A Path-Informed Distributed Routing Algorithm for LEO Satellite Direct-to-Device Communication Exploiting Collaborative DRL

Inter-satellite data routing is a fundamental issue that must be addressed to achieve efficient global satellite directto- device communication in the large-scale LEO satellite network (LSN). Existing routing algorithms are inadequate for enabling satellite direct-to-device communication in LSN due to protocol incompatibility, excessive overhead, or performance limitations. Facing these challenges, this paper investigates a novel pathinformed distributed routing algorithm based on collaborative deep reinforcement learning (PI-CDRL) to achieve highperformance and scalable distributed inter-satellite routing in dynamic LSN. We first design a virtual geographic node-assisted routing process to help devices and satellites establish mappings under dynamic conditions. Building on this foundation, the sustained path information following packet transmission completion is leveraged to facilitate collaborative training among satellites, effectively mitigating the sub-optimal performance limitations induced by partial observation and enabling satellite nodes to make accurate real-time routing decisions. Simulation results demonstrate that the successful transmission rate and average latency of PI-CDRL outperform those of baseline distributed routing methods under identical network conditions. Furthermore, our proposed method exhibits robustness in performance as the network scale expands and topology changes due to the ability to fully utilize the path information.