Multi-Task Reinforcement Learning-Based Multiple Access for Dynamic Wireless Networks

Zhenyu Chen, Xinghua Sun, Yili Jin, Fangxin Wang

Abstract: With the rapid development of emergent applications, wireless networks require the provision of high throughput. Meanwhile, wireless scenarios exhibit highly dynamic characteristics, involving frequent changes in the network scale and traffic. To satisfy the high demand for new applications in dynamic wireless scenarios, a novel medium access control (MAC) protocol is required to allow stations to access the channel with high efficiency and adaptability. Based on multi-agent reinforcement learning (MARL), we propose a new MAC protocol, Multi-task Transformer-based Multiple Access (MTMA). Multi-task learning is applied to train a single actor to adapt to multiple wireless environments simultaneously. To improve the scalability, we propose a transformer-based critic network, which can scale to different wireless scenarios. Moreover, a novel network called “Generalization for N (Gen-N)” network is proposed to enhance the generalization ability. We conduct simulation experiments to demonstrate that MTMA: 1) achieves over 95% of upper bound of throughput while maximizing the fairness performance; 2) outperforms classic MAC protocol and MARL-based baselines in scenarios with saturated and light traffic; 3) can adapt to environmental changes quickly in dynamic scenarios; 4) can generalize to unseen scenarios during training. Finally, the ablation experiments are conducted to evaluate the effectiveness of components used in MTMA.

External IDs:dblp:journals/tmc/ChenSJW25