This study explores the application of the rate-splitting multiple access (RSMA) technique, vital for interference mitigation in modern communication systems. It investigates the use of precoding methods in RSMA, especially in complex multiple-antenna interference channels, employing deep reinforcement learning. The aim is to optimize precoders and power allocation for common and private data streams involving multiple decision-makers. A multi-agent deep deterministic policy gradient (MADDPG) framework is employed to address this complexity, where decentralized agents collectively learn to optimize actions in a continuous policy space. We also explore the challenges posed by imperfect channel side information at the transmitter. Additionally, decoding order estimation is addressed to determine the optimal decoding sequence for common and private data sequences. Simulation results demonstrate the effectiveness of the proposed RSMA method based on MADDPG, achieving the upper bound in single-antenna scenarios and closely approaching theoretical limits in multi-antenna scenarios. Comparative analysis shows superiority over other techniques such as MADDPG without rate-splitting, maximal ratio transmission (MRT), zero-forcing (ZF), and leakage-based precoding methods. These findings highlight the potential of deep reinforcement learning-driven RSMA in reducing interference and enhancing system performance in communication systems.

翻译：本研究探讨了速率分割多址接入技术在干扰抑制中的关键应用，该技术对现代通信系统至关重要。论文重点研究了速率分割多址接入中预编码方法的应用，特别是在复杂的多天线干扰信道环境下，采用深度强化学习进行优化。其目标是在涉及多个决策者的场景下，优化公共数据流与私有数据流的预编码器和功率分配。我们采用多智能体深度确定性策略梯度框架应对这一复杂性，其中分散的智能体在连续策略空间中协同学习优化行动。同时研究了发射端非完美信道状态信息带来的挑战。此外，还解决了解码顺序估计问题，以确定公共与私有数据序列的最优解码顺序。仿真结果表明，基于多智能体深度确定性策略梯度的速率分割多址接入方法具有有效性：在单天线场景中可达到性能上界，在多天线场景中能紧密趋近理论极限。对比分析显示，该方法在性能上优于无速率分割的多智能体深度确定性策略梯度、最大比传输、迫零及基于泄漏的预编码等传统技术。这些发现凸显了深度强化学习驱动的速率分割多址接入在降低通信系统干扰、提升系统性能方面的潜力。