Dynamic Neighbor Cell List Management for Handover Optimization in LTE

Yoshinori Watanabe, Yasuhiko Matsunaga, Kosei Kobayashi, Hiroto Sugahara, and Kojiro Hamabe System Platforms Research Laboratories,

NEC Corporation 1753, Shimonumabe, Nakahara-Ku, Kawasaki, Kanagawa, 211-8666, Japan

{y-watanabe@ef, y-matsunaga@bl, k-kobayashi@ew, h-sugahara@cb, k-hamabe@bq}.jp.nec.com

Abstract— Self-optimization of the neighbor cell list (NCL) is expected to improve handover performance and reduce the need for site surveys. 3GPP Long Term Evolution (LTE) has introduced automatic neighbor relation (ANR), which enables a base station to manage neighbor cells on the basis of measurements made by mobiles. Because the radio coverage changes during network operations, it is essential to immediately update the NCL to improve handover performance, especially when the number of measured neighbor cells exceeds the upper limit of NCL size. In this paper, we propose a dynamic NCL management scheme to enhance NCL convergence and alleviate missing neighbor problems. The proposed scheme gives higher priority to newly detected neighbor cells over existing cells and ensures fast and accurate NCL updates after radio coverage changes. According to the LTE network simulations, the proposed scheme provides 70% faster recovery of the average radio link failure rate due to the missing neighbors compared with the non-prioritized scheme. It was also confirmed that the duration of missing neighbors is reduced by 39% on average.

Keywords-component; neighbor cell list; Self-Organizing Network; Self-Optimization; LTE; Automatic Neighbor Relation;

I. INTRODUCTION The Self Organizing Network (SON) introduced as part of

the 3GPP Long Term Evolution (LTE) is a key driver for improving operation and maintenance (O&M). To maximize network performance, the network configuration should be optimized while taking into account local characteristics of radio propagation, user traffic and mobility. However, optimization has not been frequently applied because it typically entails a heavy workload for conducting site surveys, analyzing of the performance statistics and making decisions on the optimal parameters. SON automates these tasks by using measurements taken by network equipments. Specifically, it substitutes measurements from enhanced Node B (eNB) and user equipment (UE) for the site survey data.

The Next Generation Mobile Network (NGMN) association has suggested requirements on SON use cases [1]. Given the requirements, 3GPP has investigated one of the most important use cases, management of a neighbor cell list (NCL), and has standardized a scheme of automatic neighbor relation (ANR) in release 8 [2]. ANR makes it possible for eNB to detect the surrounding neighbor relations on the basis of UE

measurements. The neighbor relations are managed in a neighbor relation table (NRT). The NCL is an extract of the NRT and the upper size is limited to 32 cells, while the NRT has no size limitation [3]. The NCL is sent to UEs, while the NRT is not known to the UEs. How to determine a target cell for handover on the basis of the NRT and the NCL depends on the eNB vender’s design policy and the operator’s management policy. The target cells have been often limited to particular neighbor cells in order to prevent a failure of handover to a neighbor cell with a temporary good quality. The NCL provides a set of the particular neighbor cells. In this case, handover performance may deteriorate as a result of there being missing neighbors if appropriate neighbor cells are not in the NCL. On the other hand, when applying the other use cases such as mobility robustness optimization (MRO) and mobility load balancing (MLB), target neighbor cells need to be listed in the NCL, in order to be able to modify each neighbor’s cell individual offset (CIO) [3]. Therefore, NCL management is a significant task to improve handover performance in LTE.

Several NRT management schemes in LTE have been investigated. Kim et al. proposed self-configuration of the initial NRT by using eNB scanning [4]. Amirijoo et al. developed an NRT self-configuration using UE measurements [5]. They also developed an automatic scheme for resolving local cell identity conflicts. Such NRT self-configuration techniques are not directly applicable to NCL management; i.e., an eNB has to take appropriate neighbor relations from the NRT and generate a new NCL when the number of neighbor relations exceeds the size of the NCL. Soldani et al. developed a scheme for prioritizing neighbor cells using UE measurement statistics for NCL management in UMTS [6]. However, when a new cell site is added to the network, the eNB has to wait to accumulate enough statistics. Consequently, the handover performance may be temporarily degraded by missing neighbors or imperfect handover parameters.

In this paper, we propose a dynamic NCL management scheme to immediately update the NCL after a radio coverage change by combining two priorities: a priority based on the number of long-term averages of UE measurements and a priority based on an increase rate of recent UE measurements. By using this scheme, the problems of missing neighbors can be solved in a short time without making too many NCL updates. We evaluated the proposed scheme in simulations.

978-1-4244-8331-0/11/$26.00 ©2011 IEEE

II. REQUIREMENTS FOR DYNAMIC NCL MANAGEMENT In this section, we describe typical situations where missing

neighbor problems occur and show requirements for the dynamic NCL management.

A. Missing Neighbor Problems As illustrated in Figure 1, typical missing neighbor

problems that cannot be solved by the conventional scheme occur when a new eNB is added to the network in a dense urban environment.

On the upper floor of high-rise buildings, reference signals with similar strength are likely to arrive from line-of-sight distant eNBs, where UE reports a lot of neighbor candidates to the serving eNB. In such situation, the number of reported neighbor candidates often exceeds the limit of NCL size, especially when the serving eNB receives the reports from the line-of-sight distant UEs. If a new eNB is deployed around the serving eNB, it is likely that NCL of the serving eNB lacks information of new cells under the new eNB. This leads to handover failures to the new cells due to missing neighbors.

To improve handover performance, these missing neighbors should be immediately added to the NCL. However, in the described situation, it often takes a long time to add a new cell to the NCL in the operational state because we need to verify if an existing neighbor cell can be replaced without a problem. Because the short-term measurements and handover frequency statistics fluctuate largely over the geographical distribution of the UE, we cannot easily use short-term measurements and statistics to select an existing neighbor cell to be replaced with a new cell.

B. Requirements for Dynamic NCL Management To improve handover performance in the described

situation, the following requirements are of key importance:

1) Autonomous detection of radio coverage changes The radio coverage changes when a new eNB is added, an

existing eNB is deleted or fails, or the radio propagation conditions change. The eNBs must detect those changes autonomously from the UE measurement results because the O&M system does not always provide such information.

New eNB

Existing eNB(serving)

ExistingeNB

many neighbor reports from LoS distant UEs

handover failure due to the missing neighbor (new cell)

NCL

Figure 1. An example of missing neighbor problems

2) Fast convergence of NCL updates Missing neighbors often cause serious degradations in

handover performance. To minimize such degradation, the NCL must be updated as soon as possible when the radio coverage changes are detected. The fast convergence of the updates should be ensured.

3) Suppression of unnecessary of NCL updates If the NCL update is too sensitive for radio coverage

changes, continuous NCL updates may increase the processing loads on the eNB and the element management system (EMS). Additionally, other SON use cases such as MRO and MLB cannot be effective if NCL is continually being updated. Therefore, unnecessary NCL updates should be suppressed.

III. DYNAMIC NCL MANAGEMENT SCHEME We propose a dynamic NCL management scheme to meet

the requirements described in Sec. II. The scheme is illustrated in Figure 2. Neighbor candidates reported by the UE are prioritized according to the statistics of the measurement reports. The neighbors are listed in the NCL in order of decreasing priority up to the maximum length of the NCL. Consequently, the previously listed cells with low priority are deleted from the NCL while the detected cells with high priority are added to the NCL.

The priorities are computed on the basis of long-term statistics of the measurement reports to mitigate short-term fluctuations in the radio environment, such as radio propagation, user mobility, and user traffic. To update the NCL as soon as possible, the long-term statistics are iteratively updated using the latest short-term statistics of the measurement reports. When the short-term statistics cannot be measured with high confidence, the priorities are computed on the basis of information from EMS or a planning tool for radio coverage. On the other hand, an increase rate of the recent measurement reports is observed to detect changes in radio coverage. Neighbor cells with high increase rates are detected as new neighbor cells due to significant changes in radio coverage. These new neighbors are immediately added to the NCL with a higher priority than the other neighbors.

By using this scheme, the problems of missing neighbors can be quickly solved, even when eNB cannot take information

NCL Updates with Priority Control

EMS

Measurements Collection / Counting

Short-term Prioritization

Prioritization for Immediate

Additions

Long-term Prioritization

Initial Prioritization

eNB

UE

UpdatedNCL

Measurement Reports

Planning Tool

Figure 2. Dynamic NCL management scheme

of the radio coverage changes from the O&M system. The detailed procedure is described below.

A. Initial Prioritization of Neighbor Cells When starting up eNB, neighbor cells cannot be registered

to the NCL until enough UE measurements are accumulated. To set initial values to the NCL, an initial priority PCPT, i is computed for each neighbor cell i based on information from EMS or the planning tool. For example, when the eNB’s location is available and each neighbor cell is ranked in order of increasing distance from the serving cell, PCPT, i is given by

( )MaxiMax

i

MaxiCPT LR

LR

LP ≤≤⎟⎟

⎠

⎞⎜⎜⎝

⎛ −−+

= 1 ,111

2, , (1)

where LMax is the maximum length of the NCL and Ri is the neighbor rank. PCPT, i is normalized to be between 0 and 1. Generally, neighbor cells at a short distance are registered to an initial NCL when detailed information of coverage planning is not available. Equation (1) gives higher priority to a neighbor cell at a shorter distance in conformity with this manner. The initial priorities are corrected by the following procedures.

B. Prioritization with Long-term Statistics In the serving cell, UEs measure the reference signal

received power (RSRP) and reference signal received quality (RSRQ) from the serving cell, listed cells (cells listed in the NCL), and detected cells (cells detected by UE but not listed in the NCL)[7]. The neighbor candidates (listed/detected cells) are reported from the UEs to the serving eNB when the measurements meet the triggering condition for reporting, for example when the RSRQ from a neighbor candidate is a3-offset dB higher than the RSRQ from the serving cell. A short-term priority pi is computed for each reported neighbor cell i by using the following metric based on number of the measurements ni:

∑

=j j

ii

nnp , (2)

where the measurement reports are not counted when the reported RSRP is below a threshold ThRSRP (to eliminate neighbor cells with low RF quality from the handover candidates).

Each neighbor candidate i is prioritized by a long-term priority Pi, which is defined as the average of the past several priorities pi and computed as

( ) ( )10 ,1 )1()( ≤<⋅+⋅−= − γγγ ij

ij

i pPP , (3)

where the priority Pi is updated iteratively and periodically using a forgetting factor γ to reflect the recent measurements of pi in a short time. Note that a larger forgetting factor γ means that the listed cells will be more frequently deleted from the NCL when the user traffic is temporarily low; hence, we employ a small γ to prevent improper deletions of listed cells. Moreover, we introduce a novel scheme of immediate neighbor additions to mitigate the delay of neighbor additions due to the small γ, especially for detected cells.

In (3), to keep a high level of confidence in Pi, we employ the initial priority PCPT, i as Pi

(0) and keep pi zero while the number of the reports ni is below a threshold ThNum.

C. Prioritization for Immediate Additions In the long-term prioritization, when the radio coverage

changes in the operational state, Pi of a new neighbor candidate tends to be underestimated because of the averaging of pi and accumulation of existing ni. In this scheme, when ni is larger than ThNum_Imm, a priority for immediate neighbor additions PImm i is computed for each neighbor candidate i by using the following increase rate of recent measurement reports:

i

iiImm

ttnP−

= , (4)

where t is the last update time of PImm i and ti is the time of the first measurement report. Note that this scheme enables eNB to detect the radio coverage changes autonomously because (4) is composed of only parameters that the eNB can measure.

D. Dynamic NCL Updates with Priority Control Neighbor cells to be listed in the NCL are determined by

combining the two priorities, Pi and PImm i. First, the maximum (LMax – LImm) neighbor candidates are registered in the NCL in order of decreasing Pi. Then, the maximum LImm cells of the unregistered neighbor candidates are added to the NCL in order of decreasing PImm. By updating with these priorities periodically, the missing neighbor problems can be resolved in a short time without too many NCL updates. Additionally, significant neighbor cells can be quickly brought under the control of self-optimizations.

IV. PERFORMANCE EVALUATION We evaluated the performance of the NCL management

scheme in LTE network simulations. In the simulations, the network was deployed in a dense urban area, where neighbor candidates exceeding the NCL capacity limit could be observed by UEs on the upper floors of high-rise buildings. The performance was evaluated under two scenarios: NCL management for an initial network in the rollout phase and NCL management for an existing network integrated with new sites. In the following evaluations, the conventional scheme means the proposed scheme without the method for immediate neighbor additions.

A. Simulation Environment We used the network layout depicted in Figure 3, where 84

eNBs were located on the top of buildings in a 100 km2 service area and 3-sector cells were managed for each eNB. Radio propagation between each eNB and UE was represented by three-dimensional ray-tracing model combined with a statistical indoor propagation model. In the propagation model, log-normal shadowing represented only vegetation effects.

UEs were uniformly distributed in the three-dimensional service area and allowed to move along streets or within the buildings according to given probabilities. FTP data were downloaded by the UEs according to a log-normal traffic

model [8]. A radio link failure was triggered when the UE completely lost synchronization with a serving eNB during a given period. In this study, we assumed that an intra-frequency handover was triggered by event A3 (Neighbor becomes offset better than serving) using RSRQ and was only allowed to listed cells [3]. The assumptions are summarized in Table I [9].

B. Simulation Results 1) Rollout Scenario

In the rollout scenario, 84 eNBs were deployed in the simulation environment simultaneously. The initial NCLs were created by taking the distance from the serving eNBs into account. Figure 4 shows the time series of the average radio link failure rate per cell. The failure rate is only for failures caused by missing neighbors. Power approximations of the failure rates are given. The failure rate was reduced in the steady state by about 90% compared with the initial failure rate. The time at which the failure rate reached the 10% level of the initial failure rate was estimated on the basis of the power approximations for each scheme. The estimates show that the proposed scheme reduces the time from 4.9 to 1.5 hours and reaches the level at 70% shorter time than the conventional one.

TABLE I. SIMULATION ASSUMPTIONS

Parameters Values

Number of cells 252 cells (84 eNBs, 3 sectors per eNB)

Cell radius Average 300 m

Antenna height 40 ~ 60 m

Path loss from eNB to UE Outdoor: 3D ray-tracing model Indoor: Statistical model [10]

Log-normal shadowing by vegetation effect

Standard deviation = 4 dB Correlation distance = 4 m (Note: Shadowing by buildings is included in the ray-tracing simulation)

Carrier frequency / Bandwidth 2 GHz / 10 MHz

Total eNB TX power 46 dBm

eNB antenna gain / tilt 14 dBi / 15 deg

UE antenna gain 0 dBi

UE noise figure 9 dB

UE required SINR -6 dB

Time threshold for RLF 1 sec

UE ratio (Indoor / Outdoor) 80% / 20% (Vehicle: 4%)

UE speed Pedestrian: 3 km/h, Vehicle: 40 km/h

Traffic intensity Average 100 calls /hour/cell

Threshold of the number of measurement reports ThNum = 20, ThNum_Imm = 3

Threshold of RSRP (ThRSRP) -118 dBm

Forgetting factor (γ) 0.68

Max. length of NCL (LMax) 32 cells

Max. number of cells for immediate addition (LImm) 8 cells

eNB10 km

10 k

m

Figure 3. Layout of eNBs

Figure 5 shows the cumulative distribution of frequency when the additions and deletions of the same neighbor cell are iterated. We see that the conventional scheme does not cause too frequent updates due to the fact that 80% of the neighbor cells that have been registered to the NCL were added to the NCL just one time and the maximum number of the iterations is three. In contrast, the proposed scheme increases the number of the iterative additions. This is because the increase rate PImm varies as a result of short-term fluctuations in the network environment. However, the number of the iterations is less than three for 90% of the neighbor cells, and the iterations can be reduced if the maximum number of neighbor additions LImm is reduced. Therefore, it can be concluded that the proposed scheme suppresses unnecessary NCL updates as well as ensures fast convergence.

2) New Site Integration Scenario In the new site integration scenario, after the previous

scenario was applied to 72 eNBs for 19 hours, 4 new eNBs were deployed between the existing sites every hour. The initial NCLs of the existing 72 eNBs did not contain newly deployed 36 neighbor cells. Figure 6 shows the radio link failure rate in this scenario. The conventional scheme cannot reduce the failure rate immediately when the rate increases steeply after every eNB addition. In contrast, the proposed scheme reduces it to the steady-state level at the next NCL update.

Figure 7 shows the average duration of the missing neighbors for each neighbor rank, as determined from the share of handover attempts estimated using approximately ideal NCLs corrected by the proposed scheme. The duration is up to 9 hours from when the new neighbor cell was deployed. The proposed scheme reduces the duration by 39% on average compared with the conventional one. Note that the proposed scheme provides this effect through autonomous detection of new neighbors and that the duration is reduced markedly among neighbor cells with a high handover share. These features result in fast convergence of the failure rate, as shown in Figure 6, and enable significant neighbor cells to be brought under the immediate control of self-optimizations such as MRO and MLB.

Rad

io L

ink

Failu

re R

ate,

R [%

]

Figure 4. Time series of average radio link failure rate due to missing

neighbor per cell in the rollout scenario

C.D

.F.

Figure 5. Cumulative distribution functions of iterative additions of the same

neighbor cell in the rollout scenario

Rad

io L

ink

Failu

re R

ate

[%]

Figure 6. Time series of average radio link failure rate due to missing

neighbors per cell in the new site integration scenario

Dur

atio

n of

Mis

sing

Nei

ghbo

rs [h

our]

Figure 7. Average duration of the missing neighbors over versus neighbor

rank by handover share in the new site integration scenario

V. CONCLUSION We proposed a dynamic NCL management scheme to

alleviate missing neighbor problems in LTE. The proposed scheme determines neighbor cells to be registered in the NCL automatically, giving higher priority to newly detected neighbor cells over existing cells. The performance of this scheme was evaluated in LTE network simulations and compared with that of the conventional scheme. The evaluations showed that the proposed scheme provides 70% faster recovery of the average radio link failure rate due to missing neighbors in comparison with the conventional one Furthermore, the evaluations showed that the proposed scheme reduced the duration of missing neighbors by 39% on average when new neighbor cells were deployed in the operational state.

REFERENCES [1] NGMN, “NGMN Recommendation on SON and O&M Requirements,”

Dec. 2008. [2] 3GPP TS 36.300 v8.12.0: “Radio Access (E-UTRA) and Radio Access

Network (E-UTRAN); Overall description; Stage 2,” Mar. 2010. [3] 3GPP TS36.331 v.8.11.0, “Radio Access (E-UTRA); Radio Resource

Control (RRC); Protocol specification,” Sep. 2010. [4] D. Kim, B. Shin, D. Hong and J. Lim, “Self-Configuration of Neighbor

Cell List Utilizing E-UTRAN NodeB Scanning in LTE Systems,” IEEE CCNC2010, 2010.

[5] M. Amirijoo, P. Frenger, et al., “Neighbor Cell List and Measured Cell Identity Management in LTE,” IEEE NOMS2008, pp.152-159, 2008.

[6] D. Soldani and I. Ore, “Self-optimizing Neighbor Cell List for UTRA FDD Networks Using Detected Set Reporting,” IEEE VTC2007, pp.694-698, 2007.

[7] 3GPP TS36.214 v.8.7.0, “Evolved Universal Terrestrial Radio Access (E-UTRA); Physical layer – Measurements,” Sep. 2009.

[8] NGMN, “NGMN Radio Access Performance Evaluation Methodology,” 31-Jan-2008.

[9] 3GPP TR 36.814 v0.4.0: “Further Advancements for E-UTRA Physical Layer Aspects,” Feb. 2010.

[10] H. Okamoto, K. Kitao and S. Ichitsubo, “Outdoor-to-Indoor Propagation Loss Prediction in 800-MHz to 8-GHz Band for Urban Area ,” IEEE Trans. Veh. Technol, vol. 58, no. 3, pp.1059-1067, Mar. 2009.