MAxLM: Multi-Agent Language Model-Based Scheduling and Resource Allocation in MU-MIMO-OFDMA-Enabled Wireless Networks

TL;DR

This paper introduces MAxLM, a multi-agent framework using a pretrained language model to optimize user scheduling and resource allocation in MU-MIMO-OFDMA wireless networks, improving throughput.

Contribution

It presents a novel multi-agent language model-based approach for scheduling and resource allocation in wireless networks, supported by an AI-assisted platform.

Findings

Achieves higher uplink scheduling throughput than benchmark methods.

Effectively handles varying numbers of users and antenna configurations.

Demonstrates the potential of language models in wireless network optimization.

Abstract

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.