Reinforcement Learning Empowered Massive IoT Access in LEO-based Non-Terrestrial Networks

Low-Earth orbit (LEO) satellite (SAT) networks exhibit ultra-wide coverage under time-varying SAT network topology. Such wide coverage makes the LEO SAT network support the massive IoT, however, such massive access put existing multiple access protocols ill-suited. To overcome this issue, in this paper, we propose a novel contention-based random access solution for massive IoT in LEO SAT networks. Not only showing the performance of our proposed approach (see, Table II), but we also discuss the issue of scalability of deep reinforcement learning (DRL) by showing the convergence behavior (see, Table III and IV).