A Handover Optimization Algorithm with Mobility Robustness for LTE systems

Koichiro Kitagawa, Toshihiko Komine, Toshiaki Yamamoto, Satoshi Konishi KDDI R&D Laboratories Inc.

2-1-15 Ohara, Fujimino-shi, Saitama, Japan {ko-kitagawa, to-komine, tos-yama, skonishi}@kddilabs.jp

Abstract— A large number of cells will be deployed to provide high speed services in any places using the Long-Term Evolution (LTE) system. The management of such a large number of cells increases the operating expenditure (OPEX). Self-organizing network (SON) attracts attention as an effective way to reduce OPEX. This paper proposes a self-optimization algorithm for handover (HO) parameters. In conventional studies, the HO optimization techniques are discussed in models with stationary mobility of user equipment (UE). On the other hand, the key feature of the proposed algorithm is the mobility robustness, which means that the HO performance is robust against the change in UE mobility. In order to realize the mobility robustness, the proposed algorithm adaptively adjusts the HO parameters considering cause of HO failures, which changes in UE mobility. We examine the performance of the proposed algorithm through the computer simulations and confirm the mobility robustness. The simulation study demonstrates the following; The HO parameters are initially set to the optimum values for UEs with 3 km/h based on the random walk. Then, when the mobility changes from 3 km/h random walk to 300 km/h linear motion, the HO failure rates increases to 19%. The proposed algorithm reduces such increasing HO failure rates less than 0.2%.

Keywords: Self-Organizing networks; Self-optimization; handover; mobility robustness; LTE

I. INTRODUCTION The recent dramatic growth in mobile traffic requires new

wireless communication systems that increase network capacity. Long-Term Evolution (LTE), which has been developed in the 3rd Generation Partnership Project (3GPP), is a wireless communication system that increases network capacity with high spectral efficiency [1]. Even if the LTE system is newly implemented, it is better to deploy a large number of cells to accommodate such a large volume of traffic. However, as the number of cells becomes larger, Operating Expenditure (OPEX), which is the cost spent continuously for the network operation and maintenance, increases enormously. Therefore, it is necessary to develop new schemes which automatically operate the cellular system. In 3GPP, Self-Organizing Networks (SON) have been specified for that purpose [2]. One of the main targets in SON is the self-optimization of handover (HO). Generally, the parameter setting for the hard HO, of which procedure is adopted in LTE, is more sensitive than that for the soft HO used in conventional CDMA systems. In addition, the LTE system is required to support a wide range of mobility of user equipment (UE) from 0 km/h up to 350 km/h. Since the changes in mobility directly

affect HO performance, it is important to develop self-optimization techniques [3] that adaptively adjust the HO parameters to achieve stability in HO performance against changes in mobility.

Several studies have discussed the optimal setting of HO parameters [4-8], considering not only the HO failure rate, but also the Ping-Pong HO rate, which is defined as the rate of HOs from the handed-over cell to the original serving cell after the HO from the original serving cell to the handed-over cell. Legg et al. [6] showed that there is a tradeoff between the HO failure rate and the Ping-Pong HO rate especially for high velocity UEs. They found the parameter setting that reduces both HO failure rate and Ping-Pong HO rate for fixed UE velocity. However, UE mobility such as velocity and moving direction varies from hour to hour in actual environment. In the situation with such changes, their optimization method for a fixed UE velocity may not provide the optimal parameter setting. Hence, it is necessary to develop a HO optimization algorithm to deal with such realistic changes while minimizing both HO failure rate and Ping-Pong HO rate.

This paper proposes a HO parameter optimization algorithm that can be used even for dramatic changes in UE mobility without requiring additional functions for estimation of UE mobility. The proposed algorithm takes into account the cause of HO failure, which directly reflects the change in UE mobility, and realizes tracking capability for any change in UE mobility. In addition, the proposed algorithm also considers the tradeoff between the HO failure rate and the Ping-Pong HO rate as in conventional studies. In order to verify the mobility robustness of the proposed algorithm, we perform a parametric study assuming environments with changes in UE velocity and moving direction. It is demonstrated that the proposed algorithm is robust against changes in UE mobility.

The rest of the paper is organized as follows. In section II, we introduce HO parameters to be optimized and present the qualitative analysis of the effect of each HO parameter adjustment. Section III explains the proposed algorithm for HO parameters, and then simulation results are presented in section IV. Finally, we conclude this paper in section V.

II. INTRODUCTION OF HO MECHANISM AND HO FAILURE EVENTS

A. Measurement report transmission for HO In LTE systems, the HO procedure is executed between

the serving evolved NodeB (eNB), that controls the serving

2011 IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications

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cell as base station, and the target eNB that controls a HO target cell. The HO procedure is initiated with measurement report transmitted from a UE to its serving eNB [9]. The measurement report includes the received power of the downlink reference signal, called the Reference Signal Received Power (RSRP), of the serving and neighbor cells. There are several conditions to trigger the transmission of measurement reports. As an example, the received power of a serving cell becomes worse than threshold, and the received power of a neighbor cell becomes better than threshold. In this paper, we focus on the trigger condition for HO, called the A3 condition, in LTE systems [9]. Fig. 1 illustrates the trigger timing of A3 measurement report. The A3 condition for the HO target cell j, at UE ID k is defined as

Μ(j, k) −Μ(i, k) > Ο(i, j), (1)

where M(i, k) and M(j, k) are the RSRP levels at UE ID k of the serving cell i and the target cell j. O(i, j) is a cell-specific parameter called the HO margin. The UE begins to transmit a measurement report to the serving eNB when Eq. (1) remains satisfied for a duration identified by the timer named Time-to-Trigger (TTT). When the serving eNB receives the measurement report from the UE, the eNB initiates a HO message transaction with the target eNB.

B. HO failures and Ping-Pong HOs Inappropriate trigger timing of a measurement report

transmission leads to HO failure. HO failures are classified into the following three types [1].

• Too Early HO Too early HO trigger timing leads to a low RSRP level of the target cell. In this case, the message transaction between the UE and the target eNB fails, and the UE changes the state to radio link failure (RLF). After detecting the RLF in the UE, the UE reconnects to the serving cell again.

• Too Late HO Too late HO trigger timing leads to low RSRP level of the serving cell. The message transaction failures between the UE and the serving cell during the HO procedure, or before HO is triggered, are classified as ‘Too Late HO’. After the RLF is detected in the UE, the UE reconnects to the target cell.

• HO to Wrong Cell HO to a wrong cell is a failure event involving three cells: the serving cell, the target cell, and the reconnected cell. HO to a target cell providing unstable received power leads to HO failure. If there is another suitable cell for the UE to reconnect to after the RLF, the UE reconnects to the cell that is neither the serving cell nor the target cell.

Since the HO procedure is radio resource consuming, in addition to the above HO failure cases, it is necessary to minimize the number of Ping-Pong HOs, in which the UE returns to the original serving cell within a certain period of time (e.g. 2 seconds) after a HO from the original serving cell to a neighbor cell.

Figure 1. Illustration on the relation between the changes in RSRP and the transmission of the measurement reports from UE until the HO completion. UE transmits the RSRP measurement report of serving cell i and target cell j

after the period of Time-to-Trigger.

In the following sections, we consider the above HO failure events as well as the Ping-Pong HO to be HO failure events, and discuss a reduction scheme for HO failure events by adjusting HO parameters.

C. Adjustment of HO parameters Since the change in mobility such as moving direction and

velocity affects the measurement report triggering timing for successful HOs, the change in mobility leads to the HO failure events explained in the previous subsections. Therefore, the measurement report triggering timing should be adaptively adjusted according to the change in mobility. The mobility in real systems generally differs among neighbor cells, because it is affected by the mobility environment between the serving cell and the neighbor cell such as the flow of cars on a road. Therefore, such parameter adjustments should be independently executed for all neighbor cells. In order to adjust the measurement report triggering timing, we can use the TTT or HO margin. However, the TTT parameter cannot be independently adjusted for each neighbor cell, while the HO margin O(i, j) can be adjusted with respect to the neighboring pair of cells. Therefore, the HO margin is more appropriate than TTT to adjust HO timing to the change in mobility. Here, we focus on the parameter adjustment of O(i, j) in order to take into account the changing UE mobility and its significance to HOs among neighbor cells.

Table I shows an example of numerical results obtained from a simulation study on the rate of each kind of HO failures, which is calculated from dividing the number of each kind of HO failures by the sum of the HO success events and the HO failure events, when the same HO margin is applied to all cells. We can see from the table that the Too Late HO rate increases as HO margin increases, while the Ping-Pong HO rate and the Too Early HO rate decrease. We take into account such a relationship between HO failure events and HO margin, and summarize, in Table II, the parameter adjustments for reducing the HO failure events. It should be noted that the idea of the parameter adjustment for the reduction of the HO to Wrong Cell is to make the timing of HO earlier for the reconnected cell rather than that for the target cell. As shown in Table II, the parameter adjustments for a reduction in HO failure events are different in some kinds of HO failures. Hence, the adjustment of the HO margin should be performed with consideration of the occurrence of each kind of HO failures. At the same time,

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since the HO procedure is radio resource consuming, the parameter adjustment for the reduction of HO failure events is desired to be executed with the minimum occurrence of Ping-Pong HO. Therefore, we regard the Ping-Pong HO as the HO failure events equivalent to the other HO failure events. Thus, we use the following objective for the optimization criterion: Minimize ε(O(i,j)) + γ (O(i,j)), (2)

where ε(O(i,j)), γ(O(i,j)) denotes the HO failure rate and the Ping-Pong HO rate, respectively. Hereafter, we call the objective function in Eq. (2) the total HO failure rate. Since, as shown in Table I, the Ping-Pong HO rate increases dramatically with the decrease in HO margin, the total HO failure rate strongly demands small Ping-Pong HO rate at the optimum point.

III. PROPOSED ALGORITHM FOR HO OPTIMIZIATION WITH CONSIDERATION OF UE MOBILITY

In order to discuss HO parameter adjustments for the change in UE mobility, in Fig. 2, we evaluate the total HO failure rates applying the same HO margin to all the neighbor cells, for several mobility UE sets. In the evaluation, we use the random walk mobility model for pedestrians at a speed of 3 km/h. On the other hand, we assume one-directional linear motion for UEs at speeds over 30 km/h. It is confirmed that, at the speed of 3 km/h, the total HO failure rate is minimized at the HO margin 4.5 dB, while at speeds of 30, 50, and 80 km/h, no HO failure events are observed at the HO margin values over 2.5 dB. Then, for the change in mobility among these speeds, the adjustment of HO margin is not necessary since the total HO failure rate is minimized at the same HO margin (4.5dB). On the other hand, for the high mobility cases 250 km/h and 300 km/h, the HO margin minimizing the total HO failure rate is less than or equal to 1.5 dB. The HO margin that minimizes the total HO failure rate for 3 km/h results in high total HO failure rate for the cases of 300 and 250 km/h. Thus, the HO margin should be adjusted according to the change in UE mobility when both 3 km/h and 300 km/h UEs exist, or when the mobility changes from 3 km/h to 250 km/h.

Hereafter, we discuss parameter adjustments according to the change in mobility. A useful fact for such parameter adjustment is that the occurrence of each HO failure event is affected by the UE mobility. For example, high mobility UEs cause Too Late HOs rather than Too Early HOs while low mobility UEs cause Too Early HOs rather than Too Late HOs.

Figure 2. HO failure rate plus Ping-Pong HO rate versus HO margin for the mobility of 3 km/h with random walk model as well as 30, 50, 80, 250, 300

km/h with 1-directional linear motion model

TABLE I. THE RATES OF HO FAILURE EVENTS IN ALL HO EVENTS, IN THE CASE OF RANDOM WALK WITH 3 KM/H FOR ALL THE UES

HO margin [dB]

Ping-Pong HO

rate [×10-3]

Too Late HO

rate [×10-3]

Too Early HO

rate [×10-3]

HO to wrong cell rate [×10-3]

2.5 140 0.16 0.075 0.019 3.5 23 1.0 0.0 0.046 4.5 1.9 1.8 0.0 0.15 6.5 0.0 8.9 0.0 0.51

TABLE II. THE PARAMETER ADJUSTMENT DIRECTION FOR REDUCTION OF EACH KIND OF HO FAILURES

Direction of Parameter Adjustment

Too Late HO Decrease O(i,j) Too Early HO Increase O(i,j) HO to Wrong Cell Increase O(i,j), and/or decrease O(i,k), where

cell i is the serving cell, cell j is the target cell, and cell k is the reconnected cell.

Ping-Pong HO Increase O(i,j) Therefore, the parameter adjustment on the basis of the occurrence of each HO failure event can deal with the change in UE mobility. By utilizing the above fact and understanding, we perform the adjustment procedure as depicted in Fig. 3. We decide the adjustment direction of the HO margin from the comparison between the number of HO failure events reduced by decreasing the HO margin and the ones reduced by increasing the HO margin. We count the HO to Wrong Cell events with the distinction of the target cell and the reconnected cell since the direction of the parameter adjustment for these cells is different from each other. In addition, in order to realize adaptive parameter adjustments according to the change in UE mobility, we periodically perform the parameter adjustment with the minimum granularity of HO margin in line with the 3GPP’s specification [9]. The parameter adjustment continues until the number of HO failure events reduced by increasing the HO margin and those reduced by decreasing the HO margin are adjusted to the same number. In the following section, we show, through a system level simulation, that our optimization algorithm significantly reduces the total HO failure rate even in the tough mobility change, such as from 3 km/h to 300 km/h.

IV. SIMULATION RESULTS In order to evaluate the adjustment capability of our

algorithm against the mobility in UEs, we apply our algorithm to cases in which the mobility of UEs changes from 3 km/h

Figure 3. The HO margin Optimization Algorithm

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with the random walk mobility to the bidirectional linear motion with 50 km/h, 250 km/h, and 300 km/h as depicted in Fig. 4. The UEs with the linear motion cross cells #1, #0, #4, and #3. In the following evaluation, we focus on the HO performance with respect to HOs from cell #0 as the serving cell, and discuss the characteristic of the proposed algorithm.

For the initial condition, we set the HO margin for all neighbor cells to 4.5 dB, which minimizes the total HO failure rate for 3 km/h as shown in Fig. 2, while we set the mobility to the linear motion depicted in Fig. 4 at speeds of 50 km/h, 250 km/h 300 km/h, respectively. TTT is set to 256 ms, with consideration for the Doppler shift at a speed of 3 km/h. In the evaluation, we assume one-path Rayleigh fading, and time-invariant shadowing map given by log normal distribution with standard deviation of 8 dB. The decorrelation distance of the shadowing map is set to 50 m. We also take into account a constant interruption time for HO delay, in which management frames are exchanged among the UE, the serving eNB, and the target eNB. The other simulation parameters are summarized in Table III. It should be noted in Table III that Hysteresis, Cell individual offset (CIO), and A3 offset [9] are parameters which are used to specify HO margin O(i,j). In order to adjust HO margin, we adjust CIO since CIO is the only parameter, which is able to adjust HO margin with respect to the neighbor cells independently. In Fig. 5, we show the dynamics of the total HO failure rate with the proposed algorithm. The horizontal axis of Fig. 5 is the serial number of the optimization opportunity. With the result in Fig. 5, it is confirmed that the proposed algorithm reduces the total HO failure rate to less than 1% regardless of the UE mobility. Before applying the algorithm, the total HO failure rates are considerably high for the UE mobility 250 km/h and 300 km/h. Such extremely high total HO failure rates are reduced by using the algorithm to less than 1%. On the other hand, in case of UE mobility of 50 km/h, there is no increase in the total HO failure rate, and then, any parameter adjustment is not needed. It should also be noted that, in each mobility case, the total HO failure rates are almost converged with the lowest value at the 6th parameter adjustment.

In the above evaluation scenarios, the HO margins for neighbor cells are independently adjusted according to each mobility. As an example, we focus on the result in 300 km/h mobility case, and further discuss the characteristic of our optimization algorithm. In Fig. 6, we show the parameter adjustment history of HO margin for the serving cell #0, and neighbor cells #1 and #4. It is confirmed that the HO margin

Figure 4. Simulation area with the cell id for cells; UEs with linear motion move along the arrows (a) and (b), and UEs with random walk moves in the

rectangular area marked by the dashed line.

Figure 5. The optimization criteria with the optimization algorithm for the mobility change from 3 km/h to 50 km/h, 250 km/h, and 300 km/h

is adjusted to around 1 dB for neighbor cell #1 and to around –4 dB for neighbor cell #4. It should be noted that the above difference in the converged value of the HO margin is due to the difference in the shadowing and pathloss effect among these cells. The parameter adjustment is converged at the point that balances HO failure events. First, in Table IV, we show the rate of each kind of HO failures for HOs from cell #0 to cell #1. At the initial optimization opportunity, Too Late HO is the dominant HO failure event, and the HO margin decreases. On the other hand, at the latter optimization opportunity, Too Late HO is completely reduced, and Ping-Pong HO becomes the dominant HO failure event, and thus the HO margin increases. Thus, there is the tradeoff between Too Late HO and Ping-Pong HO at the parameter converged point. Second, in Table V, we show the rate of each kind of HO failures for HOs from cell #0 to cell #4. Too Late HO is dominant at the initial optimization opportunity as well as the case in Table IV. However, Ping-Pong HO does not occur even in the latter of parameter adjustments. Then, the HO margin is reduced until Too Late HO is completely reduced. Thus, the proposed algorithm stops at the tradeoff point or reduces HO failure unless Ping-Pong HO does not occur. As the result, the following is confirmed; the HO failure rates are increased to about 19% by the mobility change from 3 km/h random walk to 300 km/h linear motion. Such increased HO failure rate is reduced to under 0.2% while the Ping-Pong HO rate is suppressed less than 1%. Thus, the HO margin parameter adjustments reduce the HO failure rate without a remarkable increase in the Ping-Pong HOs for high mobility. It should be noted that the proposed algorithm is also applicable in the different change in the velocity, such as the one from high mobility to low mobility.

Figure 6. The adjustment history of HO margin of serving cell #0 to the

target cell #1 and #4 in the case of 300 km/h mobility

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TABLE III. SIMULATION PARAMETERS

Number of Cells 6 cells layout as in Fig. 4 Inter-Site Distance (ISD)[m] 500 Carrier frequency [GHz] 2.0 System Bandwidth [MHz] 10 TTT[ms] 256 A3 offset [dB] 0 Hysteresis [dB] 1.5 Fading 1-path Rayleigh fading Antenna pattern vertical and horizontal pattern are

considered as [10] Cell Individual Offset [dB] specified in [9] The number of HOs for one parameter adjustment

10,000 HOs per l cell

RLF detection [8] Qin = -6 dB, Qout = -8 dB, N310 = 1, N311 = 1, T310 = 1s

Mobility model 3 km/h: random walk 50, 250, 300 km/h: linear motion between left edge and right edge of simulation area illustrated in Fig. 4

TABLE IV. THE RATES OF HO FAILURE EVENTS FOR HOS FROM CELL #0

TO CELL #1 IN THE CASE OF 300 KM/H MOBILITY

S/N of HO optimization

event

Too Late HO

rate [×10-3]

Too Early HO

rate [×10-3]

HO to Wrong Cell rate [×10-3]

Ping-Pong HO

rate [×10-3]

0 2.0 ×102 0.0 0.0 0.0 1 5.4 ×10 0.0 0.0 0.0 2 6.1 0.0 0.0 0.0 3 0.4 0.0 0.0 0.0 4 0.0 0.0 0.0 0.0 5 0.0 0.0 0.0 0.1 6 0.3 0.0 0.0 0.0 7 0.8 0.0 0.0 6.4 8 0.1 0.0 0.0 0.0 9 1.6 0.0 0.0 8.5

TABLE V. THE RATES OF HO FAILURE EVENTS FOR HOS FROM CELL #0 TO CELL #4 IN THE CASE OF 300 KM/H MOBILITY

S/N of HO optimizatio

n event

Too Late HO

rate [×10-3]

Too Early HO

rate [×10-3]

HO to Wrong Cell rate [×10-3]

Ping-Pong HO

rate [×10-3] 0 1.8 ×102 0.0 0.0 0.0 1 1.8 ×102 0.0 0.0 0.0 2 1.8 ×102 0.0 0.0 0.0 3 1.4 ×102 0.0 0.0 0.0 4 8.2 ×10 0.0 0.0 0.0 5 3.8 ×10 0.0 0.0 0.0 6 1.3 ×10 0.0 0.0 0.0 7 3.4 0.0 0.0 0.0 8 0.6 0.0 0.0 0.0 9 0.0 0.0 0.0 0.1

V. CONCLUSION This paper proposed a HO parameter optimization

algorithm that adjusts the HO margin according to the change in UE mobility. The proposed algorithm detects the change in UE mobility through the change in HO failure events, and adaptively adjusts HO margin to the UE mobility. We evaluated the performance of the proposed algorithm through system level simulations, which demonstrated that the algorithm effectively reduces both the HO failure and Ping-Pong HO rates while the tradeoff between the two rates is

taken into consideration in several scenarios of mobility changes. As an example, when the mobility changes from 3 km/h random walk mobility to 300 km/h linear motion, the HO failure rate increases to about 19% with the optimum HO parameter for 3 km/h random walk mobility. Such an increase in HO failures is reduced by the proposed algorithm. The algorithm provides less than 0.2% HO failure rate and less than 1% Ping-Pong HO rates. The proposed algorithm also achieves less 1% HO failure and Ping-Pong HO rate at the 6th parameter adjustment. When the speed of convergence is considered, the tradeoff between the number of sample HOs and the number of parameter adjustment per time should be further considered. In this paper, we studied the proposed algorithm in viewpoint of mobility robustness. However, the value of HO margin minimizing the total HO failure rate essentially depends on the rate of change in the difference between the RSRP levels of the serving cell and the target cell. Therefore, the proposed algorithm can also be effective for reduction of HO failures such as in the systems with unequal-sized cells or with severe shadowing environment caused by buildings in urban area. Also, since the proposed algorithm adjusts the HO margin at every optimization opportunity, parameter oscillation around the convergence point occurs. Such inefficient parameter adjustments are not preferred from the viewpoint of real operation. Thus, the reduction scheme for the number of inefficient parameter adjustments will be considered as a future work.

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