发表机构
Middle East Technical University; Aselsan Inc.（中东理工大学; 阿塞桑公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对有限块长多用户MISO系统中RSMA参数联合优化的AoI最小化难题，提出演员-评论家DRL框架，仿真显示其低SNR和短块长时AoI增益显著，高SNR时性能匹配且在线复杂度低。
AI 中文摘要
本文研究在有限块长（FBL）体制下运行的多用户无线网络中的信息年龄（AoI）最小化问题，这对短状态更新数据包的低延迟传输至关重要。速率拆分多址接入（RSMA）为多用户FBL系统中的干扰管理提供了强大且灵活的框架，但要联合优化其预编码向量、功率分配和速率拆分比率等参数以保证信息新鲜度，会产生解析上难以处理的复杂度。为应对这一挑战，我们提出了一种演员-评论家深度强化学习（DRL）框架，用于学习多用户多输入单输出（MU-MISO）广播信道中的动态资源分配策略。仿真结果表明，所提出的RSMA-RL框架始终比现有基准实现更低的AoI，在低信噪比（SNR）和短块长时可获得显著增益，在高SNR时与基准性能匹配，且执行时仅通过一次神经网络前向传播即可大幅降低在线复杂度。
英文摘要
This letter investigates age-of-information (AoI) minimization in multi-user wireless networks operating in the finite-blocklength (FBL) regime, which is critical for low-latency transmission of short state-update packets. While rate-splitting multiple access (RSMA) provides a powerful and flexible framework for interference management in multi-user FBL systems, the joint optimization of its parameters, such as precoding vectors, power allocation, and rate-splitting ratios, to guarantee information freshness results in analytically intractable complexity. To address this challenge, we propose an actor--critic deep reinforcement learning (DRL) framework to learn dynamic resource-allocation policies in multi-user multiple-input single-output (MU-MISO) broadcast channels. Simulation results show that the proposed RSMA-RL framework achieves consistently lower AoI than the state-of-the-art benchmarks, with substantial gains observed at low signal-to-noise ratio (SNR) and short blocklengths, while matching benchmark performance at high SNR with significantly lower online complexity via a single neural-network forward pass at execution.
DOI：10.1109/LCOMM.2026.3726818
