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