关键词: 低地球轨道卫星, 上行链路传输, 非完美信道状态信息, 鲁棒波束成形, 功率控制, 深度强化学习

Abstract: To address the challenge of obtaining prior statistical information on channel errors caused by the highly dynamic environment of Low Earth Orbit (LEO) satellite uplink communications, this paper proposes a robust uplink transmission optimization method. Aiming to minimize the total transmit power subject to User Quality of Service (QoS) and maximum transmit power constraints, a robust transmission optimization problem is formulated for scenarios characterized by inaccurate user angle information. By modeling this non-convex problem as a Markov Decision Process (MDP), a multi-user robust beamforming (BF) and power control algorithm based on Soft Actor-Critic (SAC) is designed. This approach enables the satellite to autonomously learn and adjust beamforming weight vectors and power control strategies through interaction with the environment, without relying on prior information regarding error statistics. Simulation results demonstrate that, compared with the non-robust scheme and the Proximal Policy Optimization (PPO) baseline scheme, the proposed method reduces the required transmit power by an average of 59% and 24%, respectively, under different error scenarios, thereby verifying the robustness and superiority of the proposed method.

Key words: Low Earth Orbit satellite, uplink transmission, imperfect channel state information, robust beamforming, power control, deep reinforcement learning