Joint Optimization of Handoff Control and Resource Block Allocation in Integrated GEO-Multibeam and LEO–UAV Networks

Abstract

The integrated geostationary Earth orbit (GEO)-multibeam and low Earth orbit (LEO)-uncrewed aerial vehicle (UAV) network has emerged as a promising paradigm to enhance the coverage and capacity of terrestrial networks. However, due to the high dynamics of the network and the heterogeneous service demands of user equipments (UEs), joint handoff control and resource block (RB) allocation becomes a critical yet challenging problem. To address this issue, we propose a two-stage dynamic optimization framework, aiming to maximize throughput while minimizing energy consumption and handoff cost. Specifically, the original problem is decomposed into two subproblems: handoff control and RB allocation with fixed handoff decisions. Multi-agent dueling double deep Q-network (MAD3QN) is designed for handoff control, where the centralized training with decentralized execution (CTDE) method is utilized to share intelligent information, and an offline training approach is adopted to support practical deployment. Matching theory (MT) is then applied to determine RB allocation by finding a stable matching between RBs and UEs. Simulation experiments with real-world satellite deployments demonstrate that the proposed algorithm combining MAD3QN and MT effectively converges and significantly outperforms the existing baselines.