ReToCue: Reliability-Aware Task Offloading and Caching in UAV-Assisted LEO Satellite Edge Computing

The integration of Space-Air-Ground Networks with mobile edge computing represents a key 6G paradigm enabled by advances in low Earth orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and terrestrial networks. However, several critical challenges emerge when handling delay-sensitive services in rescue missions and infrastructure-less scenarios, where terrestrial network layers are absent. In such scenarios, the limited battery capacity of UAVs and satellites can be exhausted quickly. These factors jointly increase the probability of task delay violations and degrade the quality of service. To address these challenges, our work first investigates the joint optimization of edge caching and computation offloading in the UAV-assisted LEO Satellite edge computing (UAV-LEC) system. The primary objective in our formulation is to minimize total energy consumption while maintaining strict service reliability requirements. Given the distributed and stochastic characteristics of the UAVLEC system, we utilize a partially observable Markov decision process (POMDP) to model the system. Furthermore, martingale theory is incorporated to analyze the service reliability bounds of the tandem queue in the proposed framework. Building on these theoretical foundations, we propose a novel asynchronous interactive multi-agent reinforcement learning algorithm to address the distributed caching and computation optimization problem in the UAV-LEC system. Finally, we conduct comprehensive simulations to demonstrate that the proposed algorithm achieves significant improvements compared to the baseline.