Stochastic Optimization for Joint User Scheduling and Power Allocation in LEO Satellite Networks

The rapid growth of Low Earth Orbit (LEO) satellite communications has led to an increasing demand for efficient downlink data transmission in highly dynamic network environments. However, stochastic data arrivals, time-varying satellite–ground channels, and long-term Quality-of-Service (QoS) requirements pose significant challenges to joint user scheduling and power allocation in LEO satellite systems. To address these challenges, we study a joint scheduling and power allocation problem that aims to minimize the time-average power consumption while ensuring queue stability and satisfying long-term QoS constraints. We first employ the Lyapunov drift-plus-penalty (DPP) framework to transform the original long-term stochastic optimization problem into a sequence of tractable per-slot subproblems. Each per-slot problem is further decomposed into a user scheduling subproblem and a power allocation subproblem. An alternating optimization (AO) strategy is then developed, where the power allocation subproblem is solved via a successive convex approximation (SCA) method that admits closed-form updates, and the user scheduling subproblem is efficiently handled through a greedy selection mechanism based on instantaneous weighted rates. Extensive simulation results demonstrate the effectiveness and robustness of the proposed algorithm for real-time resource management in LEO satellite networks.