Multi-Agent DRL for User Association and Power Control in Terrestrial-Satellite Network

In the past few years, satellite communications have greatly affected our daily lives. Because the resources of terrestrial-satellite network are limited, how to allocate resources of terrestrial-satellite network through effective methods have become a major challenge. We propose a framework for energy efficiency optimization of terrestrial-satellite network based on Non-orthogonal multiple access (NOMA). In our framework, we adopt a multi-agent deep deterministic policy gradient (MADDPG) method to obtain the maximum energy efficiency by user association and power control. Finally, the simulation results show that the proposed method has better optimization performance compared with the traditional single-agent deep reinforcement learning algorithm and can efficiently solve the problems of user association and power control in the integrated terrestrial-satellite network.