Dynamic Channel Reservation Strategy Based on DQN Algorithm for Multi-Service LEO Satellite Communication System

In this letter, a dynamic channel reservation (DCR) strategy based on deep Q network (DQN) is proposed for multi-service low earth orbit (LEO) satellite communication system. We develop a novel modeling method to represent DCR problem of multiple services as a reinforcement learning (RL) task. Based on this model, we calculate the influence of current channel allocation results on future environment and take it as one of the factors for channel allocation decision. Moreover, a corresponding neural network is designed as a decision evaluator to provide an end to end mapping of decision to its value, which effectively avoids the influence of artificial preconditions. Simulation results show that the proposed strategy can improve the overall quality of service (QOS) of the system.