This paper focuses on spectrum sharing in heterogeneous wireless networks, where nodes with different Media Access Control (MAC) protocols to transmit data packets to a common access point over a shared wireless channel. While previous studies have proposed Deep Reinforcement Learning (DRL)-based multiple access protocols tailored to specific scenarios, these approaches are limited by their inability to generalize across diverse environments, often requiring time-consuming retraining. To address this issue, we introduce Generalizable Multiple Access (GMA), a novel Meta-Reinforcement Learning (meta-RL)-based MAC protocol designed for rapid adaptation across heterogeneous network environments. GMA leverages a context-based meta-RL approach with Mixture of Experts (MoE) to improve representation learning, enhancing latent information extraction. By learning a meta-policy during training, GMA enables fast adaptation to different and previously unknown environments, without prior knowledge of the specific MAC protocols in use. Simulation results demonstrate that, although the GMA protocol experiences a slight performance drop compared to baseline methods in training environments, it achieves faster convergence and higher performance in new, unseen environments.

翻译：本文聚焦于异构无线网络中的频谱共享问题，其中采用不同媒体接入控制协议的网络节点通过共享无线信道向公共接入点发送数据包。尽管先前研究已提出基于深度强化学习的多址接入协议，这些协议专为特定场景定制，但其泛化能力受限，难以适应多样化环境，且通常需要耗时的重新训练。为解决这一问题，我们提出可泛化多址接入协议——一种基于元强化学习的新型MAC协议，专为在异构网络环境中快速适配而设计。GMA采用基于上下文的元强化学习方法，结合专家混合机制以改进表征学习，增强潜在信息提取能力。通过在训练过程中学习元策略，GMA能够快速适配不同且先前未知的环境，无需预先了解具体使用的MAC协议。仿真结果表明，虽然在训练环境中GMA协议相比基线方法存在轻微性能下降，但在新的未见环境中实现了更快的收敛速度和更高的性能表现。