Foundation Model Enhanced Multiple Access in Heterogeneous Networks

Next-generation multiple access techniques are crucial for providing low-latency and highly efficient data transmission services. Recently, Deep Reinforcement Learning (DRL) has emerged as a prevalent approach in the multiple access domain, aiming to facilitate user coordination and enhance transmission efficiency. However, current DRL approaches face challenges, including limited generalization ability, low sample efficiency, and the complexities associated with Partially Observable Markov Decision Processes (POMDP), which hinder their application in heterogeneous networks with varying numbers of nodes and configurations. In this paper, we propose a foundation model-based multiple access (FMA) algorithm. To address severe POMDP and sample inefficiency issues, we decompose the multiple access problem into two parts: a transmission decision part and a configuration estimation part. We leverage the strong generalization and inference capabilities of the foundation model, utilizing a Deep Learning (DL) approach instead of DRL for training, and adopt the Low-Rank Adaptation (LoRA) technique to fine-tune the foundation model for downstream multiple access tasks. Simulation results demonstrate that: 1) through the decomposition, the FMA approach exhibits sufficient generalization and inference abilities to adapt to various scenarios with various protocols, configurations, and numbers of heterogeneous nodes; 2) by incorporating expert knowledge, the FMA approach can significantly enhance network performance while ensuring certain fairness requirement for heterogeneous nodes.