Enhancing On-Demand Massive Connectivity: Cost-Effective Hetero-Granular Resource Allocation for DS2D Communication

With great potential in providing global coverage and real-time service, recently, direct satellite-to-device (DS2D) communication has attracted considerable attention. However, how to effectively utilize the costly satellite resources to satisfy the on-demand massive connectivity remains a huge challenge. This paper proposes a cost-effective hetero-granular resource allocation framework that combines the advantages of ground-based scheduling and spaceborne scheduling. In specific, we aim to optimize both the cell-level and user-level scheduling in a beam-hopping system. The formulated optimization problem is first decomposed into a coarse-grained scheduling problem among cells using historical demand information on the ground station, and a fine-grained scheduling problem among users using real-time service demands on satellite. We solve the mixed-integer non-linear programming problem of coarse-grained scheduling with cross-entropy and quantum particle swarm optimization algorithms to find the global optimum, exploiting the adequate ground-based computational resources. The fine-grained scheduling problem is solved with a generalized-benders-decomposition-based algorithm to accommodate the limited spaceborne resources, which decouples power and bandwidth allocation based on a closed-form solution of optimal dual variables in the primal power allocation problem. Simulation results demonstrate that the proposed method effectively reduces the length of the waiting queue by up to 25.05% compared to the existing methods.