Joint optimization of user association and resource allocation in cache-enabled terrestrial-satellite integrating network

Although low earth orbit (LEO) satellites can provide high-capacity backhaul to serve the terrestrial network, the performance of terrestrial-satellite communication systems is critically influenced by the coupling of user association and resource allocation in this integrating system, where user association includes small-cell base station (SBS)-user association and SBS-satellite association. In this work, we consider a cache-enabled terrestrial-satellite integrating network, in which LEO satellites provide backhaul for cache-enabled SBSs to serve ground users. Targeting at maximizing the downlink sum rate of the system and the number of accessed ground users, we formulate an optimization problem where user association and resource allocation of both terrestrial and satellite networks are joint optimized. Owing to the coupling relationship and integer programming nature of this optimization problem, we use Lagrangian relaxation to decouple and decompose it into two subproblems. We propose a user-division matching (UDM) algorithm by dividing all users into multiple user groups, which skillfully solves the first subproblem with multi-objectives. Afterward, to depict the nature of multi-connectivity sufficiently, the second subproblem is converted into a many-to-one matching game and solved by a modified Gale-Shapely (MGS) algorithm, which is highly efficient for different satellite constellations. Simulation results demonstrate the proposed algorithms can significantly improve the downlink sum rate of the system by 28.5–120.7 compared to the benchmark algorithms in the typical settings and balance the tradeoff between the downlink sum rate of the system and the number of accessed ground users. Moreover, it also shows that < 1% system performance loss can be obtained by the proposed method compared to the optimal solution.