Research onself-organization of wireless network based on reinforcement learning

Traditional wireless communication technologies are gradually unable to meet the increasingly complex requirements of the 5G system. The technologies related to self-organizing network (SON) provide scalable solutions for network intelligent management. The implementation of reinforcement learning (RL) algorithms in SON illustrates its capability on network recognition and optimization. In this paper, three modules in SON and their applications were introduced, which were self-configuration, self-optimization and self-healing. Then, related RL algorithms from different criteria were evaluated, such as scalability, complexity, robustness and convergence. Finally, this research was summarized by analyzing the challenges associated with the application of RL in future wireless networks and identifying the directions for future research.