Wireless networks support multi-user (MU) communication with multiple-input multiple-output (MIMO) and orthogonal frequency-division multiple access (OFDMA) technologies. In the joint MU-MIMO-OFDMA-enabled transmission mode, network throughput can be significantly increased by effectively utilizing the multi-channel resources to schedule numerous wireless users/stations (STAs) simultaneously. In this paper, we study ways to optimize the user scheduling and resource allocation (SRA) for the UL scheduled access (UL-SA) of a joint MU-MIMO-OFDMA-enabled wireless local area network (WLAN). In particular, we propose a multi-agent (MA) framework that utilizes an openly available pretrained small/medium-sized Language Model (xLM) to perform SRA for the UL-SA. To facilitate autonomous SRA using our proposed technique, we introduce the AI-assisted Wireless Systems Engineering and Research (WiSER) platform. We evaluate the performance of MAxLM-optimized SRA for network scenarios with a varying number of STAs and antenna settings on the WLAN Access Point. Numerical results confirm that our proposed technique achieves higher UL-SA throughput than the benchmark techniques.

翻译：无线网络采用多输入多输出（MIMO）和正交频分多址（OFDMA）技术支持多用户（MU）通信。在联合MU-MIMO-OFDMA传输模式下，通过有效利用多信道资源同时调度大量无线用户/站点（STA），可显著提升网络吞吐量。本文研究面向联合MU-MIMO-OFDMA无线局域网（WLAN）上行调度接入（UL-SA）的用户调度与资源分配（SRA）优化方法。具体而言，我们提出一种利用公开可用的预训练中小型语言模型（xLM）实现UL-SA中SRA的多智能体（MA）框架。为支持所提技术的自主化SRA，我们引入人工智能辅助无线系统工程与研究（WiSER）平台。针对不同STA数量与WLAN接入点天线配置的网络场景，我们评估了MAxLM优化SRA的性能。数值结果证实，所提技术获得的UL-SA吞吐量优于基准技术。