关键词: 下一代铁路通信网络, 网络优化, 网络覆盖, 强化学习

Abstract: In LTE-R（Long Term Evolution for Railway） communication network， one major factor influencing the coverage is related to the antenna tilt angle. By adjusting antenna tilt， signal reception of user terminal within a cell can be effectively improved. To solve the coverage optimization problem of Self-Organizing Network （SON） proposed in the 3rd Generation on Partnership Project （3GPP）， a method using reinforcement learning algorithm for optimizing the title angle of base station antenna is proposed. Firstly， with the help of the electromagnetic radiation model of the base station antenna， we build a signal coverage optimization model that takes the antenna transmission gain as the objective function. Secondly， we transform the coverage optimization problem into the maximum benefit problem by employing reinforcement learning algorithm， and then accomplish the coverage self-optimization of mobile terminal by finding the best angle value in the range space of the antenna tilt angle. Finally， we have carried out simulation and field verification. The results show that the solution increased 4.11 percentage points compared to no antenna tilt optimization， and increased 3.59 percentage points compared to the method based on the particle swarm algorithm. The proposed method is suitable for the self-optimization of base station antenna tilt angle covered by LTE-R communication network.

Key words: LTE-R（Long Term Evolution for Railway）, network optimization, network coverage, reinforcement learning