Optimization of control parameters using averaging of ... - IEEE Xplore

Proceeding of the IEEE 28th Canadian Conference on Electrical and Computer Engineering Halifax, Canada, May 3-6, 2015

Optimization of Control Parameters Using Averaging of Handover Indicator and Received Power for Minimizing Ping-Pong Handover in LTE Noha Hassan, Khalid Elkhazeen, Kaarman Raahemiafar, and Xavier Fernando

Abstract—This paper introduces an enhanced automatic selfoptimization technique that picks up the best handover (HO) limits of long-term evolution (LTE) transceivers. The goal is to minimize the possible degradation due to HO call drop, HO failure (HOF) and Ping-Pong HO to improve HO performance indicator (HPI)of the network. The proposed algorithm tunes the hysteresis and time-to-trigger based on exponential averaging of HPI and the average of the old values of the received signal reference power (RSRP). The results show a clear improvement over weighted HPI averaging without RSRP averaging. Index Terms—Handover, Ping-Pong, Handover Fail , Received Signal Reference Power, Alpha.

I. I NTRODUCTION The high demand for data traffic and services pushed the wireless mobile operators towards Long-term Evolution (LTE) as it introduces a simple architecture that provides up to 100 (Mbps) on the downlink side and 50 (Mbps) in the uplink side. Uses Orthogonal Frequency Division Multiple Access (OFDMA) techniques on the downlink side and Single CarrierOrthogonal Frequency Division Multiplexing (SC-OFDM) on the uplink. OFDM divides the assigned given bandwidth into equally spaced, and orthogonal sub-carriers [1]. LTE network architecture has three elements: Mobile Management Entity (MME), evolved-NodeB (eNodeB), Serving Gateway (S-GW), and Packet Network Gateway(P-GW). MME manages mobility, User Equipment (UE) control, equipment authentication, and control signaling. eNodeB performs all the radio-related functions like packet scheduling terminates the interface towards the radio access network eUTRAN and packet data network [2]. One of the advanced features offered by 3GPP for LTE is the Self Optimized Network (SON), which ensures coverage to all the network life cycle. The goal of SON at the optimization stage is to change the control parameters according to the degradation of the network performance. The proposed algorithm optimizes the tuning of the predefined threshold difference between the average RSRP of the target, the source cell (Hysteresis), and time-to-trigger (TTT). Usually, operators follow static setting of the control parameters and often checked it on daily, weekly or even monthly basis. They only optimize the extreme values or when there is an obvious

Fig. 1. Seamless Handover.

problem, that results from accumulating degradation in the hand over performance indicator (HPI). In a real situation, the radio network’s environment varies rapidly due to the fast signal fading, interference and User Equipment (UE) movement. That makes the HO between two neighboring cells to repeat many times in a short period of time. This kind of repeated HO is called Ping-Pong handover (HOPP), wastes the network resource and might lead to a call drop or handover failure (HOF). Therefore the HO decision has to be made at the right time [3]. The aim of our proposed algorithm is to find the optimum parameter setting based on the current network situation. We organized the remainder of this paper as follows. In section II, we present handover techniques. Section III HO metric. Section IV provides the literature review of related LTE HO work. In section V, we explain the proposed methodology. The paper is conducted in section VI to show the simulation and results. The acknowledgement is in section VII. The paper is concluded in section VIII with guidelines for the future work. II. H ANDOVER T ECHNIQUE We can define handover as the process of switching a radio connection from one eNodeB to another in order to maintain seamless radio connection during mobile station movement; Fig. 1. The types of handover are:

This work was not supported by any organization N. Hassan is with the Department of Electrical and Computer Engineering, Ryerson University, Toronto, Canada [email protected] K. Elkhazeen is with the Department of Electrical and Computer Engineering, Ryerson University, Toronto, Canada [email protected] K. Raahemiafar is with the Department of Electrical and Computer Engineering, Ryerson University, Toronto, Canada [email protected] X. Fernando is with the the Department of Electrical and Computer Engineering, Ryerson University, Toronto, Canada [email protected]

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1) Hard handover in which the current connection between the user and the target is broken before a new connection is established. Hard handover was used in the Global System for Mobile Communications( GSM) cellular

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where we subtract the path loss, and the shadowing loss from the transmitted power. III. H ANDOVER METRICS The objective of the LTE handover is to minimize the HPI. HPI is calculated using equation (2): eqHP I = HP IHOF + HP IHP P + HP IRLF

We will define the previously added performance indicators, and explain the meaning of each.

Fig. 2. Handover Steps.

A. Handover Fail Performance indicator

mobile systems, where each cell was given its frequency band [4]. 2) Soft and softer Handover are features used by the two standards (CDMA and W-CDMA), where the UE is connected to two or more sectors during a call. When the two sectors are from the same cell, the handover is referred to as the softer handover. [5]. The following steps are taken to perform the handover, these steps are also shown in figure 2. 1) The Source eNB collects the report containing the link quality measurements and performs handover function control. 2) Based on the measurement reports the source eNB decides whether to start a handover or not. If the decision is to start a handover based on the measurement reports,and it source eNB releases a handover request frame to the target. 3) Target eNB makes admission control according to the quality of service of the received information. 4) The Target eNB transmits the handover request acknowledgment frame to the Source. 5) The router forwards data from the Source eNB to the target eNB, then the source generates the handover command. 6) The UE performs synchronization with the target cell. Then it sends a confirmation message that the handover procedure was over. 7) The Target eNB starts to send data to the UE. In addition it sends a route switching message to the MME informing it that the UE has switched its cell.

We can define this term as the number of failed HOs divided by the number of handover attempts. HP IHOF =

N HOF ail N HOAttempt

(3)

B. Ping-Pong Handover Performance indicator We can define this indicator as the number of HOPP divided by the total number of handover trials. HP IHP P =

N HOP P N HOP P + N HON P P + N HOF ail

(4)

where N HOP P is the number of ping pong handover C. Radio Link Failure Performance Indicator We can define this indicator as the number of RLFs divided by the number of accepted calls. HP IRLF =

NDropped NAccepted

(5)

The system calculates the following constraints, which can be treated as optimization constraints. 1) RSRPT > RSRPS + HOM

(6)

where RSRPT = Received Signal Reference Power from target eN odeB. RSRPS = Received Signal Reference Power from source eN odeB.

A. The Handover Process Parameters We can control the HO process using two parameters: 1) Handover hysteresis (HOHISS ). 2) Time to Trigger (TTT). The red curve in Fig. 3 highlights the reduction in RSRP received by UE from eNB. The blue curve shows the rising RSRP Pb received by the UE. Handover started at time T1 , when Pb is equal to Pa plus HOHISS RSRP is calculated using equation: RSRP = PCellT ransmittingP ower − PP athLoss −PShadowF adingM argin

(2)

(1) Fig. 3. LTE Handover Process.

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HOM = Handover Margin

The timer value should be chosen with care in order to eliminate the Ping-Pong HO probability, and decrease the dropped calls. The drawbacks of this approach are that it only considers low and medium user mobility , and hysteresis is not considered in the simulation. Toshihiko et al. [8] suggested a technique for cell selection to find a suitable reconnected cell taking into account both the uplink and downlink. The steps used to apply this method are: 1) The eNB measures the interference level of the target and neighboring cells on the uplink channel. This value is sent to the SON server. 2) The SON server selects the appropriate node, to fulfill the desired uplink quality. 3) The causes of HO failure were analyzed as well. They were found to be RLF before and at measurement report transmission and HO failure during the random access procedure (RAP). The advantage of the proposed technique is that it reduces the rate of HO failure. Its main drawbacks however are that, it does not investigate the call drop which is the most important performance indicator, and there is no use of Hysteresis value or time to trigger. Kim et al. [9] presented a handover optimization scheme to adjust the time-to-trigger parameter based on the received signal strength in LTE network for a variety of velocities. This scheme demonstrates enhanced performance for data link failure and Ping-Pong over the traditional HO algorithm. However the authors did not consider the RLF and the scheme changed only the TTT. Ermolayev [10] introduced a new self-optimization algorithm that adjusts the HO metrics to improve the HPI with fixed weighted coefficients proposed by the operators. The simulation used signal filtration as suggested in 3GPP specifications. This approach greatly improved the estimated RSRP. Nevertheless the author did not investigate the Ping-Pong handover performance and used fixed speed for the UE. Zoltan et al. [11] suggested a method to classify, and eliminate unwanted effects such as the Ping-Pong effect. The proposed method generally combines sub cell movement detection method and ping-pong detection. It tries to determine the appropriate time to apply handover threshold tuning without increasing HOF. Ping-Pong detection method is based on the handover history, where the TTT time is called the times Tpp time. After experimentation, the results revealed that 73 percent of Ping-Pongs resulted from stationary terminals. Sub cell movement detection is a timing based algorithm for detecting moving terminals, as stationary users cause excessive Ping-Pong. The method is applied by: 1) Calculating the arrival time difference (Tm ) between the system frames coming from neighboring cells, compared with an inner clock. 2) Calculating the offset time difference between the two base stations Tof f set . When UE moves, Tof f set changes tremendously.

2) CLT (Load) < M LT

(7)

where CLT is the current load of target eN odeB, and M LT is the maximum load threshold of target eN odeB. 3) HOT rigger ≥ T T T

(8)

HO Trigger starts timing when both of the above conditions are satisfied. IV. R ELATED W ORK Jansen et al. [6] proposed a weighted normalized handover performance optimization technique. The algorithm chooses the best value of hysteresis and time-to-trigger in order to reduce handover failure and call dropping. The authors examined the system’s performance with no optimization for different handover parameter settings and used a weighting function to test the interaction between the HPIs. This formula is given as: HP = w1 HP IHOF + w2 HP IHP P + w3 HP IDC

(9)

wx is the weight assigned to each HPI, where x=1,2,... We set the handover performance target thresholds for all HPIs to values that increase the likelihood of optimum handover performance. The target thresholds are increased or decreased by 33 percent if the HPIs stay below the target thresholds for the good performance time or above the HPI target threshold for the bad performance time. After that, the handover performance target thresholds are increased again by 33 percent, and the operating point of the cell is altered. In their paper, Jansen and colleagues implemented a realistic network topology. After performing the optimization, the values of the handover failure ratio and the ping-pong handover ratio were zero. The call dropping ratio was not affected. The drawback of this algorithm is that it has a huge amount of signaling network overhead in the entire network, which increases the radio link failure (RLF. The other drawback of this algorithm is that the results are based on the used realistic scenario only and donot apply to other scenarios in general. Ghanem et al. [7] suggested a technique in which the old path between the source eNB and SGW/MME MMEwas kept for as long as possible during the ping-pong, delaying the HO. The model used considered user mobility throughout the cell. The indicators used were the dropped calls rate, and the ping-pong handover rate. The proposed algorithm has two phases: 1) Designing the HO: a new connection between the UE and the target is established, and at the same time the old S1 interface is kept. 2) HO completion: S1 path is canceled, and S2 interface is in use.

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where, CF N − SF N = Tm

(10)

HP IHP P = (nHP IHP P (1) + (n − 1)HP IHP P (2) +...HP IHP P (n)) )/(n(n + 1)/2) (13)

Tof f set = (SF N1 − SF N2 ) = Tm1 − Tm2

(11)

This is the actual equation used in the main reference paper where n here represents the parameter α, that was used in the other paper. The equation for exponential moving average for HP IHP P :

This technique decreases the number of dropped calls and Ping-Pong handovers. However the drawback of this technique is that radio conditions do not facilitate threshold optimizations for many users. Irina et al. [12] suggested a new technique to eliminate the bad effects of handover. This goal can be achieved by selecting the best detected values of hysteresis and time-to-trigger, and improving the convergence time as well. The authors also suggested Introducing an extra threshold that will allow the direction of optimization to be switched only if performance degrades. This method is good since: 1) It does not allow valuable changes of the HOP to be wasted by ping-ponging between the two HOPs. 2) Furthermore it Improves performance degradation (PD) by changing the optimization direction. The shortcomings of this method are that performance will vary very little between HOPs, and, there is an increase in the amount of signaling. Mizanur et al. [13] proposed a handover algorithm for LTE Self-Optimizing-Network (SON) that considers a weighted history of the Ping-Pong handover performance indicator (HP IHP P ) to change the optimization procedure through good regions. However Their results showed a slight increase in the HOF and the authors only considered linear averaging. are decreased, and otherwise increased in the bad regions based on the predefined handover performance indicator threshold (HP IHP PT H ) chosen by the network operator.

HP IHP P = αHP IHP P (n) + (1 − α)

n−1 

iHP IHP P (i)

i=1

(14) where the coefficient α represents the degree of the weighting decrease and its value is between 0 and 1. A higher α discounts older observations faster. 2) Addition of the average OLDRSRP condition using exponential averaging with α=0.3 to have more efficient HO performance. The average OLDRSRP was calculated as follows: OldRSRP SU (nT p) = αRSRPold(n) + (1 − α)

n−1 

iRSRPold(i)

(15)

i=1

Then we compared the new value of the RSRP, with the average of the old values. where OLDRSRPSU (nT p) is the power received by user U from the assigned serving node S at the nth HO measurement time TP , and N is the total number of time periods having a duration of time TP . The average old values of RSRP of cell node S that was received by user U is named RSRPavg(SU ) . This was calculated using the previously explained exponential averaging method. The constraint on the average RSRP can be expressed as follows:

V. M ETHODOLOGY We implemented our approach through the following steps: 1) The main equation of the algorithm implemented in Mizanur’s paper was to calculate the weighted HP IHP P , and use this value as a threshold to calculate the new hysteresis, and time to trigger. First we chose the best averaging or smoothing technique for HPI equation that gives the best result for handover failure, and handover Ping-Pong. Then we compared the results obtained using the simple averaging method, the weighted averaging method, and the exponential averaging method. The linear averaging equation for HP IHP P :

HOT rigger ≥ T T T

(16)

where the value of RSRPT represents the current RSRP of the target cell T, HOM is the handover margin, and RSRPavgU is the average RSRP calculated by 15. The goal of this algorithm is to eliminate the possibility of HO in order to reduce the number of unnecessary HO and to make sure that the received reference power of the target cell is better than the RSRP of the current serving cell, and the average RSRP from the beginning of the measurement period to the end.

HP IHP P = (HP IHP P (1) + HP IHP P (2) + ... HP IHP P (n)) )/n (12)

VI. S IMULATION AND R ESULTS

where n is the number of past values. The equation for weighted moving average for HP IHP P :

We have used the LTE System Level simulator [14], which was made up of 7 hexagonal cells with 500 m distance between

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the eNodeBs. We ran the simulation using the configuration parameters shown in table I. The goal was to implement our proposed methodology of average RSRP with exponential HP IHP P averaging, and compare the results with the results of other averaging techniques ( Simple, Weighted, and Exponential ). All researchers have shown that the traditional nooptimization technique in which the T T T and hysteresis are fixed is not the best way to obtain good network performance consistently. In fact, the dynamics techniques in which the T T T , and Hysteresis are changed based on the network performance thresholds lead to the optimum parameter setting. Our main objective is to find to find the best averaging technique, i.e., the one that gives the best result for HOF among the

Fig. 6. NHO Fail Per TTI Values for Different Alpha.

TABLE I P ERFORMANCE PARAMETERS Parameter Value Parameter Value Initial Hysteresis 2 dB UE Traffic Environment Deep Indoor Initial Time to Trigger 2 UE Speed 60 km/hr Critical Time 10 UE Receiver Noise Fig. 9 Good Performance Counter 30 UE Thermal Noise density -174 dBm/Hz Bad performance Counter 10 UE walking Model Star burst Uniform eNodeB TX power 20 Inter eNodeB distance 500 eNodeB ring 7 Minimum Coupling Loss 70 dB eNodeB Ring type Hexagonal Frequency 2.1 GHz Bandwidth 5 MHz Frequency 2.1 GHz Number of Tx 2 Number of Rx 2 Target Sector Center UE Placement Whole Antenna Gain Pattern TS 36.942 Antenna Gain 15 dB Antenna Tilt 8 Rx Height 1.5 Pathloss Model TS25814 Pathloss Environment Urban

Fig. 7. NHOPP Per TTI Values For Different Alpha.

Fig. 8. Comparing the NHO Fail in Our Proposed method, Reference Paper Method, and no Optimization Method.

simple, weighted and exponential moving averaging. Figures 4, and 5 show that exponential averaging gives the best result. Therefore, we used this technique and tried to optimize the value of alpha (the weight used in exponential averaging, that gives the least HOF. The HOPP is shown in figures 6 and 7. Our choice of the best α was based on the average best value, as some values of α give better results than others in some instances. When we run the simulation for the no-optimization with the average RSRP, we got better results for the HOF. The HOPP increased as expected from [2]. However, when we applied the exponential averaging algorithm the HOPP improved as we changed the handover parameters ( TTT, and Hysteresis ) based on the HP IHP P with a predefined threshold the same as [1]. We applied the exponential averaging with α=0.3 for the old values of RSRP, where we applied the following equation:

Fig. 4. NHO Fail Per TTI Without RSRP Averaging.

Fig. 5. NHO Fail Per TTI With RSRP Averaging.

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R EFERENCES [1] A. Omri, “Channel estimation for lte uplink system by perceptron neural network,” International Journal of Wireless and Mobile Networks, vol. 2, pp. 155–165, 2010. [2] L. C.-C. et al., “Optimized performance evaluation of lte hard handover algorithm with average rsrp constraint,” International Journal of Wireless and Mobile Networks, vol. 3, pp. 1–16, 2011. [3] K. Thomas, “Final report on self-organisation and its implications in wireless access networks,” 2010. [4] e. a. A. Abdulhadi, “Evaluation and comparison of soft and hard handovers in universal mobile telecommunication umts networks,” Journal of Kerbala University, vol. 8, pp. 1–13, 2010. [5] J. Kumawat and S. Tailor., “Soft and softer handover in communication netwoks,” International Journal on Recent and Innovation Trends in Computing and Communication, vol. 1, pp. 558–562, 2013. [6] T. J. et al., “Handover parameter optimization in lte self-organizing networks,” in Vehicular Technology Conference Fall (VTC 2010-Fall), 2010 IEEE 72nd. IEEE, 2010, pp. 1–5. [7] K. G. et al., “Reducing ping-pong handover effects in intra eutra networks,” in Communication Systems, Networks and Digital Signal Processing, 2012 8th International Symposium on. IEEE, 2012, pp. 1–5. [8] K. T. et al., “A proposal of cell selection algorithm for lte handover optimization,” in Computers and Communications (ISCC), 2012 IEEE Symposium. IEEE, 2012, pp. 1–5. [9] K. Juwon, “Adaptive ttt scheme for optimizing lte handover,” International Journal of Control and Automation, vol. 7, pp. 35–44, 2014. [10] E. Victor, “Handover parameter optimisation in lte,” 2014. [11] Z. F. et al., “Ping-pong reduction using sub cell movement detection,” in Vehicular Technology Conference (VTC Spring), 2012 IEEE 75th. IEEE, 2012, pp. 1–5. [12] B. Irina, “Enhanced weighted performance based handover optimization in lte,” in Future Network and Mobile Summit (FutureNetw), 2011. IEEE, 2011, pp. 1–8. [13] M. K. et al., “Self-optimizing control parameters for minimizing pingpong handover in long term evolution (lte),” in Communications (QBSC), 2014 27th Biennial Symposium on. IEEE, 2014, pp. 18–122. [14] J. C. I. et al., “System level simulation of lte networks,” in Vehicular Technology Conference, 2010.

Fig. 9. Comparing the NHOPP Obtained by our proposed Method, Reference paper method, and No Optimization.

RSRP avgSU = αRSRPold(n) + (1 − α)

n−1 

iRSRPold(i)

(17)

i=1

Then we compared the new value of the RSRP, with the average of the old values. As it can be seen in figures 8 and 9. Our proposed method showed better result in decreasing HOF, and HOPP than the performance with no optimization. Our results were even better than the ones obtained by the ordinary history method implemented in the reference paper(Mizanur’s paper). VII. ACKNOWLEDGMENTS The authors gratefully thank all whose work, research and support helped us in writing this paper. Also we would like to show our gratitude to the CCECE committee, and we really appreciate the reviewers comments. VIII. CONCLUSIONS AND FUTURE WORKS A. Conclusions Comparing the performance of different averaging techniques, it is evident that exponential averaging gives the best performance for HOF, and HOPP. The value for parameter alpha for the exponential averaging was optimized and found to be 0.3. After applying exponential averaging to the RSRP value, this technique showed better performance than the technique implemented in Mizanur’s paper. B. Future Work For our future work we aim to implement: 1) Implement the simulation on LTE-advanced with different environments. 2) Consider user mobility, speed, and call drop. 3) Consider the performance and Quality of service offerred by the network.

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