计算机工程与应用 ›› 2020, Vol. 56 ›› Issue (8): 74-80.DOI: 10.3778/j.issn.1002-8331.1812-0183

• 网络、通信与安全 • 上一篇    下一篇

基于IGWO-RBF的LTE-R切换算法研究

苏佳丽,伍忠东,丁龙斌,刘菲菲   

  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
  • 出版日期:2020-04-15 发布日期:2020-04-14

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

SU Jiali, WU Zhongdong, DING Longbin, LIU Feifei   

  1. Information Security Laboratory, School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
  • Online:2020-04-15 Published:2020-04-14

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。

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

Abstract:

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.

Key words: LTE-R technology, high-speed environment, handover, A3 event, Improved Gray Wolf Optimizer RBF neural network(IGWO-RBF), switching success rate