ShellMean-MAPPO: A Conflict-Aware MARL Framework for Downlink Resource Allocation in Multi-Shell LEO Satellite Networks
Low Earth orbit (LEO) satellite communication, able to provide ubiquitous and continuous connectivity, has become a vital component of future sixth-generation global networks. To further improve service continuity and support spatially non-uniform traffic demand, LEO satellite systems are evolving toward dense multi-shell constellations, where overlapping coverage enables multiple satellites to serve the same traffic region. However, under limited onboard beams, spectrum, and power budgets, such overlap may lead multiple beams to request the same traffic cell, causing duplicate beam-cell requests (DBRs) in downlink scheduling. To address this challenge, we propose a structured multi-agent reinforcement learning (MARL) framework based on multi-agent proximal policy optimization (MAPPO), termed ShellMean-MAPPO, for downlink resource allocation with explicit conflict resolution. Specifically, we first formulate a long-term scheduling problem that separates pre-resolution beam-cell requests from post-resolution retained transmissions, enabling DBRs to be modeled together with resource block (RB) chunk and power allocation. By leveraging compact local information and per-shell summaries, a typed encoder is then designed to capture heterogeneous service and contention states without relying on a full global map. Furthermore, an autoregressive policy is developed to generate beam-cell, RB chunk, and power-share decisions in accordance with the downlink scheduling sequence, while a deterministic replay-based post-resolution credit mechanism transforms team outcomes into per-beam training signals. Extensive simulation results validate the effectiveness of ShellMean-MAPPO, and demonstrate its advantages over representative MARL schemes in terms of scheduling performance and conflict mitigation.