Heterogeneous Wireless Network Resource Management Algorithm Based on Reinforcement Learning - Chenwei Feng; Jiangnan Yuan (Dianxin kexue)
In order to make full use of the resources of all kinds of wireless network，the integration of heterogeneous network is necessary.However，when it comes to the heterogeneous network integration，the problems of call request access control and resource management emerge.A reinforcement-learning-based algorithm was presented for heterogeneous wireless network resource management.D2D （device-to-device）communication was introduced into the proposed algorithm and the appropriate network for access could be selected according to different traffic types，terminal mobility，network load status and so on.Meanwhile，to reduce the storage requirement，the neural network technology was used to solve the problem of continuous state space.Simulation results show that the proposed algorithm has an efficient learning ability to achieve autonomous radio resource management，which effectively improves the spectrum utility and reduces the blocking probability.