  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.
