Battery Lifetime Extension in Heterogeneous Satellite Edge Computing: A Lyapunov-DRL Approach

Low Earth Orbit (LEO) satellite mobile edge computing (SMEC) has emerged as a pivotal technology for delivering low-delay communication and computational services to under-served regions. However, ensuring sustainable operation of SMEC systems remains challenging due to limited onboard energy and battery degradation, which is critically influenced by the Depth of Discharge (DoD) of battery. This paper investigates the collaborative DoD optimization problem in heterogeneous SMEC, formulating it as a long-term stochastic optimization aimed at minimizing DoD while maintaining system stability under dynamic energy supply and stochastic task arrivals. To address this problem, we propose a Lyapunov-guided optimization framework that integrates Lyapunov optimization with a multi-agent deep reinforcement learning algorithm. Specifically, we employ Lyapunov optimization to transform the long-term objective into a sequence of per-time-slot subproblems. Subsequently, given the high complexity of minimizing the derived Lyapunov drift function directly, we utilize a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm to solve these subproblems. This approach leverages MADDPG’s proven capability in handling complex sequential decision-making processes while enabling real-time adaptive optimization in dynamic environments. Extensive simulations demonstrate that the proposed framework achieves significant reduction in DoD, bounded delay, and stable queue dynamics in diverse scenarios.