Joint Beam Hopping and Resource Allocation for Load Balancing and Interference Avoidance in Multi-LEO Satellite Networks

Multi-beam low earth orbit (LEO) satellites, with their wide coverage, high communication rates, low latency, and flexibility, are essential components in the 5G and 6G eras. However, challenges such as uneven ground user distribution, multi-dimensional resource coupling, inter-beam interference, and dynamic network topology complicate efficient resource management. This paper presents a joint beam hopping and resource allocation (BHRA) scheme within a Digital Twin (DT)empowered multi-beam LEO satellites network. The complex resource management problem is divided into two sub-problems: traffic-satellite allocation and joint multi-satellite BHRA. First, cell traffic is allocated among satellites to balance load using deep reinforcement learning (DRL). Then, Hierarchical Proximal Policy Optimization (HPPO) optimizes multi-satellite beam hopping and resource allocation to meet demand. Extensive simulation results demonstrate that the proposed method reduces satellite load imbalance by approximately 83%, achieves the highest throughput compared to other algorithms, and generalizes well as traffic demand increases.