User Grouping-Based Beam Handover Scheme with Load-Balancing for LEO Satellite Networks

With the rapid growth in demand for Internet services, multi-beam low earth orbit(LEO) satellite networks have received increasing attention in recent years. However, due to the fast movement of LEO satellites, users need to frequently handover between beams. Such handovers can significantly degrade network performance and increase the handover failure rate. In light of these challenges, this paper proposes a user grouping-based beam handover scheme that aims to reduce handover frequency and improve satellite handover decision efficiency. Specifically, we divide users with similar handover moments and similar geographic into groups, and then design separate strategies according to different handover types to achieve load-balancing of the network. For frequent intra-satellite beam handover, because a single beam can only provide a short service time, we design a single-objective function based on the weighted entropy method for to decide handover beam. For the more complex inter-satellite beam handover that requires balanced channel capacity, a Markov decision process(MDP) is further introduced to characterize the state of user groups, and an improved deep reinforcement learning(DRL) algorithm that reduces the dimensionality of the state space is developed to decide handover satellite and handover beam. The simulation results indicate that the proposed scheme can effectively improve the handover success rate and optimize the network performance, especially when the number of users increases.