The forthcoming 6G systems are expected to address a wide range of non-stationary tasks. This poses challenges to traditional medium access control (MAC) protocols that are static and predefined. In response, data-driven MAC protocols have recently emerged, offering ability to tailor their signaling messages for specific tasks. This article presents a novel categorization of these data-driven MAC protocols into three levels: Level 1 MAC. task-oriented neural protocols constructed using multi-agent deep reinforcement learning (MADRL); Level 2 MAC. neural network-oriented symbolic protocols developed by converting Level 1 MAC outputs into explicit symbols; and Level 3 MAC. language-oriented semantic protocols harnessing large language models (LLMs) and generative models. With this categorization, we aim to explore the opportunities and challenges of each level by delving into their foundational techniques. Drawing from information theory and associated principles as well as selected case studies, this study provides insights into the trajectory of data-driven MAC protocols and sheds light on future research directions.

翻译：未来6G系统预计将处理一系列非平稳任务，这对传统静态预定义的中介访问控制（MAC）协议提出了挑战。为此，数据驱动的MAC协议近期应运而生，能够针对特定任务定制信令消息。本文提出将这类数据驱动MAC协议划分为三个层次的全新分类：第一层MAC——基于多智能体深度强化学习（MADRL）构建的任务导向神经协议；第二层MAC——通过将第一层MAC输出转化为显式符号发展出的神经网络导向符号协议；第三层MAC——利用大型语言模型（LLMs）和生成模型的语义导向语言协议。通过这一分类体系，我们旨在深入剖析各层次的核心技术，探索其机遇与挑战。基于信息论及相关原理，并结合典型案例研究，本文揭示了数据驱动MAC协议的发展轨迹，并对未来研究方向提供了启示。