Soft Metric Assisted Mobility Robustness Optimization in L TE Networks

Gao Hui and Peter Legg Huawei Technologies Sweden

Kista, Sweden {gaohui, peter.legg}@huawei.com

Abstract-Mobility Robustness Optimization (MRO) is a use-case

of Self-Organizing Networks (SON) for LTE that aims to optimize handover performance automatically. Conventional MRO algorithms tune handover parameters based on counts of handover failures and handover events. The algorithm requires

many handovers per cell pair (e.g. 1000) to trigger a reliable adjustment. Thus it can result in a slow adaptation to changes that improve or deteriorate hand over performance, such as network loading or UE speed. In this paper, we propose a new MRO

algorithm taking into account soft metrics, such as handover command transmission time, derived both from failed and successful handovers. The proposed algorithm provides a faster reaction and lower failure rate than the conventional method, with no deterioration in the handover count.

Keywords- LTE;handover; MRO; soft metric; SON

I. INTRODUCTION

Manual setting of handover parameters in current network systems is a time consuming task. In many cases, it is considered too costly to update the mobility parameters after the initial deployment. In Release 9 of 3GPP specifications, a number of use-cases were introduced as part of the Self-organizing Networks (SON) work item for LTE [1]. One use-case, of interest here, is called Mobility Robustness Optimization (MRO). It is expected that MRO functionality is distributed among eNBs: eNBs exchange signaling regarding handover failure events and they autonomously adjust parameters (used by their UEs) that govern the triggered measurement reporting (such as cell individual offset) and/or internal handover algorithm elements.

Previous studies [2-4] have proven that MRO can make an efficient and stable modification of handover parameters towards optimum values. One challenge of MRO algorithm design is the mechanism that triggers handover parameter adjustment. Possible methods are based on timers or counters, or some combination of the two. A conventional MRO algorithm optimizes handover parameters based on handover failures and the number of handovers. In other words, each handover provides one bit of information: handover successlhandover failure. The algorithm requires many handovers (e.g. 1000 [3]) to trigger a reliable adjustment because handover failure rates are typically small (say 1%). Thus it can result in a slow adaptation to changes that improve or deteriorate handover performance, such as network loading or UE speed. In this paper, we propose a new MRO algorithm, called Soft MRO, that takes

978-1-4673-0762-8/12/$31.00 ©2012 IEEE 1

into account soft metrics, such as handover command transmission time, derived both from failed and successful handovers. The latter primarily drive the algorithm. Soft MRO promises faster convergence to the optimum operating point because for each handover there is an indication (several bits of information) of how close the handover was to failing.

II. MOBILITY ROBUSTNESS OPTIMIZATION

In LTE a UE-assisted network directed handover (HO) procedure has been defmed [1]. The eNB configures the UE to take measurements of the strength (RSRP, Reference Symbol Received Power) of the serving cell and neighbor cells, and to send a triggered measurement report when an "entry condition" has been maintained for a duration of time equal to the TIT (Time To Trigger) [5]. In this paper, we assume Event A3 is used for intra frequency LTE handover, with an entry condition that a neighbor is "offset" dB better than the serving cell.

One of the functions of MRO is to detect and subsequently prevent connection failures that occur due to Too Early or Too Late Handovers, or Handover to Wrong Cell [1]. Additionally, incorrect handover parameter settings can negatively affect user experience and waste network resources by causing unnecessary handovers such as ping-pongs (PP). Typically, there is a trade off between handover reliability and handover frequency [6].

A. Conventional MRO algorithm

An example of a conventional MRO algorithm is given in Fig. 1. The typical handover parameter to be optimized is the HO offset which is adjusted via the cell pair specific parameter C/O [5]. In each adjustment period, the eNB collects related handover counters HO too late, HO too early and HO to wrong cell (treated as HO too early in Fig. 1). To trigger a reliable adjustment, the algorithm may need more than 1000 handovers assuming a 1 % handover failure rate target (expect 10 failures). If the handover failure rate, which is considered as the main optimization objective, is too high, MRO takes the action to update the HO offset. The adjustment direction is determined based on the comparison of the number of HO Too Late and HO Too Early, as shown in Fig. 1. For example, the HO offset is decreased if the number of HO Too Late is larger than the number of HO Too Early. When handover failure rate is low, MRO takes the action to increase HO offset to reduce the PP rate which is considered as the secondary optimization objective of MRO. Otherwise, there is no HO offset change. After the adjustment, all counters are reset for a new HO statistic period.

Decrease HO offset

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Figure l. Conventional MRO algorithm procedure.

B. Soft MRO algorithm

If we require MRO to be able to respond to environmental changes such as wrt UE speed (vehicle speed increases after clearing of an accident) or network loading (transition to the busy traffic hour), then conventional MRO may be too slow. It can only average out such perturbations and give sub-optimal performance. In these circumstances we propose to use a number of so-called "soft" metrics that could instead drive the algorithm, which we call "Soft MRO". These are metrics that are derived from successful handovers, not only from handover failures, and demonstrate some correlation with the handover reliability. Indeed operation is possible without handover failures, although this is not generally the case. Examples of soft metrics are measures of the handover command transmission time or the number of HARQ transmissions to send the handover command to the UE.

The general procedure of the Soft MRO algorithm is shown in Fig. 2. The mechanism is designed to maintain the soft metric close to a threshold value such that the handover failure rate is equal to the target value - operating at failure rates below the target is assumed to result in too many handovers. The HO offset is increased when the measured soft metric is too low and decreased when it is too high.

The algorithm gathers soft measurements from a number of handovers, typically 100-200 are sufficient to give accurate statistical measures, such as the 90 percentile value. This value is then compared to two thresholds, SMT and k. SMT, where o < k ::; 1 , and adjustments to handover parameters are then made. Parameter k introduces hysteresis to the control loop and thereby manages HO offset oscillations. The threshold SMT itself may be set empirically or by an additional "outer-loop" algorithm driven by handover failures and the number of unnecessary HOs such as PPs. A simple outer loop procedure is shown in Fig. 3. When the soft metric threshold is set too low, the algorithm may result in more HO PPs.

2

Handover statistics

Increase HO offset

Decrease HO offset

Figure 2. Soft MRO algorithm inner loop procedure.

Figure 3. Soft MRO algorithm outer loop procedure.

III. SIMULATION AND RESULTS

The simulator described in [6] has been modified to allow comparison of conventional and Soft MRO. A small area was selected to test the two algorithms as shown Fig. 4. Color shading represents the identity of the strongest cell at each geographical point. 480 UEs are assigned in the rectangular path moving with an anti-clockwise direction. Downlink interference is implicitly generated by setting transmission power on a number of PRBs selected randomly given the specific load. A summary of simulation assumptions is given in Table I.

TABLE I. SIMULATION ASSUMPTIONS

Feature Implementation

Network topography Hexagonal grid of 16x3=48 cells, with wrap around. ISO 500m

Tx power BS: 46dBm; UE: 23dBm

System bandwidth 5 MHz

Antenna pattern eNB: 30 model as described in [7] UE: Onmidirectional

Charmel model 6 paths Typical Urban (TU)

Shadowing Log-normal shadowing Mean OdB, Standard deviation 8dB

Propagation model Path loss (dB) - 128.1 + 37.6 Logi O(R), where R is distance from UE to eNB (in krn)

UE speed 20mls

EESM, Chase combined HARQ Physical layer OLIUL: 1x2 MRC

PRACH with power ram�11g RSRP measurement Measurement interval 50ms, measurement model bandwidth 7 PRBs

HO offset range Limited in range [0 6] dB with I dB granularity

LI quality model Uses a measure of SIR of the cell specific reference symbol

RLF detection by T310= Is,N310-I, N311-1 Qin = -4.8 dB Layer I of UE Qout = -7.2 dB

L2 Full MAC, RLC without segmentation

L3 All RRC signalling explicitly modelled

PP threshold 5s

MRO triggering Conventional MRO: 1000 HOs condition Soft MRO: 200 HOs Soft metric

SMT=100ms, k=O.9 threshold

We use the handover command transmission delay as the soft metric since from simulation it has been found that there is a direct correlation between this measure and the handover failure rate as shown in Fig. 5 (measured over 1000s of hand over with no environmental changes). From the figure we can see that the

Figure 4. Simulation scenario.

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90 percentile value (pale blue curve) almost has a linear relationship with handover failure rate. In the simulation, the handover command delay is set to 300ms if handover command cannot be received by UE (handover failure).

Note that there is no RLC transmission failure (we assume infmite RLC transmission) for HO command in this simulation. The failure is declared when T310 expires in UE. In this study, the soft metric is chosen as the 90 percentile value which is determined from a CDF calculated from 200 handovers.

In L TE, the network load has large impact on handover performance. High downlink load means a UE would get strong interference from neighboring cells especially when UE is in cell edge area. For handover, a UE may suffer radio link failure if the handover is too late. Usually varying load is considered as an important input for the test not only for simulation but also for lab and field tests. Here, it is interesting to see how the MRO algorithms react on system load change.

A. MRO reaction on downlink load increase

In this example, we are going to see how the MRO algorithms react on a system load increase. Fig. 6 shows the handover performance for all handovers from cell 29 to cell 31 using conventional MRO and the soft MRO algorithm. UEs are configured with 6dB HO offset initially. The blue curves show the HO offset changes after each adjustment. The red and green curves give the failure rate and PP rate, respectively. Handover failure rate of both algorithms is below the target (1 %) and there is no PingPong handover given 50% load at the beginning. At one point in time (t=200s), the system load is increased to 100% (and maintained at that value). After that, more handover failures occur due to unsuccessful transmission of the handover command. For Soft MRO, the failure rate is controlled below the target quickly within 50s. While for conventional MRO, the first reaction needs quite a long time (more than 100s) since many handovers (1000) are needed to trigger the assessment of parameter adjustment. After 3 adjustments, conventional MRO controls the handover failure rate below the target.

In Fig. 6 we can see that there are some adjustment oscillations after 300s in Soft MRO. This suggests that the k value is too large. The measured soft metric of each adjustment

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period is shown as the blue curve in Fig. 7. After the load is increased, UEs suffer high interference and need more transmission attempts to successfully receive the handover command sent by the eNB if the HO offset is not updated. This is reflected in a high measured soft metric (>800ms) as shown in Fig. 7. After several adjustments, the measured soft metric is reduced to approximately the threshold value. The subsequent oscillations mirror the oscillating HO offset value.

Fast reaction to the environmental change enables the system to recover from performance deterioration such as handover failures quickly. In Fig. 8, the total number of successful and failed handovers is compared for the two algorithms. Soft MRO gives significantly fewer handover failures (by about 60%) than conventional MRO after the load increase, without incurring deterioration in handover count.

B. MRO reaction on conservative HO parameter setting

In this example, MRO algorithms' reaction on conservative HO parameter setting is simulated. UEs are configured with OdB HO offset initially. Handovers on the road from cell 29 to cell 31 are investigated here. A small overshoot in coverage of cell 45 exists between the two cells as shown in Fig. 4 as purple shading.

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Since the initial offset is small, the UE has two handovers along this road: first from cell 29 to cell 45 and then from cell 45 to cell 31. For conventional MRO, there is only one HO offset adjustment (increase to 1 dB) for cell pair <29,45> as shown in Fig. 9 - the handover failure rate is low and there are some PPs (PP target is 0%). For soft MRO, the adjustment is triggered earlier because the measured soft metric is less than soft metric threshold. After two adjustments, the soft MRO HO offset is adjusted to 2dB (blue curve). From this point, time=80s, there is no further adjustment since the (unnecessary) handover from cell 29 to 45 is prevented by the 2 dB HO offset.

The counters for the handover event from 29 to 31 and the handover sequence 29 to 45 to 31 are plotted in Fig. 10. For conventional MRO, there are some handovers from cell 29 to cell 31 but most UEs do a double handover even after the HO offset is increased to IdB. For soft MRO, a very different behavior is apparent. After the HO offset is increased to IdB, some UEs are handover from cell 29 to cell 31 directly. Later, all UEs handover from cell 29 to cell 31 directly when HO offset is increased to 2dB.

In Fig. 11, the total number of successful and failed

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handovers is compared for the two algorithms_ Soft MRO gives fewer handover (by about 50%) than conventional MRO, without incurring a deterioration in handover failures_

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IV. CONCLUSIONS AND FUTURE WORK

In this paper, we presented a novel MRO algorithm called Soft MRO which uses soft metric measurements as a basis to trigger handover parameter adjustment. The presented algorithm uses soft metrics that are derived from successful handovers, not only from handover failures. Simulation results showed that soft MRO using the metric of handover command transmission delay reacts to environmental change faster than conventional MRO algorithm under varying load condition. Soft MRO gives fewer handover failures (by about 60%) than conventional MRO after a downlink load increase without incurring deterioration in the handover count, and helps to greatly reduce unnecessary handovers in case the HO offset is set too conservatively.

Also we found that there can be HO offset adjustment oscillations if the soft metric thresholds are not optimally set. An outer-loop procedure for soft MRO is proposed for fmding the optimal soft metric threshold, and this should be enhanced to determine the best k value. In the future, more soft metrics such as the number of HARQ transmissions will be considered as the input for soft MRO and verified together with outer-loop procedure.

V. REFERENCES

[I] 3GPP TS36.300 "E-UTRA and E-UTRAN; Overall description; Stage 2 (Release 9)", v9.3.0, December 2010.

[2] Lutz Ewe, et aI., "Base station distributed handover optimization in LIE self-organizing networks", PIMRC 2011.

[3] Koichiro Kitagawa, et aI., "A handover optimization algorithm with mobility robustness for LTE systems", PIMRC 2011.

[4] Jansen, T,. et aI., "Handover parameter optimization in LTE self-organizing networks", VTC 2010-fall.

[5] 3GPP TS36.331, "E-UTRA Radio Resource Control (RRC); Protocol specification (Release 9)", v9.2.0, March 2010.

[6] P. J. Legg et aI., "A simulation study of LTE intra-frequency handover performance", VTC 20 I O-fall, Ottawa, September 2010.

[7] 3GPP TS36.814 "Further advancements for E-UTRA physical layer aspects (Release 9)", v9.0.0, March 2010.