Research on LTE-R handover algorithm based on improved grey-Markov model
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School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, P. R. China

Clc Number:

U285.2

Fund Project:

Supported by National Natural Science Foundation of China (61661027).

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    Abstract:

    During the handover process of high-speed trains, the reference signal receiving power (RSRP) experiences fluctuations caused by path loss and terrain, leading to issues with the traditional A3 event-based handover decision method, such as ping-pong occurrences and a decrease in handover success rate. To address this problem, a handover algorithm based on an improved gray-Markov model is proposed. This algorithm processes and predicts the received power of the reference signal using an enhanced grey-Markov model. The handover process then utilizes the prediction results as the basis for the handover decision based on the preloading method. Simulation results demonstrate that the improved handover algorithm significantly reduces the fluctuation of RSRP values received by the train, lowers the probability of ping-pong handovers, and effectively improves the handover success rate.

    Reference
    [1] 杜涛, 陈永刚, 李德威. 基于实时动态迟滞的LTE-R切换算法优化研究[J]. 铁道标准设计, 2018, 62(4): 176-180.Du T, Chen Y G, Li D W. Research on optimization of LTE-R handover algorithm based on real-time dynamic hysteresis[J]. Railway Standard Design, 2018, 62(4): 176-180.(in Chinese)
    [2] Li D H, Li D P, Xu Y Y. Machine learning based handover performance improvement for LTE-R[C]//2019 IEEE International Conference on Consumer Electronics. IEEE, 2020: 1-2.
    [3] 苏佳丽, 伍忠东, 丁龙斌, 等. 基于RBF神经网络的LTE-R切换算法优化[J]. 计算机工程, 2019, 45(10): 110-115, 121.Su J L, Wu Z D, Ding L B, et al. Optimization of LTE-R handover algorithm based on RBF neural network[J]. Computer Engineering, 2019, 45(10): 110-115, 121.(in Chinese)
    [4] Bang J H, Oh S, Kang K, et al. A Bayesian regression based LTE-R handover decision algorithm for high-speed railway systems[J]. IEEE Transactions on Vehicular Technology, 2019, 68(10): 10160-10173.
    [5] Cai X Q, Wu C, Sheng J, et al. A parameter optimization method for LTE-R handover based on reinforcement learning[C]//2020 International Wireless Communications and Mobile Computing (IWCMC). June 15-19, 2020, Limassol, Cyprus. IEEE, 2020: 1216-1221.
    [6] Wang Q W, Ren G L, Tu J. A soft handover algorithm for TD-LTE system in high-speed railway scenario[C]//2011 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC). September 14-16, 2011, Xi'an, China. IEEE, 2011: 1-4.
    [7] 席皓哲, 王瑞峰. 基于天线分组的高铁大规模多输入多输出自适应波束赋形方案[J]. 计算机应用, 2019, 39(3): 839-844.Xi H Z, Wang R F. Adaptive beamforming scheme of massive MIMO based on antenna grouping in high-speed railway environment[J]. Journal of Computer Applications, 2019, 39(3): 839-844.(in Chinese)
    [8] Zhao J H, Liu Y Y, Wang C Y, et al. High-speed based adaptive beamforming handover scheme in LTE-R[J]. IET Communications, 2018, 12(10): 1215-1222.
    [9] 温强, 李积英, 杨永红, 等. 基于灰色预测的LTE-R越区切换算法[J]. 激光与光电子学进展, 2020, 57(19): 63-71.Wen Q, Li J Y, Yang Y H, et al. LTE-R handover algorithm based on grey prediction[J]. Laser & Optoelectronics Progress, 2020, 57(19): 63-71.(in Chinese)
    [10] Hsieh P J, Lin W S, Lin K H, et al. Dual-connectivity prevenient handover scheme in control/user-plane split networks[J]. IEEE Transactions on Vehicular Technology, 2018, 67(4): 3545-3560.
    [11] 孙宇彤. LTE教程: 原理与实现[M]. 北京: 电子工业出版社, 2017.Sun Y T. LTE Course: Principle and Implementation[M]. Beijing: Publishing House of Electronics Industry, 2017.(in Chinese)
    [12] 米根锁, 马硕梅. 基于速度触发的提前切换算法在LTE-R中的应用研究[J]. 电子与信息学报, 2015, 37(12): 2852-2857.Mi G S, Ma S M. Advance trigger handover algorithm based on the speed in LTE-R[J]. Journal of Electronics & Information Technology, 2015, 37(12): 2852-2857.(in Chinese)
    [13] 滕云龙, 师奕兵, 郑植. 接收机钟差灰色马尔可夫预测模型研究[J]. 电子科技大学学报, 2011, 40(2): 242-245.Teng Y L, Shi Y B, Zheng Z. Research on grey Markov model for predicting receiver clock bias[J]. Journal of University of Electronic Science and Technology of China, 2011, 40(2): 242-245.(in Chinese)
    [14] 俞树荣, 韩竣羽, 李淑欣. 利用灰色马尔可夫模型预测腐蚀管道寿命[J]. 机械强度, 2016, 38(4): 850-856.Yu S R, Han J Y, Li S X. Predictive grey Markov chain model for pitting corrosion in piplines[J]. Journal of Mechanical Strength, 2016, 38(4): 850-856.(in Chinese)
    [15] 马创, 袁野, 尤海生. 基于灰色: 马尔可夫模型的农产品产量预测方法[J]. 计算机科学, 2020, 47(S1): 535-539.Ma C, Yuan Y, You H S. Agricultural product output forecasting method based on grey-markov model[J]. Computer Science, 2020, 47(S1): 535-539.(in Chinese)
    [16] 闻映红. 电波传播理论[M]. 北京: 机械工业出版社, 2013.Wen Y H. Radio wave propagation theory[M]. Beijing: China Machine Press, 2013.(in Chinese)
    [17] 陈永刚, 李德威, 张彩珍. 一种基于速度的LTE-R越区切换优化算法[J]. 铁道学报, 2017, 39(7): 67-72.Chen Y G, Li D W, Zhang C Z. A speed-based cross-zone handoff optimization algorithm of LTE-R[J]. Journal of the China Railway Society, 2017, 39(7): 67-72.(in Chinese)
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王瑞峰,范文静.基于改进灰色-马尔可夫模型的LTE-R越区切换算法[J].重庆大学学报,2023,46(7):44~52

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  • Received:April 12,2022
  • Online: August 02,2023
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