Handover Optimization Algorithm in LTE High-Speed Railway Environment

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1 Wireless Pers Commun DOI /s Handover Optimization Algorithm in LTE High-Speed Railway Environment Fang Yang 1 Honggui Deng 1 Fangqing Jiang 1 Xu Deng 1 Springer Science+Business Media New York 2015 Abstract The traditional A3 event-based HO algorithms are mainly designed for the low speed (\30 m/s) networks. That aren t suitable for the high-speed railway scenario which the link quality may deteriorate sharply and the wireless channel environment may become unstable with the increase of velocity. To overcome the disadvantages of the handover algorithms in LTE high-speed railway networks, we proposed a handover optimization algorithm based on statistics, where we not only consider reference signal received power and reference signal received quality at the same time but also the rate of cell resources change. The simulation results show that the proposed algorithm has higher handover success rate and lower handover numbers. Thus unnecessary handover is reduced by up to 47 % and the novel algorithm provides success rate of % higher than the classical A3 algorithm under different conditions of velocity, and greatly improve handover performance. Keywords LTE High-speed railway networks Handover algorithm RSRP RSRQ 1 Introduction Universal terrestrial radio access network long-term evolution (UTRAN LTE), also known as Evolved UTRAN (E-UTRAN), is the 4th generation cellular mobile system that is being developed and specified in 3GPP [1]. LTE uses different radio access technologies for downlink and uplink. Orthogonal frequency-division multiple access (OFDMA) and Single carrier-frequency-division multiple access (SC-FDMA) is used for the downlink and the uplink, respectively. OFDMA provides high spectral efficiency which is very immune to interference and reduces computation complexity in the terminal within larger bandwidths. & Honggui Deng denghonggui@163.com 1 Changsha, Hunan, China

2 F. Yang et al. LTE is designed to improve the capacity, coverage, and the speed of mobile wireless networks over the earlier wireless systems. The requirements for 3GPP LTE include the provision of peak cell data rates up to 100 Mbps in downlink and up to 50 Mbps in uplink under various mobility and network deployment scenarios [2, 3]. As one of the crucial aspects in radio resource management functionality, the handover performance becomes more important, especially for real-time service, since the handover failure rate will increase with the higher moving velocity. An additional requirement is the uninterrupted provision of high data rates and call services. LTE has a very simplified network architecture compared to universal mobile telecommunications system (UMTS). The LTE network architecture is consisted of three elements as shown in Fig. 1 [4]: evolved-nodeb (enodeb), mobile management entity (MME), and serving gateway (S-GW). The enodeb performs all radio interface related functions such as packet scheduling and handover mechanism. MME manages mobility, user equipment (UE) identity, and security parameters. S-GW is a node that terminates the interface towards E-UTRAN. This paper focuses on high-speed railway scenario, which deploys enodeb consisting of base band unit (BBU) and radio remote unit (RRU) along the railway line as demonstrated in Fig. 2 [5]. Several studies have evaluated the handover performance or have proposed optimization methods to improve the traditional handover algorithm in LTE system. In paper [6] and [7], a soft handover algorithm is presented for TD-LTE system in the high-speed railway specialized network which has a much better performance comparing with hard handover algorithm but at the expense of higher implementation complexity. A novel approach to handover management for LTE femtocells is presented in [8], which runs on the femtocell base station, does not require any prior knowledge of the architecture of the building in which it is deployed; thus it is fully consistent with the self-organizing network plug-n-play requirement. Three well known handover algorithms have been optimized in the LTE system [9]. Fig. 1 LTE system architecture

3 Handover Optimization Algorithm in LTE High-Speed Railway Fig. 2 enodeb deployment along the railway line And the simulation results show that this optimization outperforms non-optimized algorithms by minimizing the average number of handover. In [10], the author proposes a new handover strategy between the femtocell and the macrocell for LTE-based networks in hybrid access mode, which consider some parameters for handovers, including interference, velocity, RSS and quality of service (QoS) level. Furthermore, there are some new studies focused on the self-organizing network (SON) and adaptive handover algorithm to increase the robustness of the system performance [11][12]. Since the traditional A3 event-based HO algorithm is mainly designed for the low speed networks (e.g. speed \120 km/h) and the above researches are also mainly evaluated with a low-speed. These studies aren t suitable for the high-speed railway scenario which the link quality may deteriorate sharply and the wireless channel environment may become unstable with the increase of speed. Therefore, to overcome the disadvantages of the handover algorithm in LTE high-speed railway networks we proposed a handover optimization algorithm from statistics, which not only consider RSRP and RSRQ at the same time but also the rate of cell resources change. This paper is organized in five different sections. Section 1 is background and related works of handover algorithm in LTE system. In Sect. 2, the definitions of RSRP, RSSI, RSRQ and the rate of cell resources change are explained in it. In Sect. 3, the classical handover algorithm and the novel handover algorithm are shown. Then in Sect. 4, simulation of the proposed algorithm is shown and the results are analyzed. Finally, a conclusion is drawn in Sect Measurement Report in LTE UE related measurements for the handover are defined in 3GPP specification in [13, 14]. For simplicity in simulation of handover, input measurements are divided into three signals which are RSRP, RSRQ and the rate of cell resources change. Detail of them will be explained below.

4 F. Yang et al. 2.1 Reference Signal Received Power (RSRP) RSRP is measured for a considered cell as the linear average over the power contribution of the resource elements that carry cell-specific reference signal within the considered measurement frequency bandwidth. The cell-specific reference signal can be used for RSRP determination. RSRP can be calculated from serving cell enodeb transmit power (P s ), the path loss value from UE to the serving cell enodeb (PL ue ) and additional shadow fading with a log-normal distribution and a standard deviation of 3 db (L fad ). Following is the equation to calculate RSRP. RSRP s;ue ¼ P s PL ue L fad 2.2 E-UTRA Carrier Received Signal Strength Indicator (RSSI) RSSI is the total received wideband power observed by the UE from all sources, including co-channel serving and non-serving cell, adjacent channel interference, thermal noise and so on. RSSI can be calculated as follows. RSSI ¼ RSRP s;ue þ RSRP int;noise 2.3 Reference Signal Received Quality (RSRQ) RSRQ can be calculated by the ration RSRQ = N 9 RSRP/RSSI, where N is the number of resource block (RB) of the E-UTRA carrier RSSI measurement bandwidth. RSSI includes thermal noise and interference generated in the target enodeb, thus RSRQ can be written as the relation between signal and interference plus noise as follows. RSRQ ¼ N RSRP RSSI 2.4 The Rate of Cell Resources Change The rate of cell resources change reflects the state of cell resources dynamic change, because the size of the available resources can t fully reflect the use of cell resources. If you select the cell when the available resources and the rate of resources change are both greater, handover are likely to cause other users to handover fails or call blocking. In order to reasonably use the cell resources, the smaller rate of cell resource change is selected when the available resources of cells are little difference, which can increase the stability of the system and the handover success rate and reduce the blocking probability of the system. In this paper the rate of cell resource change is the statistics of resources periodically change. It can be calculated as follows. a k pre ¼ x ak þ ð1 xþ a k 1 k where a pre represents predictive value of the rate of cell resources change at k time. a k and a k-1 are the rate of cell resources change at present and at k-1 time, respectively. x is the weighting factor.

5 Handover Optimization Algorithm in LTE High-Speed Railway 3 Handover Algorithm 3.1 A3 Handover Algorithm The A3 handover algorithm [15] in LTE system is a basic but effective handover algorithm consisting of two variables, handover margin (HOM) and time to trigger (TTT) timer. A handover margin is a constant variable that represents the threshold for the difference in RSRP between the serving and the target cell. HOM identifies the most appropriate target cell when the mobile can camp on. A TTT is required for satisfying HOM condition. The handover can only be executed after both the criteria of TTT and HOM are met. Figure 3 shows the basic concept of standard handover algorithm in LTE. Handover is triggered when the triggering condition as following is fulfilled for the entire TTT time duration followed by the handover command sent from the enodeb to the UE. RSRP T [ RSRP S þ HOM where RSRP T and RSRP S are the RSRP received by a UE from the target cell and the serving cell, respectively. TTT starts whenever the RSRP difference received by a UE from the target cell and the serving cell is greater than the specified HOM value. The serving cell starts observing the incoming consecutive time slots after TTT starts. If in any of the incoming consecutive time slots the RSRP difference is less than or equal to HOM, the handover process will be reset, otherwise handover process will be executed which includes the handover decision and the handover command. 3.2 Proposed Handover Optimization Algorithm 3GPP protocol stipulates that measurement reports constantly satisfy the requirement in trigger time, that is the concept of TTT. However, how to achieve the trigger delay is not stipulated in the protocol. Therefore, we can trigger handover from statistics: The UE continually receives measurement report of physical layer, then through the layer 3 filtering to judge whether the measurement report meets trigger criteria. If satisfied, statistical Fig. 3 The classical handover algorithm

6 F. Yang et al. values of satisfied handover are accumulated (N = N?1), then to judge whether N meets Nt which the statistical threshold value is set. If N is satisfied, the measurement report is sent to the enodeb and handover will be triggered. Otherwise, continue to wait for the arrival of the next measurement report. In this paper, on the basis of the traditional handover algorithm, we not only consider RSRP and RSRQ, but also choose the rate of change of cell resources as one of the selected factors. At the same time, from the view of statistics we proposed a handover optimization algorithm. Figure 4 presents a flowchart of the handover optimization algorithm. Step 1 Define the initial parameters. V is the speed of the train. If V is greater than 120 km/h, trigger the new handover algorithm. Otherwise, the conventional handover algorithm based on A3 event is used Step 2 Calculate the value of the criterion function RSRP Ti [ RSRP S? HOM, where i ¼ 1; 2;...; n represents the total number of neighboring cells. When the triggering condition as shown above is fulfilled for the entire TTT time duration, go to Step 4. Otherwise go to Step 3 Step 3 Calculate the value of the criterion function RSRQ Ti [ Thrs, where Thrs represents the RSRQ threshold of adjacent cells. If the condition is satisfied, go to Step 4. Otherwise continue to iterate Step 4 If N [ N t is satisfied, the cell is added to the cell list. Otherwise continue to search for the next cell Step 5 Calculate the rate of neighboring cells resources change. If all neighboring cells are searched and calculated, the cell of smallest rate of resource change is selected to trigger handover. Otherwise continue to iterate 4 Simulation Results and Analysis The simulation work is implemented by Matlab. The network scenario considered assumes a chain structure with 5 cells (controlled by 5 enodebs respectively). The channel model includes channel bandwidth, carrier frequency and path-loss model. The main system simulation parameters are shown in Table 1. UE should deliver its measurement report according to various moving speed, namely, when UE running in a high speed, its HOM should be set smaller, vice versa. In this way, UE s handover action determined by HOM will be triggered strictly according to a same physical distance in reality environment when UE is in different velocities. To simplify the simulation, when the velocity of the UE is 0 30 m/s, m/s and m/s, respectively, HOM is 6, 4 and 2 db. The path loss is calculated with the Hata model [16] and the baseband signal is transmitted through SCME (Spatial Channel Model Extended) channel, shown as Table 1. From the simulation results as shown in Figs. 5 and 6, we can conclude that RSRP doesn t almost change and RSRQ change obviously when SINR value is increased. If only consider RSRP, as shown in Fig. 5, RSRP can well reflect the size of the signal strength. When the channel environment is under relatively good condition, it can be used to make decision. When the channel environment is bad, even if the signal strength is large, the noise is also great, which will cause ping-pong handover and greatly deteriorate system performance. So the algorithm of only considering RSRP is suitable for good channel environment. However, the high-speed railway environment is very complex. We consider

7 Handover Optimization Algorithm in LTE High-Speed Railway Fig. 4 Handover procedure of optimization algorithm

8 F. Yang et al. Table 1 Parameters of simulation Parameters Values Channel bandwidth 10 MHz UE number 1 enodeb number 5 Height of enodeb 35 m Height of UE 3 m Cell radius 1200 m Distance between enodebs 2000 m HOM 2 * 6dB Cell overlapping region 300 m Velocity of UE 0 * 100 m/s Vertical distance between enodeb and railway 200 m Carrier frequency 2000 MHz Path loss model Hata model enodeb initial transmitting power 53 dbm Measurement interval 0.01 s TTT 180 ms Nt RSRP RSRP RSRP SINR(dB) Fig. 5 The relationship between RSRP and SINR both RSRP and RSRQ for harsh channel environment, which the former reflects signal strength and the latter reflects channel environment. Since the velocity of the UE is various dynamically from 0 m/s to 100 m/s during the travel, parameters of handover algorithm should be adjusted accordingly to ensure

9 Handover Optimization Algorithm in LTE High-Speed Railway RSRQ RSRQ RSRQ SINR(dB) Fig. 6 The relationship between RSRQ and SINR Handover Number the Novel Handover Algorithm the A3 Handover Algorithm handover number Velocity of UE(m/s) Fig. 7 HO number versus velocity of UE for different handover algorithms

10 F. Yang et al Handover Success Rate the Novel Handover Algorithm the A3 Handover Algorithm 90 handover success rate(%) Velocity of UE(m/s) Fig. 8 HO success rate versus velocity of UE for different handover algorithms handover success rate and satisfying wireless communication quality in train. From the simulation results as shown in Figs. 7 and 8, compared with the A3 handover algorithm, the handover number of the new algorithm is greatly reduced and handover success rate is significantly increased. Figure 7 shows that the handover number increases with the speed of UE. When the velocity of the UE is 30, 60, 100 m/s, the handover number of the A3 algorithm and the handover number of the novel algorithm are 8, 17, 34 and 5, 11, 18, respectively. Thus we can conclude that the handover number of the new algorithm is reduced compared to the A3 algorithm. Frequent handover may lead to the interruption of business and deteriorate QoS of users. By dynamically adjusting handover parameters in different speeds and simultaneously considering the statistical characteristics to trigger handover, the algorithm can reduce unnecessary handover by up to 47 %. Figure 8 shows the handover success rate comparison between the proposed algorithm and A3 algorithm. The handover success rate decreases when the speed of the UE increases. In the low speed of the UE that is less than 30 m/s, the handover success rate of the two algorithms has little difference. For instance, when the speed is 30 m/s, the handover success rate of the A3 algorithm and the handover success rate of the proposed algorithm are 91.9 and 92.5 %, respectively. If the speed of the UE is greater than 30 m/s, the handover success rate of the optimization algorithm starts to be obviously higher than the A3 algorithm, especially when the speed is higher than 70 m/s. For example, when the speed is 100 m/s, the handover success rate of the A3 algorithm and the handover success rate of the novel algorithm are 57.6 and 71.5 %, respectively. In contrast, the improved algorithm provides % higher success rate than A3 algorithm. This is because we

11 Handover Optimization Algorithm in LTE High-Speed Railway consider not only RSRP and RSRQ but also the rate of change of cell resources, simultaneously consider the statistical characteristics to trigger handover. 5 Conclusions Handover algorithm is one of critical things in mobile communication environment. Seamless handover can guarantee better QoS even UE be moved very fast by taking a high speed railway train. This paper proposes a handover optimization algorithm from the view of statistics to improve handover performance for LTE. The input signals are measured by not only from two enodebs, but are able to receive from more than two enodebs. Based on simulation, the algorithm has the ability to reduce unnecessary handover by up to 47 % and provides % higher success rate than A3 algorithm. The handover optimization algorithm can greatly improve handover performance, especially in high-speed railway environment. Acknowledgments This work was supported by the Natural Science Foundation of Hunan project ringresonator-spectroscopic-based detection mechanism and methods of gas pollution, Project No. 14JJ2013 and Natural Science Foundation of Xinjiang project Detection theory and methods of gas pollution, Project No A035. References 1. Pelcat, M., Aridhi, S., Piat, J., & Nezan, J. F. (2013). 3GPP long term evolution. Physical Layer Multi- Core Prototyping Lecture Notes in Electrical Engineering, 171, GPP TS : version Release 10 (2011). Physical layer: Measurements. 3. 3GPP TS : version Release 10 (2012). Overall description: stage Divya, R. & Hüseyin, A. (2009). 3GPP Long term evolution A technical study. Spring Linlin, L., Wu, M., Shen, J., Ye, J., & He, X. (2012). Optimization of handover algorithms in LTE highspeed railway networks. International Journal of Digital Content Technology and its Applications, 6, Linlin, L., Wu, M., Zhou, P., Di, S. & Ge, S. (2012). The research of soft handover signaling for lte system in high-speed railway environment. (2012) In: International conference on information technology and software engineering (ITSE2012), (Vol. 1), (pp ). 7. Wang, Q., Ren, G. & Tu, J. (2011). A soft handover algorithm for TD-LTE system in high-speed railway scenario. (2011) In: IEEE international conference on signal processing, communications and computing (ICSPCC), (pp. 1 4). 8. Sinclair, N., Harle, D., Glover, I. A., Irvine, J., & Atkinson, R. C. (2013). An advanced som algorithm applied to handover management within LTE. IEEE Transactions on Vehicular Technology, 62(5), Lin, C.-C. & Sandrasegaran, K. (2011). Optimization of handover algorithms in 3GPP long term evolution system. (2011) In: 4th international conference on modeling, simulation and applied optimization (ICMSAO), (pp. 1 5). 10. Wu, S.-J., & Lo, S. K. C. (2011). Handover scheme in lte-based networks with hybrid access mode. JCIT: Journal of Convergence Information Technology, 6(7), Puttonen, J., Kurjenniemi, J. & Alanen, O., (2010). Radio problem detection assisted rescue handover for LTE. In: 2010 IEEE 21st international symposium on personal, indoor and mobile radio communications,( pp ). 12. Alonso-Rubio, J., Ericsson research (2010). self-optimization for handover oscillation control in LTE. In: 2010 IEEE/IFIP network operations and management symposium NOMS 2010, (pp ) GPP TS : version (2007). Evolved universal terrestrial radio access (E-UTRA); Physical layer; Measurements GPP TS : version (2007). Evolved universal terrestrial radio access (E-UTRA); Physical channels and modulation.

F. Yang et al. 15. Lin, C.-C., Sandrasegaran, K & Reeves, S, (2012). Handover algorithm with joint processing in LTEadvanced.

(2006). Principles of wireless communications. ISBN:91-44-03080-0. Fang Yang was born in Anhui, China, in 1988.

Honggui Deng was born in Hunan, China, in 1965. He is a professor and vice president at the School of Physics and Electronics in Central South University.

12 F. Yang et al. 15. Lin, C.-C., Sandrasegaran, K & Reeves, S, (2012). Handover algorithm with joint processing in LTEadvanced. In: th International conference on electrical engineering/electronics, computer, telecommunications and information technology (ECTI-CON), (pp. 1 4). 16. Ahlin, L., Zander, J., & Slimane, B. (2006). Principles of wireless communications. ISBN: Fang Yang was born in Anhui, China, in She is a postgraduate student at the Institute of Physics and Electronics in Central South University. Her research interests deal with wireless communication, source coding and signal processing. Honggui Deng was born in Hunan, China, in He is a professor and vice president at the School of Physics and Electronics in Central South University. His research interests include wireless communication, information theory, source coding, and signal processing. He has published more than 50 academic papers. Fangqing Jiang was born in Hunan, China, in He is a postgraduate student at Institute of Physics and Electronics in Central South University. His research interests include visible communication, information theory, source coding and signal processing.

Handover Optimization Algorithm in LTE High-Speed Railway Xu Deng received the B.S. degree in electrical engineering from Central South University, Changsha, Hunan, in 2009.

13 Handover Optimization Algorithm in LTE High-Speed Railway Xu Deng received the B.S. degree in electrical engineering from Central South University, Changsha, Hunan, in He is currently working toward the M.S. degree at Carleton Univeristy, Ottawa, Canada. His reserach interestes include model order reduction and 5G netwrok.

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