An Evolved Mobility Robustness Optimization Algorithm for LTE Heterogeneous Networks

Zhou Guohua and Peter Legg Huawei Technologies Sweden

Kista, Sweden {guohua.zhou, peter.legg}@huawei.com

AbstractSmall cells have been integrated into the LTE system of 3GPP (3rd Generation Partnership Project), and the mobility situation in the resulting Heterogeneous Networks (HetNet) is more challenging due to more complex cell topologies and radio channel conditions. The outdoor deployment of small cells will also typically suffer user speed variations which impact handover performance a lot. A conventional tuning of handover parameters by a Mobility Robustness Optimization (MRO) algorithm suffers limitations in this new situation. In this paper, we propose a soft-metric assisted and network controlled handover optimization mechanism, which is self adaptive to the user speed variation in a quick manner. Simulations demonstrate that the new mechanism can save many handover failures compared to the conventional algorithm.

Keywords LTE, handover, MRO, SON, Heterogeneous Networks.

I. INTRODUCTION In LTE, a backward handover (HO) mechanism is

employed in which the user equipment (UE) is configured to provide measurement reports when a neighbour cell becomes a suitable candidate for a handover [1]. The network uses several configuration parameters such as measurement report offset (aka handover margin), time to trigger (TTT) and filter coefficient K to determine the trigger time of a handover. In order to make the handover trigger more accurately and efficiently, one Self-Optimizing Network (SON) use case named Mobility Robustness Optimization (MRO) was introduced to optimize these handover parameters to improve the handover reliability and reduce unnecessary handovers [1]. MRO is the first step to evolve the handover trigger from network-wide configuration to the cell or cell pair level configuration in a self-organized way. However, in the Heterogeneous Networks (HetNet) environment [2], the cell (pair) level parameters and their optimization may still not be enough to ensure the mobility performance, for several reasons. Firstly the interference distribution in handover region is uneven, such that at the handover trigger point the channel quality differs at different parts of the border between two cells; the cell pair level parameter setting will be too conservative in one area while too aggressive in the other. Secondly, the load variation in small cells is more rapid and greater in magnitude, which makes the optimization of the parameters more difficult. Thirdly, the user speed is also varying and hard to predict in the outdoor environment.

Our previous study in [3] shows a Network Controlled Handover (NCH) mechanism can solve the first two challenges very well. The NCH can be seen as the second step to evolve the handover trigger from cell (pair) level handover trigger into

a UE level handover trigger meaning that each individual handover is timed to meet the current radio environment. However, in [3] it is shown that the optimum configuration of the NCH is dependent on the user speed, and we didnt solve the speed adaptive problem in that study. There are two main approaches to take the UE speed into account for mobility control. The first one is estimate the UE speed and group the UEs with different speed levels, then capture different parameter settings for different UE speed groups. The studies in [5] gives some UE speed estimation enhancements for the mobility enhancement purpose, and [6] also suggests transmitting UE speed information in MRO signaling. However, the drawback of this approach is the UE speed is very difficult to estimate accurately, and if the speed is also varying then historical information may not be representative of current speed. Another approach to take UE speed into account for mobility optimization for a given cell pair is to capture the speed change for most of users, such as the user speed change caused by rush hour and non-rush hour transition for the same cell. However, the traditional MRO which takes a certain number of failures or handovers to give a decision may be too slow to be used in this scenario. Our study in [4] shows the soft-metric assisted MRO (Soft-MRO) enhancement can adapt the mobility parameters much quicker and with a much smaller handover failure cost. Soft-metric measurements are gathered on all handovers and are very sensitive to the radio conditions during the handover. In this paper, we integrate the two mechanisms of Soft MRO and NCH to solve all these three HetNet mobility challenges. An evaluation in our ray tracing simulator shows significant performance gain.

II. HANDOVER AND MRO ALGORITHMS

A. Classic Handover Trigger A successful handover procedure from source cell to target

cell in LTE includes HO trigger and HO execution parts. The classic HO trigger includes:

Measurement report transmission is triggered in the UE;

Measurement report is received in the source cell and a HO is triggered by the source cell at the same time.

The handover trigger in the classic mechanism depends on the measurement report trigger timing in UE and the delivery delay of the measurement report. The A3 event measurement report trigger in UE side depends on two conditions [8]:

1. Reference Signal Received Power (RSRP, measured in dBm) of target cell j of UE k is larger than serving cell i RSRP measured by k by at least the offset value:

978-1-4673-6337-2/13/$31.00 2013 IEEE

, , , (1) 2. The condition (1) remains true during the TTT time.

B. Network Controlled Handover Trigger [3] The HO trigger mechanism in NCH comprises the

following steps (Fig. 1):

1. The eNB configures a triggered periodic A3 event with a small offset (say 1dB) and short TTT (say 0ms);

2. Once a measurement report from UE k has been received, the source eNB monitors the downlink SINR of UE k in the source cell i;

3. Once the SINR passes below a threshold value the handover preparation is triggered for the strongest target cell in the last measurement report.

Herein we use the filtered wideband CQI (Channel Quality Indicator) to reflect the downlink SINR for the HO trigger. The control plane overhead of the measurement report signaling can be managed by configuring a long periodicity (e.g. 480ms) with little performance loss if desired [3].

C. Conventional MRO algorithm The conventional MRO algorithm gathers statistics over a

certain number of handovers, such as 1000 handovers assuming a 1% handover failure rate target (we expect 10 failures on average). If the handover failure rate, which is considered as the main optimization objective, calculated over the samples is too high, MRO takes the action to update the HO parameters to adjust the HO timing.

The adjustment direction is determined based on the comparison of the number of HO Too Late and HO Too Early, as shown in Fig. 2. For example, with classical handover trigger, the 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 below the target and the unnecessary handover rate exceeds a threshold, MRO takes the action to increase the offset to reduce the unnecessary handovers which is considered as the secondary optimization objective of MRO. Otherwise, there is no change. After the adjustment, all counters are reset for a new HO statistic period.

When conventional MRO is used with the NCH triggering mechanism the timing of the handover can be advanced or retarded, in a similar fashion, by adjusting the SINR threshold.

D. Soft MRO algorithm

The general procedure of the Soft MRO algorithm is shown in Fig. 3. 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 (or SINR threshold reduced in case of NCH) when the measured soft metric is too low and decreased when it is too high. The soft metrics could be handover interruption time, handover command transmission time or number of HARQ transmissions for handover command. The idea here is that when handover execution becomes more difficult, not only does the failure rate increase, but measurements of soft attributes (such as those above) for successful handovers also change (increase for the examples given). 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 [4]. This value is then compared to two thresholds, SMT (Soft Metric Threshold) and k, and adjustments to handover parameters are then made. Also an outer-loop optimization may be added [4] to ensure the SMT is suitable to ensure a desired failure rate and unnecessary HO rate. Parameter k introduces hysteresis into the control loop.

III. SIMULATION AND RESULTS In this study we selected the Lincolns Inn Fields in central

London from our ray tracing simulator. On top of the baseline of a LTE macro cell network (using real cell sites), we added 4

Figure 3. Soft MRO procedure

Handover statistics

HO number > Threshold 1?

Failure rate > Threshold 2?

Unnecessary HO rate >

Threshold 3?

#Too late >= #Too early?

Increase offset

Decrease offset

N

NNN

YY

Y

Y

Figure 2. Conventional MRO procedure

Figure 1.Network Controlled HO trigger mechanism

small cells in the square to satisfy the capacity demand and another 4 small cells beside the surrounding streets to also boost the capacity there, as shown in Fig. 4. Cars can move on the streets and a lot of handovers between small cells happen since it is a high density small cell deployment. The color shading in Fig. 5 represents the identity of the strongest cell at each geographical point. On the roads surrounding the square, 1200 test users are moving in random clockwise or anti-clockwise direction on the white circuit. A summary of simulation assumptions is given in Table I.

TABLE I. SIMULATION ASSUMPTIONS

Feature Implementation

Network Topology Heterogeneous 12 macro cells and 8 small cells

Maximum Tx power Macro: 46dBm; Pico: 30dBm; UE:23dBm

Frequency/bandwidth 2.6GHz / 5MHz

Antenna pattern Macro: Tri-sectored directional antenna;

Small cell: Omni antenna, gain 4dBi, elevation beam width =31

Channel model 6 paths, Typical Urban (TU)

UE speed 10km/h, 20km/h

Physical layer EESM, Chase combined HARQ

DL/UL: 1x2 MRC PRACH with power ramping

RSRP measurement model

Measurement interval 50ms, measurement bandwidth 7 PRBs

L1 quality model Uses a measure of SINR of the cell specific reference symbol

RLF detection by Layer 1 of UE

T310= 1s,N310 =1, N311 =1 Qin = -4.8 dB

Qout = -7.2 dB L2 Full MAC, RLC without segmentation

L3 All RRC signaling explicitly modeled Unnecessary HO threshold 1s

MRO triggering condition

Conventional MRO: 1000 HOs Soft MRO: 100 HOs

Soft metric threshold SMT=150ms, k=0.8

X2 delay (Tx2) 30ms

Tested cases Case 1: NCH + Conventional MRO; Case 2: NCH + Soft MRO

In this study for the soft MRO algorithm, we use the HO interruption time measured in the source eNB as the soft metric, which is defined as the time difference between the initiation of the HO Command message transmission by the source cell and the successful reception of the HO Complete message by the target cell, plus a fixed delay TX2 for the target to inform the source that a handover has completed:

(2) Each handover gives a HO interruption time sample, and by gathering 100 handovers including the successes and failure handovers (for failure HO we use the RRC Re-establishment attempt time as the complete time of the past failed HO), the 90 percentile value is selected for comparison with the SMT.

The results in [3] show NCH works well in HetNet environment except that the optimum values of parameters (such as the SINR threshold) show sensitivity to the UE speed. This is not surprising since NCH is only able to establish equal radio conditions (SINR) at the beginning of each handover, and the faster UEs will experience a more rapid SINR degradation during the handover giving a higher failure rate. We expect the soft MRO can make the NCH react quickly on a speed change and then NCH plus soft MRO working together can address all the three handover challenges which we identified in LTE HetNet.

A. MRO+NCH reaction on UE speed increase In this section we compare the two algorithms reaction to a

speed variation. The UE speed changes abruptly in the simulation from 10km/h (representing severe traffic congestion, walking speed) to 20km/h (representing normal city congestion), 100s from the start time. While small cell 68 covering the square also has good coverage on the street, the shadow fading caused by trees makes the handover reliability on the street from cell 68 to cell 66 very sensitive to the UE speed. Here we use Case 1 (Conventional MRO + NCH) as the baseline and Case 2 (Soft MRO + NCH) as the proposed mechanism to compare. The initial NCH CQI threshold is 2dB, which works very well for the UE speed of 10km/h. There are

Figure 5. Simulation scenario with cell shape

Figure 4. Simulation scenario

no failures during the first 100s of the simulation, as shown in Fig. 6.

When the UE speed increases from 10km/h to 20km/h, because of the deep shadow fading of small cell 68, on the path of handover from cell 68 to 66, the source cell RSRP quickly drops causing the SINR also to quickly drop. The low speed user can survive from the SINR drop with the CQI threshold 2dB in this study, because the HO command can be delivered successfully before the SINR drops too low. While for higher speed users, although the HO trigger time should be similar to the low speed users, the SINR level during the HO command transmission period is lower.

Finally, in Fig. 6 we see the speed change gives dramatic failure rate increase for the handovers from cell 68 to 66. To react on this handover situation degradation, the soft MRO in Fig. 6 shows a much quicker adaption to the situation; in about 50s the soft MRO has already gathered sufficient soft metric samples to give two rounds of adjustment. After two rounds of adjustment (the adjust step is 0.5dB for the filtered wideband CQI threshold in NCH), the HO failure rate was controlled to be below the target. However, the conventional MRO in Case 1 needs an increase in the failure number to recognize the performance degradation, it requires many more handovers to find a accurate decision, especially when the failure rate is not extremely high. Finally in the long run time, the HO failure rate from conventional MRO keeps high, and the conventional MRO takes about 10 times longer time than the soft MRO to solve this HO too late case.

This fast recovering character of soft MRO comes from the strong mapping of soft metric and the handover difficulty. In Fig. 7 we can see when the UE speed is increased, the soft metric of HO interruption time increases sharply. Thats because almost for each handover including the successful handovers, the quicker SINR decreasing in the source cell for higher speed makes the HO command delivery more difficult and it takes longer time to accomplish the handover. This kind of HO interruption time increase can be collected by the network for each handover and actions could be triggered at once to avoid the potential following handover failures.

The benefit of the fast failure recovery is shown in Fig. 8,

when the UE speed increases, since in the coverage of cell pair 68 and 66 theres no overshoot coverage or small pocket coverage, the earlier HO parameter adjustment of soft MRO didnt cause the handover number to increase. Finally only dramatic handover failure number saving by the quick adjustment of soft MRO is seen, and about 85% failures are avoided compared to the conventional MRO of Case 1.

B. MRO+NCH reaction on conservative parameter setting For the opposite direction of MRO to reduce unnecessary

handover by increasing the HO difficulty, soft MRO is also expected to give quicker reaction than conventional MRO.

In our simulation scenario, since theres no overshoot small coverage, the ping-pong handover is not an issue. However the unnecessary handover could be those handovers from cell A to cell B succeeding and within a very short time another handover from cell B to cell C is triggered. In Fig. 5 we see the cell pair of {63, 68} has this phenomenon. With the study of initial NCH CQI threshold 3dB, the 20km/h speed users in the test circuit give a lot of unnecessary handovers at the beginning. The unnecessary handovers happen when the UE

Figure 8. HO number and failure number comparison for two cases

Figure 7. Soft metric behaviour for Case 2

Figure 6. Simulation of algorithm performance under speed increase

UE speed change at 100s time

UE speed change at 100s time

UE speed change at 100s time

successfully hands over from cell 63 to cell 68 and within a short time there is a further handover from cell 68 to cell 61.

To solve the unnecessary handover problem, MRO usually tunes the handover parameter to delay the trigger of the first handover (making the first handover more difficult), then the UE can directly handover from the first cell to the third cell.

In order to identify the unnecessary handover problem, the conventional MRO calculates the unnecessary handover number and divides by the total handover count. It again requires enough handovers to give an accurate decision. In this study we used the 1000 handovers as the trigger condition and the unnecessary handover target rate is 2%. On the contrary, the soft MRO uses the soft metric to compare with the lower SMT (k*SMT and k=0.8 here) to give the decision. Since the soft metric is also gathered from successful handovers, it requires a smaller amount of handover samples than conventional MRO. Finally, we see from Fig. 9 the quick convergence of unnecessary handover rate of the red dashed line. While the conventional MRO with the blue dashed line takes a much longer time to get an unnecessary handover rate drop.

The comparison of handover numbers and unnecessary handover numbers are given in Fig. 10. The benefit from the quick reaction to the too conservative parameter is evident; the unnecessary handover number of soft MRO has only a short period of growth at the beginning and then keeps static after about 40s. While the unnecessary handover number of conventional handover keep increasing for a long time. Also the difference of the handover numbers is seen, benefiting from the reduced unnecessary handover from cell 66 to cell 68 and directly handover from cell 66 to cell 61, soft MRO also has a lower handover number than the conventional one.

IV. CONCLUSIONS In this paper we used the soft MRO and NCH to work

together to solve a previously unresolved challenge in LTE HetNet mobility, namely to be self adaptive to the UE speed changes. The comparison by simulation with the conventional MRO shows the soft MRO can quickly adapt to the speed changes in HetNet and saves a lot of handover failures during the convergence. Also if the NCH parameters are set too conservatively, the soft MRO also can quickly adapt the parameters to reduce the unnecessary handover number.

REFERENCES [1] 3GPP TS 36.300, E-UTRA and E-UTRAN; Overall description; Stage

2 (Release 9), V9.3.0, December 2011. [2] 3GPP TR 36.814, Further advancements for E-UTRA physical layer

aspects (Release 9), V9.0.0, March 2011. [3] G. Zhou, P. Legg, and G. Hui, A network controlled handover

mechanism and its optimization in LTE heterogeneous networks, WCNC 2013, April 2013.

[4] G. Hui and P. Legg, Soft metric assisted mobility robustness optimization in LTE networks, ISWCS 2012, August 2012.

[5] 3GPP TR 36.839, Mobility enhancements in heterogeneous networks, V11.0.0, September 2012.

[6] D. Lopez-Perez, I. Guvenc, and X. Chu, "Mobility management challenges in 3GPP heterogeneous networks," IEEE Communications Magazine, vol. 50, no.12, pp.70-78, December 2012.

[7] 3GPP R3-112989, Analysis and support of enhanced mobility in HetNet deployments, November 2011.

[8] 3GPP TS 36.331, E-UTRA Radio Resource Control (RRC); Protocol specification (Release 9), v9.2.0, March 2010.

Figure 10. HO number and unnecessary HO number results

Figure 9. MRO algorithm reactions on too conservative parameter

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