MDP-based Satellite-Ground Handover Schemes in 5G Satellite-Terrestrial Integrated Networks

With the rapid development of low earth orbit (LEO) satellites, the fifth generation (5G) satellite-terrestrial integrated network (STIN) has been proposed recently to provide global coverage and universal multi-access. However, the complex communication environments of the 5G STIN, such as dynamic topology and intermittent connectivity, pose challenges in designing handover schemes. This paper investigates the satellite-ground handover problem of the 5G STIN. Initially, we consider a 5G STIN, where multiple LEO satellites and multiple ground base stations are taken as candidate nodes to provide Internet access for a ground user. We establish the satellite channel model and the ground channel model, respectively. To make accurate handover decisions, we consider three attributes, i.e., the received signal-to-noise ratio, the remaining service time, and the remaining load. Then, we analyze the composite attribute value by the entropy weight method and the composite link quality by the adaptive modulation and coding scheme. We formulate a Markov decision process based satellite-ground handover (MDPSGH) problem by maximizing the expected total reward. However, due to the large state space and action space, the MDPSGH problem suffers from the curse of dimensionality. To tackle such a problem, we divide it into a non-terrestrial candidate node (NTCN) selection subproblem and a dimension-reduced MDPSGH subproblem. We propose an entropy weight method-based NTCN selection algorithm and a Bellman optimality equation-based value iteration algorithm to solve these two subproblems. After that, we construct the overall MDPSGH algorithm and analyze its computational complexity as well as convergence. Simulation results show that the proposed MDPSGH algorithm achieves the best performance compared with the existing benchmarks.