06934879

IEEE WCNC 2014 - Workshop on Self-Organizing Networks

Self-optimization of L TE Mobility State Estimation Thresholds

Jussi Turkkai,2, Tero Henttonen3, Tapani Ristaniemi2 iDepartment of Communications Engineering, Tampere University of Technology, Finland

2Department of Mathematical Information Technology, University of Jyvaskyi1i, Finland 3Renesas Mobile Europe Ltd, Helsinki, Finland

juss i. [email protected], tero. [email protected], tapani. ristaniemi@jyufi

Abstract-This paper describes an algorithm for self-optimizing Mobility State Estimation (MSE) thresholds in heterogeneous Long Term Evolution (L TE) networks using Minimization of Drive Testing (MDT) measurement traces. The algorithm is using the MDT measurements to construct statistics of reselection and handover distributions for different mobility categories to learn how network topology and UE velocities are correlating in local geographical neighborhood. The distributions are used to selfoptimize L TE Release 8 MSE thresholds by employing a standard score linear classifier. This allows simple, backward compatible and cost-efficient optimization of MSE thresholds. Moreover, such self-optimization results in a high classification accuracy of UE mobility states and decreases operator's manual parameter configuration complexity. Performance evaluation of the proposed algorithm was done by conducting extensive system simulations. The performance results indicate that in the studied sparse and dense heterogeneous networks the average classification accuracy was 72.2% and 78%, respectively.

Keywords-component; Mobility State Estimation; Minimization of Drive tests; Self-organizing Networks; Data Mining;

I. INTRODUCTION Mobility State Estimation refers to means of estimating the

user equipment (UE) velocity. This knowledge is useful in wireless radio networks as it can be used to optimize UE and network performance in many ways. Typically, network performance is optimized for low UE velocities and certain service degradation is accepted for higher velocities. For example, the 3rd Generation Partnership Project (3GPP) specifies that all L TE UEs, from low to high velocity, should support mobility, but the performance is more optimized for low UE velocities, ranging from 0 kmJh to 15 kmJh [1]. Velocities of up to 120 kmlh should be supported with high performance, and velocities from 120 kmlh to 350 km/h should be supported in terms of mobility [I]. The MSE procedure defined in 3GPP specifications for idle mode [2] and for connected mode [3] is a procedure intended for optimizing the mobility performance of UEs moving at the higher velocities. The procedure works by counting the number of handovers or reselections (NCR) the UE does during a sliding time window (TCRmax), and categorizes the UE to one of three states: Normal, Medium or High based on the MSE thresholds NCR_M and NCR H. The idea is that the more handovers or reselections the

UE does during TCRmax, the "faster" the UE is moving with regards to the cell size e.g., if UE NCR count is smaller than NCR M then UE mobility state is Normal. However, if NCR is greater than NCR_M but less than NCR_H then the UE mobility is Medium and if the NCR count is greater than the NCR H then UE is assumed be in High mobility state. The procedure uses the mobility state information to scale the UE mobility parameters i.e., idle mode reselection trigger or connected mode reporting triggers (TT7), so that the faster the UE is moving, the more quickly the reselection or the measurement reporting is triggered. The gain of MSE is two-fold. Firstly, high velocity UEs benefit shorter timers since it can reduce the number of Radio Link Failures (RLF) due to too late handovers. On the contrary, low velocity UEs benefit longer timers since it can reduce the number of unnecessary short-stay handovers.

The performance of L TE Rel-8 idle mode MSE in a homogeneous environment was studied in [4] and [5], where results indicate that the Normal mobility state can be detected reasonably easily but it is more difficult to reliably detect the differences between Medium and High mobility states. However, both papers indicate that using the MSE procedure outperforms the case where the MSE is not used in terms of mobility performance. In addition, Rel-8 MSE procedure has been evaluated recently in several 3GPP contributions during the studies conducted for Mobility Enhancements in Heterogeneous Networks [6] concluding that Rel-8 MSE procedure works in regular macro networks but in heterogeneous small cell networks i.e., HetNet, the cell densities and sizes can vary, and therefore, a principle based purely on the reselection counts leads to an inaccurate mobility state detection. This resulted in several proposals for enhanced MSE to be considered for upcoming L TE Releases [7-12].

In this paper, the prospects and constraints of the MSE enhancements in [7-13] are shortly described and a novel SelfOrganizing Networks (SON) algorithm for improving the performance of MSE in heterogeneous networks is given. The proposed algorithm is based on the novelties in [14] using the MDT measurement traces for self-optimizing Evolved NodeB (eNB) parameters for the MSE i.e., thresholds NCR_M and NCR H. Performance of the algorithm is evaluated with extensive simulation campaign. Simulated results indicate that classification accuracy of UE mobility states can be improved

978-1-4799-3086-9/14/$31.00 2014 IEEE

978-1-4799-3086-9/14/$31.00 2014 IEEE 161


in heterogeneous small cell networks by means of selfoptimization without introducing additional changes to the standards. Rest of the paper is organized as follows: Section II describes the proposed MSE Rel-8 enhancements and Section III describes the Minimization of Drive testing functionality according to the 3GPP specifications. In Section IV, the selfoptimization of mobility state estimation thresholds is described and fmally in Section V, the simulation results for algorithm performance evaluation and verification are discussed.

II. ENHANCED MSE Recent 3GPP studies indicate that the accuracy of the Rel-8

MSE procedure is decreased significantly in heterogeneous small cell networks compared with the performance in homogeneous macro networks [6]. A root cause for the performance degradation impinges in the uncertainties related to the configured MSE thresholds NCR M, NCR H and UE observed NCR count which depends on the network topology and the UE movement. In heterogeneous networks, the UE observed NCR count becomes larger compared with sparser macro networks since UE is visiting in cells more frequently, thus, increasing the probability of observing larger NCR [12]. This means that for achieving good MSE performance within the whole network, either geographically varying MSE configurations or an enhanced MSE procedure that is topology independent is required. If nothing is done and Rel-8 MSE would be used with the same parameters in every eNB, then either the number of outbound RLFs or number of unnecessary short stay handovers would increase due to the ambiguous relation between UE velocity, observed NCR count and the MSE configuration as observed in [11] in case of biased MSE thresholds. On the other hand, if MSE configuration takes into account the differences in local topologies and uses different manually configured MSE thresholds from region to region, then the network mobility performance can be improved but the configuration burden is intolerable unless parameters are configured and optimized automatically.

Abovementioned challenges resulted In several improvements for the MSE procedure to be considered for upcoming L TE Releases [7-12]. Majority of the proposed improvements were related to enhance the MSE procedure either filtering or weighting the NCR count depending on the handover type. Goal of the improvements was to ensure that UEs NCR count would be independent of the small cell density e.g., depend only on the UE velocity and overlaying macro cell topology. This would allow simple global configuration of MSE thresholds and result in a reliable and stable MSE procedure even in case that fast moving UEs are not allowed to handover to small cells. In filtering based approach i.e., Selective MSE, eNS informs UE whether or not a handover shall be counted for NCR. In [10-11], it was observed that if UEs would count only macro eNB to macro eNB (M-M) handovers and Pi co eNS to macro eNS (P-M) handovers, then MSE procedure becomes small cell independent in cases when small cell density varied from 2 to 10 cells per macro cell. Weighting-based approach resulted in a similar kind of MSE performance as analyzed in [10-11]. In weighting-based MSE, eNB signals to UE a different weight factors for different

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handover types giving higher weight for macro cell related handovers and smaller weights for handovers involving small cells [7, 9, 12]. Moreover, in [8], the weights are based on the cell types rather than handover types. Simulation results in [8] and [13] suggests that such approach can also improve the MSE performance in heterogeneous networks.

Prospects of the selective and weighting-based MSE are similar. The enhancements make MSE procedure small cell independent; can filter out load balancing handovers; and would work in dynamic small cell environment where small cells are switched on and off [11-12]. Constraint of the enhancements is that these require changes to the 3GPP specifications, and therefore, the enhanced MSE would not be backwards compatible with earlier L TE devices [11-12]. Moreover, if the MSE procedure is not UE autonomous requiring eNB assistance after every handover, then the improvements are increasing signaling loading making enhancements usable only in connected mode [11-13]. Thus, UEs in idle would not benefit from the enhancement at all. Therefore, SON algorithm for self-optimizing L TE Rel-8 MSE parameters is proposed improving the MSE in heterogeneous networks whilst being rather simple and backward compatible solution for both idle and connected mode UEs.

III. MINIMIZATION OF DRIVE TESTS L TE Release 10 Minimization of Drive Testing

functionality defines two MDT operation modes, Immediate MDT and Logged MDT [15-17]. Logged MDT measurement mode is used for gathering MDT data from UEs which are in Radio Resource Control (RRC) idle state and Immediate MDT measurements are used while UE is in RRC connected state. The immediate MDT is based on the existing RRC measurement procedure with an extension to include the available location information to the measurement reports. The most coarse location info is the serving Cell Global Identification (CGI) and in the best case the detailed location i.e., latitude and longitude, is provided by Global Navigation Satellite System (GNSS) receiver. Thus, MDT allows configuring RRC measurements in a way that Reference Signal Received Power (RSRP) and Reference Signal Received Quality (RSRQ) measurements are reported periodically from the serving cell and neighboring cells including intrafrequency, inter-frequency and inter-RAT (Radio Access Technology) with the cell identification and the available location information. In logged MDT the number of logged neighboring cells is limited by a fixed upper limit per frequency. If UE is attached to L TE network, then the UE should try to log periodically the measurements for 6 intrafrequency neighboring cells, 3 inter-frequency neighboring cells, 3 GSM neighboring cells and 3 UMTS neighboring cells [15-17].

Since the MDT measurement trace can contain periodical measurements of the time stamp, detailed location, cell identification data and radio measurements, it can be used to estimate UE velocity and count the number of reselected cells e.g., NCR as described in [14]. One way to estimate the UE velocity is to use the distance difference of two GNSS coordinate points and their corresponding measurement time difference. Thus, the MDT measurement traces can be used to


correlate UE's estimated average velocity with the reselections and handover counts. This knowledge is the base of our selfoptimization algorithm allowing to construct NCR distributions with the known velocities and to learn how these distributions behave in certain local geographical regions.

IV. SELF-CONFIGURING MSE ALGORITHM Our mobility state estimation algorithm is depicted in

Figure I describing the algorithm behavior. The framework consists of following steps:

I. MDT measurement traces are collected from UEs as specified in LTE Rel-lO onwards.

2. If UE's velocity Dkmph can be estimated from the MDT traces e.g., distance difference of two GNSS locations and the time difference between them, then the MSE data is stored to a correlation database.

3. Stored data consists of three values, NCR, predefined TCRma< and estimated average velocity Dkmph.

4. eNB uses NCR count to update the distribution Xc.t of NCR samples.

5. After collecting enough MSE data, Rel-8 MSE thresholds NCR M and NCR H are updated accordingly.

First, MSE training data is collected by gathering enough periodical MDT measurement traces which can be used to estimate UE average velocity Dkmph in certain local area e.g., a set of cells in which the UE visited during certain time period of TCRmGx. This training data is used by eNB to derive NCR count as a function of TCRmax and mobility class c i.e., c= { N, M, H}, which is determined by comparing Dkmph to predefmed velocity thresholds that are typical in the region of interest.

3) Derive NCR for targetTCR and associate it with mobility

class c according to the velocity.

4) Update distribution Xc , corresponding the NCR

-

Figure 1: SON framework for !VISE

After obtaining the NCR sample, eNB updates the corresponding NCR distribution Xc,!(!1c, (Jc) where t corresponds to used TCRma< and c corresponds to the used mobility class determined based on Dkmph. After updating the Xc,!, eNB recalculates the MSE thresholds NCR M and NCR H by using standard score criteria given as:

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z=x

-j1 , (I) 0'

where variable z is the score of the observation x assuming it belongs to normal distribution defined by mean value of /1 and standard deviation (J. The absolute score tells the distance between the observation and the distribution mean in units of the standard deviation, and therefore, it can be used testing how well the observation x fits to the target distribution. In this study, the standard score criterion is used to find NCR threshold between two adjacent NCR distributions having the same TCRmax. The threshold is found by using z-score equality given as:

NiH - f-i] f-i2 - NiH

0'] 0'2

(2)

where the threshold Nm between two distributions Xl and X2 defined by mean /11, standard deviation (Jl, mean /12 and standard deviation (J2 then becomes to:

(3)

By using (3), the threshold NCR M can be found by setting Xl distribution to correspond XN mobility class i.e., lower mean value, and X2 to correspond XM mobility class distribution i.e., higher mean value. Similarly, the threshold NCR H can be found by setting Xl and X2 to XM and XH mobility class distributions as depicted in Figure 2.

Figure 2: NCR distributions for three mobility categories In Figure 2, solid, dashed and dotted black lines illustrate

the observed probability density functions for three different NCR distributions. As depicted in Figure 2, the goal of the selfoptimization is to find a threshold between two adjacent distributions ensuring that z-score is balanced. This means that the lower distribution is used for NCR values before the threshold and the higher distribution is for the values beyond the threshold. This ensures a high classification accuracy of the MSE algorithm. It is worth noting that more sophisticated classification algorithms may exist for defming the threshold between two distributions. However, (3) is simple, easy to implement and justified for the purpose, thus chosen here.

V. PERFORMANCE EVALUATION The proposed self-optimization algorithm is evaluated by

conducting system simulations with state-of-the-art LTE Rel-


10 dynamic system simulator modeling both the downlink and the uplink in an OFDM symbol resolution with radio resource management, scheduling, mobility, handover and traffic modeling functionalities. Simulation parameters and models are based on the 3GPP simulation assumption in [6] defining i.e., used bandwidth, center frequency, propagation, slow fading and fast fading parameters. More details about the simulation parameters are shown in Table I.

TABLE I. SIMULA TION PARAMETERS

Parameter Sparse HetNet Dense HetNet

Macro layoyt Regular, ISO 500m Regular, ISO 500m

Small cells per 2 10 Macro

Antenna conf Sectored I Onmi Sectored I Onmi

Macro Propagation 12S.1 + 37.6Iog10(dkm) model Pi co Propagation 140.7 + 36.7IogI0(dkm) model

Shadowing stdey. S dB Macro eNBs 110 dB Pico eNBs

Shadowing model 20 spatial shadowing with 0.5 correlation coefficient at correlation distance of 25m.

Fast fading model Typical Urban

MaxTX Power 46 dBm 130 dBm

UE Movement Random 30, 60, 120 km/h vehicular

MDT trace length > 240s

Performance simulations are done in sparse network consisting of 2 randomly deployed small cells per macro cell (2 P/M) and dense network consisting of 10 small cells per macro cell (lOP 1M). In both cases, the macro cells and the small cells are operating on the same overlapping frequency band i.e., colocated scenario. UEs are moving randomly in the network with constant velocity of 30 km/h, 60 km/h or 120 kmlh . The UEs log MDT measurements for a time period of 240s. Each recording is split to time slots corresponding TCRmax of 120 seconds, and for each slot, the number of the cell reselections and handovers were counted to obtain the NCR samples. Cell reselections after radio link failures and all handovers except short stay handovers (cell visit shorter than 1 second) were counted for NCR. Measurement event A3 parameterization was similar to Set3 configuration in [6].

Figure 3 and Figure 4 show the simulated NCR distributions XN with green line, XM with red line, XH with blue line for Normal, Medium and High mobility UEs in sparse and dense networks in case TCRma< is 120 seconds. Figure 3 and Figure 4 indicate that in the sparse network the range of NCR values are smaller compared with the dense network distributions. Moreover, Table II shows mean and standard deviation values for the NCR distributions indicating that number of observed NCR as a function of TCRmax are different in dense and sparse networks. This suggests that one MSE configuration should not be used globally in all sites if the goal is to have a reliable MSE process because one configuration can cause mobility problems e.g., due to negatively biased thresholds as discussed in [11]. In case the values in Table II are used to calculate NCR thresholds

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based on (3), then the NCR M is 13 and NCR H is 20.7 in sparse network whereas in dense network the NCR M is 18.4 and NCR H

- -

is 29.4 for the case that TCRmax is 120s. According to the dotted vertical lines in Figure 3 and Figure 4, those thresholds are good choices for the NCR thresholds. However, the distributions in Figure 3 and Figure 4 overlap and it also means that just by counting the number of handovers or reselections, ideal classification of the mobility states (discrimination between the different distributions) cannot be achieved.

TABLE I!. DISTRIBUTION CHARACTERISTICS

Sparse HetNet Dense HetNet TeR Value

XVorma/ XJ/edium XTTigh XVofmal Xlfediflltl XTTigh

JI 10.3 16.5 24.S 14.0 24.1 35.3 120s

(J 3.7 4.7 4.6 4.4 5.7 6.3

JI 5.6 S.S 12.9 7.5 12.5 IS. I 60s

(J 2.4 3. I 3.3 2.9 3.9 4.4

JI 3.4 4.9 6.9 4.4 6.S 9.5 30,1

(J 1.7 2.1 2.3 1.9 2.6 3.1

Table III shows True Positive (TP) classification accuracies of UE's MSE estimations assuming that NCR_M and NCR_H thresholds are set according to (3) in case TCRmax is 30s, 60s or 120s. TP tells how many NCR samples were classified to belong to the correct mobility distribution Xc,t based on UE's real velocity. For example, if UE velocity is 30 kmJh, it should be classified with Normal mobility and 60 km/h and 120 kmlh UEs should be classified with Medium and High mobility, respectively. Table III indicates that highest average classification accuracy is achieved with the longest used TCRmax value. In sparse HetNet, the average TP accuracy is 72.2% whereas in dense HetNet it was 78%. In all cases, detection of Normal and High mobility tends to be good but with shorter TCRmGX values i.e., 30s and 60s, correct classification of Medium mobility becomes challenging. It was observed that with shorter TCRmax the mean values decrease as expected but the degradation in standard deviation is relatively smaller. This suggest that with shorter TCRmax values, the Xc,! distributions overlap more. This makes class discrimination more difficult. Moreover, TP classification accuracy of the High mobility UEs were 83.2% and 82.5% in sparse and dense networks in case TCRmGX is 120s. Similar accuracies were reported for weightingbased MSE in [12] where 80% of 120 kmlh mobiles were classified correctly with similar TCRmax values. This suggests that self-optimization of L TE Rel-8 MSE can result in comparable performance with the enhanced MSE without necessarily required changes to the standards.

In this paper, the performance evaluation is limited to study the classification accuracy of the self-optimization. Some network performance results i.e., RLF and handover statistics, in similar scenarios are shown for example, in [11], where it was concluded that biased thresholds (poor classification accuracy) can increase either the number of RLFs for high mobility UEs or unnecessary handovers for low mobility UEs.


TABLE III. CLASSTFTCA TTON ST A TTSTTCS

Scenario TCRm{L'l; 30kmlh 60 kmlll 120 kmlh Average

30s 76.3% 18.2% 71.9% 55.4%

Sparse HetNet 60s 66.9% 47.6% 76.9% 63.8% (2 P/M) f20s 80.7% 52.6% 83.2% 72.2%

JOs 72.5% 40.9% 610% 58.2% Dense HetNet 60s 75.0% 54.6% 72.1% 67.2%

(IOP/M) 120s 84.9% 66.6% 82.5% 78.0%

0.12

0.1

0.08

>< 0.06 Q.

0.04

0.02

O --L---------------o 13.04 20.7 30 40 50

0.09

0.08

0.07

0.06

>< 0.05 '5 Q. 0.04

0.03

0.02

0.01

Number of cell reselections

Figure 3: NCR distributions in sparse network

50 Number of cell reselections

Figure 4: NCR distributions in dense network

VI. CONCLUSION This paper described a self-optimization algorithm for

configuring Mobility State Estimation thresholds NCR M and NCR_H in heterogeneous LTE networks using Minimization of Drive Testing measurement traces. The algorithm uses MDT measurements to construct cell reselection and handover distributions for different mobility categories to learn how network topology and UE velocities are correlating in eNB's local geographical neighborhood. The distributions are used by the self-optimization algorithm to autonomously select the MSE thresholds by employing a z-score based linear classifier. Simulation results indicate that by self-optimizing the MSE thresholds in the studied network scenarios, the average

165

classification accuracy for sparse scenario is 72.2% and for dense scenario is 78%. Furthermore, the classification accuracy of the High mobility UEs was found to be similar with Weighting-based MSE enhancement. Moreover, the data that is needed to self-optimize the theresholds can be collected autonomously using L TE Rel-lO MDT functionality. Hence, the threshold optimization can be done in cost-efficient and UE backward compatible manner whereas other proposed MSE enhancements require changes to the 3GPP specifications regarding signaling and UE behavior.

VII. ACKNOWLEDGEMENTS

The authors would like to thank colleagues from Magister Solutions Ltd, Renesas Mobile and University of JyvaskyI1i for their constructive criticism, comments and support through the work.

REFERENCES

[I] 3GPP TS 25.913, "Requirements for Evolved UTRA (E-UTRA) and Evolved UTRAN (E-UTRAN)", v.9.0.0, Section 7.3, December 2009.

[2] 3GPP TS 36.304, "User Equipment (UE) procedures in idle mode", v.1 0.5.0, March 2012.

[3] 3GPP TS 36.331, Radio Resource Control (RRC) Protocol specification, v.l0.5.0, March 2012.

[4] J. Puttonen, N. Kolehmainen, T. Henttonen and J. Kaikkonen, "On Idle Mode Mobility State Detection in Evolved UTRAN", in Proc. of Information Technology: New Generations, Las Vegas, US, 2009.

[5] N. Kolehmainen, J. Puttonen, T. Henttonen and J. Kaikkonen, "Performance of Idle Mode Mobility State Detection Schemes in Evolved UTRAN", Proc. of IEEE International Symposium on Wireless Pervasive Computing, Modena, Italy, May 5-7, 2010.

[6] 3GPP TR 36.839, "Mobility enhancements in heterogeneous networks", v.lLl.O, January 2013.

[7] S. Barbera, P. H. Michaelsen, M. Saily and K. Pedersen, "Improved Mobility Performance in LTE Co-Channel HetNets Through Speed Differentiated Enhancements", in Proc. of IEEE GLOBECOM 2012, Anaheim, CA, USA, 03-07 Dec 2012.

[8] R2-121250, "Further evaluation on enhancements of mobility state estimation in HetNet"', Huawei, HiSilicon, 3GPP TSG-RAN WG2 Meeting #77bis, March 2012.

[9] R2-122370, "Mobility State Estimation Enhancements", Nokia Siemens Networks, 3GPP TSG-RAN WG2 Meeting #78, May 2012.

[10] R2-130233, "Selective Mobility State Estimation", Renesas Mobile Europe, 3GPP TSG-RAN WG2 Meeting #81, February 2013.

[I I] R2-131356, "Further Evaluation of MSE Enhancements", Renesas Mobile Europe Ltd, 3GPP TSG-RAN WG2 Meeting #81 bis, April 2013.

[12] R2-131422, "Enhanced Mobility State Estimation", Nokia Siemens Networks, 3GPP TSG-RAN WG2 Meeting #81bis, April 2013.

[13] H. Shen, K. Liu, D. Xiao and Y. He, "The Enhancements of UE Mobility State Estimation in the Het-net of the LTE-A System", in Proc. of the International Conference on ITSE-2012, Lecture Notes in Electrical Engineering Volume 210, 2013, pp 99-106.

[14] J. Turkka and J. Puttonen, 'Terminal Mobility State Detection", U.S Patent 2013/0005381 A I, January 2013.

[15] S. Hamalainen, H. Sanneck, C. Sartori, "Lte Self-Organising Networks (SON): Network Management Automation for Operational Efficiency", John Wiley and Sons, 201 I.

[16] 3GPP TR 36.805, "Study on minimization of drive-tests in Next Generation Networks", v.9.0.0, December 2009.

[17] 3GPP TS 37.320, "Radio measurement collection for Minimization of Drive Tests", v.0.7.0, June 20 I O.

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