Local search resource allocation algorithm for space-based backbone network in Deep Reinforcement Learning method

Abstract

With the evolution of Space-based backbone networks, the demand for enhanced efficiency and stability in network resource allocation has become increasingly critical, presenting a substantial challenge to conventional allocation methods. In response, we introduce an innovative resource allocation algorithm for space-based backbone networks. This algorithm represents a synergistic fusion of Deep Reinforcement Learning (DRL) and Local Search (LS) methodologies. It is specifically designed to reduce the extensive training duration associated with traditional policy networks, a crucial aspect in assuring optimal service quality. Our algorithm is structured within a two-stage framework that seamlessly integrates DRL and LS. A distinctive feature of our approach is the incorporation of link reliability into the algorithmic design. This element is meticulously tailored to address the dynamic and heterogeneous nature of space-based networks, ensuring effective resource management. The effectiveness of our approach is substantiated through extensive simulation results. These results demonstrate that the integration of DRL with LS not only enhances training efficiency but also exhibits significant improvements in resource allocation outcomes. Our work represents a noteworthy contribution to the development of practical optimization strategies in space-based networks, merging DRL with traditional methodologies for improved performance.