Two-Step Random Access Optimization for 5G-and-Beyond LEO Satellite Communication System: A TD3-Based MsgA Channel Allocation Strategy

Abstract

For 2-step random access of 5G-and-beyond low earth orbit (LEO) satellite communication system, the large coverage of satellite causes the serious collisions on MsgA channel which reduce the access success probability seriously, and introduces the problem of high dimensionality. To maximize the access success probability of the system, we propose a dynamic MsgA channel allocation (MCA) strategy. The strategy is realized by flexibly pre-configuring the channels mapping relationship on MsgA via the perception of historical access information. To mitigate the curse of dimensionality for the large action space, we establish an improved twin delayed deep deterministic policy gradient (TD3) based deep reinforcement learning (DRL) model for the strategy. Simulation result shows that the proposed strategy can increase the access success probability of the system by 39.12%.