基于IGWO-RBF的LTE-R切换算法研究  

Research on LTE-R Handover Algorithm Based on IGWO-RBF

作　　者：苏佳丽 伍忠东[1] 丁龙斌 刘菲菲 SU Jiali;WU Zhongdong;DING Longbin;LIU Feifei(Information Security Laboratory,School of Electronic and Information Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China)

机构地区：[1]兰州交通大学,电子与信息工程学院,信息安全实验室,兰州730070

出　　处：《计算机工程与应用》2020年第8期74-80,共7页Computer Engineering and Applications

基　　金：中国铁路总公司科技研究开发计划重大课题(No.2017X013-A)。

摘　　要：针对高速铁路LTE-R越区切换中,A3事件下的越区切换算法容易出现乒乓效应(PPE)和无线链路连接失败(WLF)的问题,提出了粒子群优化(PSO)灰狼算法改进的RBF神经网络(IGWO-RBF)的越区切换优化算法。该算法采集大量列车以不同速度(0~100 m/s)运行在特定环境中时切换成功率高的切换迟滞门限(Hys)和触发延迟时间(TTT)参数集,送入改进的RBF神经网络,训练完成后得到不同速度下的Hys和TTT的拟合曲线。根据列车接收到的参考信号接收质量(RSRQ),加入自矫正项对Hys和TTT进行二次优化调整。在matlab上进行仿真实验,结果表明提出的算法减小了掉话率和乒乓切换率,提高了列车在高速环境下的切换成功率及鲁棒性。In the LTE-R handover of high-speed railway,the A3 event-based handoff algorithm is prone to the problem of Ping-Pong Effect(PPE)and Wireless Link connection Failure(WLF),the Particle Swarm Optimization(PSO)gray wolf algorithm,the improved RBF neural network(IGWO-RBF)handover optimization algorithm is proposed.The algorithm firstly collects Hys and TTT parameter sets with high success rate when a large number of trains run in different environments at different speeds(0~100 m/s),and sends them into the improved RBF neural network.After training,they get Hys at different speeds.The fitting curve with TTT is then based on the Received Signal Received Quality(RSRQ)of the train,and the self-correcting term is added to perform secondary optimization adjustments on Hys and TTT.Finally,the simulation experiments on matlab show that the proposed algorithm reduces the call drop rate and ping-pong switching rate,and improves the switching success rate and robustness of the train in high-speed operation environment.

关 键 词：LTE技术 高速环境 越区切换 A3事件 改进灰狼优化的RBF神经网络(IGWO-RBF) 切换成功率 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]