Hierarchical Cross-Domain Satellite Resource Management: An Intelligent Collaboration Perspective

The expansion of satellite applications induces the formation of the multi-domain satellite system (MDSS) containing multiple domains with specific applications such as earth resource remote sensing and the Internet of remote things. Resource management is pivotal in enhancing the scheduling capability of the MDSS. However, this is challenging since the dynamic buffer space and communication opportunity, as well as the uncertain data traffic, exacerbate the difficulty of matching satellite resources with data traffic. Moreover, the coexistence of resource competition and collaboration across domains aggravates the dilemma of cross-domain collaboration. In this paper, we propose a hierarchical cross-domain collaborative resource management framework that can flexibly allocate the mission data through local intra-domain and global cross-domain scheduling. Then, to match the uncertain demands of missions with dynamic and limited resources, we propose a multi-agent reinforcement learning-based resource management method to guide collaboration for multi-satellite data carry-forward in a domain. Further, considering resource competition and collaboration in MDSS, we propose a domain-satellite nested matching game data scheduling algorithm to achieve pair-wise stable collaboration of cross-domain satellites. The simulation results indicate that the proposed algorithm improves the amount of offloaded data by 64.4% and 12.7% compared to the non-collaborative and the non-cross-domain schemes, respectively.