Self-optimizing Neighbor Cell List for UTRA FDD Networks Using Detected Set Reporting

David Soldani Strategy & Technology, Technology

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

david.soldani@nokia.com

Ivan Ore Strategy & Technology, Technology

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

ivan.ore@nokia.com

Abstract— This paper examines the possibility of using detected set reporting by mobile stations for automated neighbor cell lists performance improvement. An efficient optimization procedure is presented. This includes a model for predicting performances of detected cells as if they were included in the neighbor lists. The proposed algorithm for automated optimization of neighbor lists was validated by means of trialing in a live WCDMA network. Experimental results showed the described approach to be a feasible solution for self-optimizing 3G radio access networks. In fact, the method makes it possible to identify essential missing neighbors and operating expenditures are dramatically reduced with respect to walk/drive testing.

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

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

the most important tasks operators have to perform in order to provide seamless mobility and satisfactory quality of end user experience [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 is time consuming and needs to be repeated every time the system deployment scenario changes, e.g. due to variations in network topology and/or configuration. Thus, alternative solutions, which utilize measurements made in the network elements, to drive testing, are much more desirable.

A simple method and system for neighbor cell list creation and validation was presented in [2]. In that work, we studied automated mechanisms to improve the adjacencies list through identification and addition of potential missing neighbors to the NCL plan. The missing neighbors were analytically identified based on the corresponding coordinates and antenna directions. The main problem with this method was the length of the resulting list of potential missing neighbors (denoted as pool) and the statistical confidence on the parameter used for ranking the cells. Therefore, due to the large pool size and ranking uncertainty, the optimization process required several iterations to 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-frequency 3GPP functionality that allows UE to report cells not defined in the UE NCL. By collecting and processing detected cells and key handover (HO) performance indicators, only the relevant missing neighbors are seized and thus a short pool list created. The operating time is further reduced by predicting handover shares of the detected cells as if they were defined neighbors. (For multi-frequency scenarios, this process may be applied to each frequency layer.)

The proposed solution assumes that the detected set reports are triggered when Ec/N0 levels of the detected cells are close to Ec/N0 values of cells in the active set and only soft or hard handovers to cells defined in the UE NCL are possible.

II. DETECTED SET REPORTING Terminals monitor three mutually exclusive categories of

cells: active set (cells in soft handover); monitored set (cells not in soft HO, but included in the UE NCL; and detected set (cells detected by the UE, which are neither monitored or active set. The detected set reporting is applicable only to intra-frequency measurements in CELL_DCH state [3].

A terminal is supposed to identify a new detectable cell not belonging to the monitored set within 30s, if the CPICH Ec/N0 > -20 dB. (If L3 filtering is used, an additional delay can be expected.) Whereas the maximum identification time for a cell belonging to NCL is ∼800 ms [4]. This means that terminals moving at high speed might not identify detected cells.

The criteria for reporting detected cells are the same as the conditions defined in 3GPP for active and monitored cells. This means that it is up to the network administrator to define the triggering and reporting criteria for the UE. How to implement the DSR for handover control is left to vendors’ choice [3].

In this work, we assume that a HO to detected cells is not possible and the events used for DSR are 1A (a P-CPICH of a detected cell enters the reporting range) and 1C (a P-CPICH of a detected cell becomes better than an active one) [3].

III. CLASSIFICATION OF DETECTED CELLS In this paper, a basic neighbor is a cell already on the NCL

defined by the operator. An undefined neighbor is a cell not included in the NCL of a source cell. Yet, handovers from the

1550-2252/$25.00 ©2007 IEEE 694

source cell to that undefined target cell are possible if the target cell is included in the UE NCL. The UE NCL is a combination of the NCLs of all the cells participating in soft HO (active set). A detected cell is a cell not defined in the UE NCL that is able to 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 a target 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 and there are no HO attempts from Cell A. Such missing neighbors are 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 and handover 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. Handover from Cell A to Cell D are possible only in Area III, where Cell D 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 good neighbor depends on the size of Area I with respect to Area III. The smaller the ratio between Area I and Area III is, the lower the gain in adding the undefined Cell D as neighbor of Cell A.

Class 4 detected cells are defined in the NCL of Cell A or Cell B, but are excluded from the UE NCL. This is the case where the size of the combined NCLs exceeds the maximum length of the NCL (32). Class 4 detected cells, which provide good performance, are rare, if the HO control algorithm is well designed [1].

IV. OPTIMIZATION PROCEDURE The adopted optimization procedure is illustrated in Figure

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

Class 1Class 2

Class 3

Area I = A ∩ D – Area IIIArea 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 measured and a pool of neighbor candidates is created from the detected cells. Low performing adjacencies are removed from the NCL, as explained in [2], whereas detected cells are included in the NCLs and their performances are assessed. (Note: if the NCL length exceeds the allowed size, rotations of detected set are needed. The rotation process was described in [2].)

The “optimal NCL plan” is the one that includes the best performing neighbors in the pool.

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

The proposed algorithm and model for estimating a priori the performance of detected cells, as explain in the following sections, aim at identifying the essential missing neighbors at once, i.e. without any verification of the best seized candidates.

Let us now examine the process in detail.

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

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

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

• Cell D is a Class 4 cell already defined in the NCL of the 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:

695

( ) ( ) RSCPN/Ec ThD,ARSCPThD,ANEc

≤∨≤00 , (2)

where Ec/N0 (A,D) and RSCP (A,D) are the average Ec/N0 and RSCP levels of detected Cell D reported in source Cell A. All thresholds values are for the operator to set.

The filtered detected set is then ranked using the following cost function:

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

(3)

where NormNum(A,D) is the ratio between NumDet(A,D) and total number of detected cells reports in source Cell A, W a parameter for the operator to tune, and HOatt(A) is the total number of HO attempts from source Cell A.

In (3), W⋅NumDet/HOatt provides an estimate of the ratio between Area I and Area II (or Area II plus Area III, in the case of Class 3 cells, see Figure 1). This is the proposed model for HO share prediction. The higher the ratio in (3), the better the performance (in terms of HO share) expected from the detected cell in question (if included in the NCL).

Once the prediction model is tuned over a cluster of cells representing the network, where the NCL performance needs to be improved, (3) may be used for evaluating the detected set in the 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 of the seized candidates.

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

B. Optimal Neighbor Cell List Plan The “optimal NCL plan” consists of the best basic neighbor

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

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

3G cellular network using the measurement system depicted in Figure 3. In this trial, we used an Enhanced (E) version of the Automated 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. The cells were located in the downtown of Helsinki (Finland) and surrounding areas. The RNC supported High Speed Downlink Packet Access (HSDPA) and inter-system HO. Inter-frequency HO was disabled.

Signalling and counters monitoring

RNCEANCLalgorithm

Adjacencies rotation 431 7437 1950 79% /

File server usage

Help

Mount

87 71677 1121 98%/us

Fil e Edit Locat eVi ew Hel p

1 2 3 4 5 6 70

1 00

2 00

3 00

4 00

5 00

EDCBA

NMS

Adjacenciescreation/deletion

EANCL

Configuration Management

Unit

Figure 3. Measurement setup [2].

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

A. Ranking Criteria Settings and Calibration The thresholds ThNum, ThEc/N0 and ThRSCP defined in Section

IV-A used for filtering the detected cells were set to 30, -10 dB and -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 (cells dropped by HO control while combining the NCLs during soft HO). No Class 1 cells were detected.

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

The W values in (3) for Class 2 and 3 detected cells may be derived experimentally using the method of least squares (see e.g. [6]). In this case, for a given detected cell class, the best-fit curve of soft HO prediction, i.e. W⋅NumDet/HOatt in (3), is the cumulative distribution function (CDF) that has the minimal sum of the deviations squared (least square error) from the corresponding CDF curve attained from the set of measured HO share data, when the detected set in question are included in the NCLs.

The curves attained for Class 2 and 3 detected cells are depicted in Figure 4. The resulting W values, for the cluster of cells 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 Results The most relevant performance results, in terms HO share,

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

696

0 5 10 15 20 250

10

20

30

40

50

60

70

80

90

100

HO share (%)

Cum

ulat

ive

dist

ribut

ion

func

tion

(%)

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 2 and 4 detected cells, respectively. Figure 6 shows graphically the 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 identified only thanks to the fact that the DSR was enabled in the RNC and supported in the NCL optimization algorithm. As shown in the table, the HO share of three detected cells was higher than 5%. The HO success ratio was satisfactory towards most of the identified neighbors and the HO duration was on average long enough to avoid awkward situations, e.g. ping pong effects.

The cell pair 464-089 shown in Figure 5 is in the downtown of Helsinki. From the map, it is rather clear that cell 089 should have been on the NCL of cell 464. The relatively low Ec/N0 and RSCP values, found in the measured data, might be the reason of the poor HO success ratio reported in Table I.

In Table II are reported the performances of detected cells that were also on the UE NCL during soft HO (Class 3, see Figure 1). Hence, it was possible to measure the HO statistics before, as undefined adjacencies, and after, when the cells were included in the NCLs of the source cells in question. As can be noticed 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 be evaluated a priori (predicted) using (3).) The corresponding metrics measured a posteriori are reported in the same table as “optimal NCL plan”, where the undefined cells were included in the NCLs of the source cells. Performance improvements thereof are represented graphically in Figure 6. As shown in the figure, in 9 and 11 cell pairs out of 16, the improvement in terms of HO share and HO success rate, respectively, is more than 5 points!

TABLE I. CLASS 2 DETECTED 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 Figure 7. Here a particular detected cell (SC 129) was left out by HO control 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 facilitated by the presence of water (sea). Although the performance of Class 4 detected cells cannot be predicted using (3), by adopting the proposed ENCL algorithm, the operator is made aware of those potential missing neighbors, and it is then up to the network planner to drilldown a posteriori into the metrics characterizing 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.

697

TABLE II. CLASS 3 DETECTED 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 5.94

453 - 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)

Perfo

rman

ce im

prov

emen

t (%

) HO ShareHO 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. CONCLUSIONS An enhanced algorithm for automated neighbor cell list

(NCL) optimization, supporting the concept of Detected Set Reporting (DSR), was presented. Performances thereof were analyzed in a real 3GSM mobile network. Experimental data show a clear evidence of the importance of DSR in the overall automated neighbor cell list optimization process.

The proposed method and ranking model make it possible to drastically reduce the number of candidates and identify only the 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 drive and/or walk testing, using scanners or dedicated call generators, is remarkably reduced.

Furthermore, the solution may be included in an autonomic control system for cellular networks, whose primary goal is to deliver cost reduction by relieving network planners of some of cognitive load associated with administering complex neighbor cell lists definition.

ACKNOWLEDGMENT We would like to acknowledge the valuable contributions

and suggestions of Pekka J. Ranta, Riccardo Guerzoni, Mikko Kylväjä, Achim Wacker, Jose Luis Alonso Rubio, and Simon Browne working at Nokia Networks, and Kimmo Valkealahti of Cyberell Oy.

REFERENCES [1] D. Soldani, M. Li and R. Cuny, (eds), QoS and QoE Management in

UMTS Cellular Networks, John Wiley & Sons, June 2006, 460 pp. [2] 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.

[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.

698