Cluster Hopping in Multi-beam Satellite Communication Systems Using Deep Reinforcement Learning

Beam hopping method has become a promising and significant technology in the next generation of high throughput satellite (HTS) systems. Using deep reinforcement learning (DRL), this research suggests a unique method for improving precoded cluster hopping in multi-beam geostationary orbit (GEO) satellite communication systems. Choosing the optimum beam clusters for transmission based on the current channel circumstances, our goal is to increase the effectiveness and capacity of satellite communication systems. Our simulation results based on DRL yields better performance when compared to other techniques.