A Parameter Optimization Method for LTE-R Handover Based on Reinforcement Learning

With the rapid development of China's high-speed railway, the traditional railway wireless communication system technology has been difficult adapting to the requirement, and is gradually being replaced by the LTE-R communication system. However, the LTE-R handover parameters selection mainly depends on historical experience, and there is no established theory or method. Therefore, research on adaptive optimization methods of handover parameters at different speeds has great significance on improving the handover performance of LTE-R systems for high-speed railway wireless communications. Combined with the environment adaptive ability of reinforcement learning, this paper proposes an adaptive optimization method based on the Q-Learning algorithm to achieve real-time estimation of the handover parameters of the LTE-R system. And based on these, we establish a performance situation map for handover parameters for different speeds, and use the generated "handover situation" to provide a basis for mobile users accessing opportunistic channels during the handover process, thereby improving handover performance. Our simulation results show that the optimized handover parameters can significantly improve the handover performance of the LTE-R system.