Compared with the terrestrial networks (TN), which can only support limited coverage areas, low-earth orbit (LEO) satellites can provide seamless global coverage and high survivability in case of emergencies. Nevertheless, the swift movement of the LEO satellites poses a challenge: frequent handovers are inevitable, compromising the quality of service (QoS) of users and leading to discontinuous connectivity. Moreover, considering LEO satellite connectivity for different flying vehicles (FVs) when coexisting with ground terminals, an efficient satellite handover decision control and mobility management strategy is required to reduce the number of handovers and allocate resources that align with different users' requirements. In this paper, a novel distributed satellite handover strategy based on Multi-Agent Reinforcement Learning (MARL) and game theory named Nash-SAC has been proposed to solve these problems. From the simulation results, the Nash-SAC-based handover strategy can effectively reduce the handovers by over 16 percent and the blocking rate by over 18 percent, outperforming local benchmarks such as traditional Q-learning. It also greatly improves the network utility used to quantify the performance of the whole system by up to 48 percent and caters to different users requirements, providing reliable and robust connectivity for both FVs and ground terminals.

翻译：与仅能支持有限覆盖区域的地面网络（TN）相比，低地球轨道（LEO）卫星可提供无缝全球覆盖，并在紧急情况下具备高生存能力。然而，LEO卫星的高速移动带来了挑战：频繁的星间切换不可避免，这会损害用户的服务质量（QoS）并导致连接中断。此外，当考虑地面终端与不同类型的飞行器（FV）共同存在时的LEO卫星连接问题时，需要一种高效的卫星切换决策控制与移动管理策略，以减少切换次数并分配符合不同用户需求的资源。本文提出了一种基于多智能体强化学习（MARL）与博弈论的新型分布式卫星切换策略——Nash-SAC，以解决上述问题。仿真结果表明，基于Nash-SAC的切换策略可有效减少超过16%的切换次数，并降低超过18%的阻塞率，优于传统Q-learning等本地基准方法。同时，该策略能显著提升用于量化系统整体性能的网络效用（最高提升48%），并满足不同用户需求，为飞行器和地面终端提供可靠稳健的连接。