Joint Power Control and Channel Allocation for Interference Mitigation Based on Reinforcement Learning

In dense Wireless Local Area Networks (WLANs), high-density Access Points (APs) bring severe interference that seriously affects the experience of users, resulting in lower throughput and poor connection quality. Due to the heavy computation workload raised by the sizable networking systems and the difficulty in estimating instantaneous Channel State Information (CSI), existing works are hard to solve interference problem. In this paper, we propose a Joint Power control and Channel allocation based on Reinforcement Learning (JPCRL) algorithm combining with statistical CSI to reduce interference adaptively. Firstly, we analyze the correlation between transmit power and channel, and formulate the interference optimization as a Mixed Integer Nonlinear Programming (MINLP) problem. Secondly, we use the statistical CSI method to take the power and channel state as the state and action space, the overall throughput increment as the reward function of Q-learning, and obtain the optimal joint optimization strategy through off-line training. Moreover, for the periodic reinforcement learning process leading to resource consumption, we design an event-driven mechanism of Q-learning, which triggers online learning to refresh the optimal policy by event-driven condition and the consumption of computing resources can be reduced. The evaluation results show that the proposed algorithm can effectively improve the throughput compared with the existing scheme.