enhanced procedure for intra-frequency adjacencies plan optimization, which makes use of

Detected Set Reporting(DSR) concept defined in [3] and [4].(Similar reporting criteria are also specified for 3G Long Term

Evolution (LTE) systems [5].) The DSR is an intra-frequency3GPP functionality that allows UE to report cells not defined inthe UE NCL. By collecting and processing detected cells andkey handover (HO) performance indicators, only the relevantmissing neighbors are seized and thus a short pool list created.The operating time is further reduced by predicting handovershares of the detected cells as if they were defined neighbors.(For multi-frequency scenarios, this process may be applied toeach frequency layer.)

The proposed solution assumes that the detected set reportsare triggered whenEc/N0 levels of the detected cells are close to

Ec/N0 values of cells in the active set and only soft or hardhandovers to cells defined in the UE NCL are possible.

II. DETECTED SET REPORTINGTerminals monitor three mutually exclusive categories of

cells: active set(cells in soft handover); monitored set(cells notin soft HO, but included in the UE NCL; and detected set(cellsdetected by the UE, which are neither monitored or active set.

The detected set reporting is applicable only to intra-frequencymeasurements in CELL_DCH state [3].

A terminal is supposed to identify a new detectable cell notbelonging to the monitored set within 30s, if the CPICH Ec/N0> -20 dB. (If L3 filtering is used, an additional delay can beexpected.) Whereas the maximum identification time for a cell

belonging to NCL is 800 ms [4]. This means that terminalsmoving at high speed might not identify detected cells.

The criteria for reporting detected cells are the same as theconditions defined in 3GPP for active and monitored cells. Thismeans that it is up to the network administrator to define thetriggering and reporting criteria for the UE. How to implementthe DSR for handover control is left to vendors choice [3].

In this work, we assume that a HO to detected cells is notpossible and the events used for DSR are 1A (a P-CPICH of adetected cell enters the reporting range) and 1C (a P-CPICH ofa detected cell becomes better than an active one) [3].

III. CLASSIFICATION OF DETECTED CELLSIn this paper, a basic neighboris a cell already on the NCL

defined by the operator. An undefined neighbor is a cell notincluded in the NCL of a source cell. Yet, handovers from the

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source cell to that undefined target cell are possible if the targetcell is included in the UE NCL. The UE NCL is a combinationof the NCLs of all the cells participating in soft HO (active set).A detected cellis a cell not defined in the UE NCL that is ableto trigger measurement reports (Events 1A and 1C) [1].

Detected cells may be categorized in four different classes,as shown in Figure 1, where Cell A is a source cell, Cell B atarget cell (basic neighbor), and Cell D a detected cell. (This

concept is also valid for more complex scenarios.)

Class 1 detected cells are not on the NCL of Cell A andthere are no HO attempts from Cell A. Such missing neighborsare typically detected in low traffic cells or cells with a poor

NCL, and cannot be found without DSR.

Class 2 detected cells are not on the NCL of Cell A andhandover from Cell A towards the basic neighbors are possible.These missing neighbors are very good candidates and cannot

be found without DSR.

Class 3 detected cells are defined in the UE NCL. Handoverfrom Cell A to Cell D are possible only in Area III, where CellD is defined in the NCL of Cell B, and Cell A and B are in soft

HO. The potentiality of this type of detected cells as goodneighbor depends on the size of Area I with respect to Area III.The smaller the ratio between Area I and Area III is, the lowerthe gain in adding the undefined Cell D as neighbor of Cell A.

Class 4 detected cells are defined in the NCL of Cell A orCell B, but are excluded from the UE NCL. This is the casewhere the size of the combined NCLs exceeds the maximumlength of the NCL (32). Class 4 detected cells, which providegood performance, are rare, if the HO control algorithm is welldesigned [1].

IV. OPTIMIZATION PROCEDUREThe adopted optimization procedure is illustrated in Figure

2. As shown in the figure, first the DSR is activated in the areawhere the NCL needs to be optimized. (Since DSR increasesthe signaling load in the radio network controller (RNC), it isrecommended to use this feature only when and where needed.)

Class 1

Class 2

Class 3

Area I = A D Area III

Area II = A B Area III

Area III = A B D

A

A

A

B

B

D

D

D

I

II

II

I III

Figure 1. Classification of detected cells.

1. Activate DSR, assess

neighbors performance and

create pool from detected

cells (neighbors candidates)

2. Select detected cells from

pool and update NCL

3. Assess performance and

choose best cells from pool

Deploy optimal NCL plan

Has the system

deployment

scenario

changed?

Figure 2. Optimization flow chart.

Then, the performance of the current NCL plan is measuredand a pool of neighbor candidates is created from the detectedcells. Low performing adjacencies are removed from the NCL,

as explained in [2], whereas detected cells are included in theNCLs and their performances are assessed. (Note: if the NCLlength exceeds the allowed size, rotations of detected set areneeded. The rotation process was described in [2].)

The optimal NCL plan is the one that includes the bestperforming neighbors in the pool.

The duration of the procedure depends on the number ofdetected cells, available space in the NCL list and on the trafficvolume in the measured cluster of cells, since a certain numberof samples is needed for statistical reliability [1].

The proposed algorithm and model for estimating a priorithe performance of detected cells, as explain in the following

sections, aim at identifying the essential missing neighbors atonce, i.e. without any verification of the best seized candidates.

Let us now examine the process in detail.

A. Ranking CriteriaEach cell of the NCL is ranked using the metrics presented

in [2], where indicators such as HO success ratio, HO share andEc/N0 values are combined into a main performance index. Theunnecessary neighbors are deleted and the detected cells arefiltered and rank using the following criteria.

A detected Cell D is not included in the pool list if any ofthe following conditions is satisfied.

Cell D is a Class 4 cell already defined in the NCL ofthe source Cell A. The number of HO reports triggered by Cell D in source

Cell A,NumDet(A,D), is lower than a threshold, ThNum:

( ) NumThB,ANumDet . (1)

If the reported signal level of Cell D is not high enough:

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( ) ( ) RSCPN/Ec ThD,ARSCPThD,ANE c 00 ,(2)

whereEc/N0 (A,D) andRSCP(A,D) are the averageEc/N0andRSCPlevels of detected Cell D reported in source Cell A. Allthresholds values are for the operator to set.

The filtered detected set is then ranked using the followingcost function:

)A(HOatt)D,A(NumDetW)D,A(NormNumRank += ,

(3)

whereNormNum(A,D) is the ratio between NumDet(A,D) andtotal number of detected cells reports in source Cell A, W a

parameter for the operator to tune, and HOatt(A) is the totalnumber of HO attempts from source Cell A.

In (3), WNumDet/HOattprovides an estimate of the ratiobetween Area I and Area II (or Area II plus Area III, in the caseof Class 3 cells, see Figure 1). This is the proposed model forHO share prediction. The higher the ratio in (3), the better the

performance (in terms of HO share) expected from the detectedcell in question (if included in the NCL).

Once the prediction model is tuned over a cluster of cellsrepresenting the network, where the NCL performance needs to

be improved, (3) may be used for evaluating the detected set inthe remaining part of the network. As a result, only the best

performing cells will be included in the optimal NCL plan,without any need of verifying a posteriori the performance ofthe seized candidates.

Furthermore, in the case of Class 3 cells, the above rankingcriteria make it possible to filter unnecessary undefined cells,e.g. seized using the method proposed in [2], and capture onlythe ones providing good numbers in terms of HO share andduration, as explained in Section V.

B. Optimal Neighbor Cell List PlanThe optimal NCL plan consists of the best basic neighbor

cells remaining from the initial NCL and the best detected setselected from the pool using (3). In other words, if the reliabledetected set fits into the NCL of the source cell, then all thedetected set can be included in the plan. If there is not enoughroom in the NCL, the mobile network operator may eitherchoose the best candidates using (3), or select the best detectedset from the pool by trial and error using rotations [2].

V. EXPERIMENTAL VALIDATION AND DISCUSSIONThis section presents performance results attained in a live

3G cellular network using the measurement system depicted inFigure 3. In this trial, we used an Enhanced (E) version of theAutomated NCL (ANCL) platform described in [2]. The E-ANCL supports the algorithm presented in Section IV.

The network consisted of one RNC with 400 cells. Thecells were located in the downtown of Helsinki (Finland) andsurrounding areas. The RNC supported High Speed DownlinkPacket Access (HSDPA) and inter-system HO. Inter-frequencyHO was disabled.

Signallingand countersmonitoring

RNCEANCLalgorithm

Adjacenciesrotation 4 3 1 7 4 3 7 1 9 50 7 9 % /

Fileserver usage

Help

M o u n t

8 7 7 1 6 7 7 1 1 2 1 9 8 %/us

Fil e Edit Locate Vi e w H el p

1 2 3 4 5 6 70

1 00

2 00

3 00

4 00

5 00

EDCBA

NMS

Adjacenciescreation/deletion

EANCL

ConfigurationManagement

Unit

Figure 3. Measurement setup [2].

The experimental validation consisted of two main steps:parameter settings for the detected set ranking criteria and aposteriori assessment of the selected detected cells.

A. Ranking Criteria Settings and CalibrationThe thresholds ThNum, ThEc/N0 and ThRSCPdefined in Section

IV-A used for filtering the detected cells were set to 30, -10 dBand -100 dB, respectively.

After filtering, using those values, only 84 (3.7%) detected

cells were considered useful. Out of these 84 cells, 10 (12%)

were Class 2, 64 (76%) Class 3 and 10 (12%) Class 4 (cellsdropped by HO control while combining the NCLs during softHO). No Class 1 cells were detected.

This made it possible to eliminate a priori many undefinedcells that would have been improperly included in the pool ifthe ranking criteria proposed in [2] had been used. In our case,without DSR, 2500 undefined cells should have been analyzed!

The Wvalues in (3) for Class 2 and 3 detected cells may be

derived experimentally using the method of least squares (seee.g. [6]). In this case, for a given detected cell class, the best-fit

curve of soft HO prediction, i.e. WNumDet/HOattin (3), is thecumulative distribution function (CDF) that has the minimalsum of the deviations squared (least square error) from thecorresponding CDF curve attained from the set of measuredHO share data, when the detected set in question are includedin the NCLs.

The curves attained for Class 2 and 3 detected cells aredepicted in Figure 4. The resulting Wvalues, for the cluster ofcells under test, were 0.1948 and 0.004, respectively. Equation(3) closely approximates the measured data for Class 2,whereas for Class 3 the best fitting results when the HO share

is higher than 5%.

B. Discussion of Performance ResultsThe most relevant performance results, in terms HO share,

HO success ratio, and average soft HO duration, of Class 2 andClass 3 detected cells, attained when the cells were included inthe optimal NCL plan, are reported in Table I and Table II,respectively.

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0 5 10 15 20 250

10

20

30

40

50

60

70

80

90

100

HO share (%)

Cumulativedistributionfunction(%)

HO measured (Class 2)HO predicted (Class 2)

W(Class 2) = 0.1948

HO measured (Class 2)HO predicted (Class 2)

W(Class 2) = 0.1948

HO measured (Class 3)

HO predicted (Class 3)

W(Class 3 ) = 0.004

HO measured (Class 3)

HO predicted (Class 3)

W(Class 3 ) = 0.004

Figure 4. Cumulative distribution functions of HO share related to Class 2

and Class 3 detected cells, using predicted and measured values.

Figure 5 and 7 illustrate some practical examples of Class 2and 4 detected cells, respectively. Figure 6 shows graphicallythe gains performance improvements of Class 3 detected cells(data are from Table II). (SC stands for scrambling code.)

The missing neighbors reported in Table I were identifiedonly thanks to the fact that the DSR was enabled in the RNCand supported in the NCL optimization algorithm. As shown inthe table, the HO share of three detected cells was higher than5%. The HO success ratio was satisfactory towards most of theidentified neighbors and the HO duration was on average longenough to avoid awkward situations, e.g. ping pong effects.

The cell pair 464-089 shown in Figure 5 is in the downtownof Helsinki. From the map, it is rather clear that cell 089 shouldhave been on the NCL of cell 464. The relatively lowEc/N0 and

RSCPvalues, found in the measured data, might be the reasonof the poor HO success ratio reported in Table I.

In Table II are reported the performances of detected cellsthat were also on the UE NCL during soft HO (Class 3, seeFigure 1). Hence, it was possible to measure the HO statistics

before, as undefined adjacencies, and after, when the cells wereincluded in the NCLs of the source cells in question. As can benoticed in Table II, most of the undefined cells on the current

NCL plan provided poor performances. Those are, for example,particular scenarios when Area I is much larger than Area III,see Figure 1. (The relevance of such missing neighbors may beevaluated a priori (predicted) using (3).) The corresponding

metrics measured a posteriori are reported in the same table asoptimal NCL plan, where the undefined cells were includedin the NCLs of the source cells. Performance improvementsthereof are represented graphically in Figure 6. As shown in thefigure, in 9 and 11 cell pairs out of 16, the improvement interms of HO share and HO success rate, respectively, is morethan 5 points!

TABLE I.CLASS 2DETECTED CELLS PERFORMANCES

Cell pair Optimal NCL plan

Source -Target

(SC)

HO

Share

(%)

HO

Success

Ratio

(%)

Av. HO

duration

(s)

072 -398 4.9 95.6 1.92

072- 390 1.5 78.3 1.47

280 - 412 6.8 93.4 3.07

414 - 494 6.2 89.3 3.74

083 - 061 2.0 88.8 2.07

083 - 012 2.0 88.8 1.69

454 - 89 13.8 87.2 2.27

464 - 089 3.7 83.7 4.65

Another possible application of DSR is depicted in Figure7. Here a particular detected cell (SC 129) was left out by HOcontrol while combining the NCLs of the cells participating in

soft HO.In that particular scenario, despite the long distance, the

signal propagation between the 271-129 cell pair is facilitatedby the presence of water (sea). Although the performance ofClass 4 detected cells cannot be predicted using (3), byadopting the proposed ENCL algorithm, the operator is madeaware of those potential missing neighbors, and it is then up tothe network planner to drilldown a posteriori into the metricscharacterizing the performance of the identified cells.

Figure 5. Practical example of Class 2 detected cell. The cell pair is 464(source) 089 (target, i.e. detected in this case), see Table I.

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TABLE II.CLASS 3DETECTED CELLS PERFORMANCES

Cell pair Current NCL plan Optimal NCL plan

Source -

Target

(SC)

HO

Share

(%)

HO

Success

Ratio

(%)

Av. HO

duration

(s)

HO

Share

(%)

HO

Success

Ratio

(%)

Av. HO

duration

(s)

029 - 001 12.9 90.5 2.51 15.2 93.1 3.08

104 - 106 6.0 83.1 5.76 14.8 97.7 5.94453 - 089 5.1 78.0 5.38 17.4 96.4 5.12

433 - 026 4.7 86.5 1.58 19.6 90.5 3.29

106 - 104 3.4 76.2 4.57 9.5 91.0 4.15

089 - 097 2.8 81.5 3.84 8.5 94.0 4.11

483 - 421 2.2 87.5 1.85 11.2 93.0 2.18

417 - 379 1.9 88.3 1.76 10.9 88.8 2.29

089 - 453 1.7 72.7 2.41 7.4 94.2 5.22

011 - 025 1.2 76.2 1.96 3.7 81.0 1.39

426 - 413 1.1 68.2 1.65 1.8 94.9 3.68

349 - 381 1.0 90.9 1.48 4.4 91.3 1.93

026 - 433 1.0 76.9 1.72 4.0 85.5 4.71

381 - 379 0.7 87.0 1.43 9.3 92.3 1.80

418 - 014 0.3 79.2 2.32 0.3 94.4 2.81

418 - 351 0.3 61.9 1.27 0.3 91.2 1.96

0

5

10

15

20

25

30

029-001

104-106

453-089

433-026

106-104

089-097

483-421

417-379

089-453

011-025

426-413

349-381

026-433

381-379

418-014

418-351

Source-Target (Cells pair)

Perform

anceimprovement(%) HO Share

HO Success Ratio

Figure 6. Performance improvement in terms of soft HO share and soft HO

success Ration of Class 3 detected cells, when included in the optimal plan.

VI. CONCLUSIONSAn enhanced algorithm for automated neighbor cell list

(NCL) optimization, supporting the concept of Detected SetReporting (DSR), was presented. Performances thereof wereanalyzed in a real 3GSM mobile network. Experimental datashow a clear evidence of the importance of DSR in the overallautomated neighbor cell list optimization process.

The proposed method and ranking model make it possibleto drastically reduce the number of candidates and identify onlythe key (performing) missing neighbors at once.

Figure 7. Practical example of Class 4 detected cell. The cell pair is 271(source) 129 (target, i.e. detected in this case).

By adopting the proposed solution, the need of costly driveand/or walk testing, using scanners or dedicated call generators,is remarkably reduced.

Furthermore, the solution may be included in an autonomiccontrol system for cellular networks, whose primary goal is todeliver cost reduction by relieving network planners of some ofcognitive load associated with administering complex neighborcell lists definition.

ACKNOWLEDGMENT

We would like to acknowledge the valuable contributionsand suggestions of Pekka J. Ranta, Riccardo Guerzoni, MikkoKylvj, Achim Wacker, Jose Luis Alonso Rubio, and SimonBrowne working at Nokia Networks, and Kimmo Valkealahtiof Cyberell Oy.

REFERENCES

[1] D. Soldani, M. Li and R. Cuny, (eds), QoS and QoE Management inUMTS Cellular Networks, John Wiley & Sons, June 2006, 460 pp.

[2]

R. Guerzoni, I. Ore, K. Valkealahti, D. Soldani, Automatic NeighborCell List Optimization for UTRA FDD Networks: Theoretical Approachand Experimental Validation, IWS/WPMC, Aalborg, Denmark, 2005.

[3] 3GPP TS 25.331, Radio Resourse Control protocol specification.[4] 3GPP TS 25.133, Requirements for support of radio resource manage-

ment (FDD).

[5] 3GPP TS 36.300, Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial Radio Access (E-UTRAN);Overall description; Stage 2.

[6] Stephan G. Nash and Ariela Sofer, Linear and Nonlinear Programming,McGraw-Hill Companies, Inc, New York, 1996.

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modifying adjacenct cells for 3g networks using detectedsetreporting

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