Resource Scheduling in Satellite Networks: A Sparse Representation Based Machine Learning Approach

With the growth of global communication service demand, constructing large-scale satellite networks has become the future development trend for improved system performance. However, due to the high-speed orbit motion of satellites, the connection relationship of network topology (CRNT) is complex and changeable. This phenomenon is particularly pronounced in large-scale satellite networks and the existing representation schemes of CRNT for large-scale satellite networks have high space complexity. Therefore, we explore the sparse characterization of the inter-satellite visibility matrix and propose an integrated sparse space-time resource representation (ISST-RR) scheme to efficiently characterize the satellite network communication resources with low complexity from the dimension of time and space. On the basis of the proposed ISST-RR scheme, we further propose a multi-agent reinforcement learning with sparse representation based resource scheduling (MARLSR-RS) algorithm to obtain the optimal resource scheduling policy. Simulations demonstrate the efficiency of the proposed MARLSR-RS algorithm in terms of communication resource utilization. In addition, we investigate the impact of several typical netwrok parameters, e.g., transmission rate of observation satellites on network performance, which can provide a theoretical guidance for system design.