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The forthcoming 6G systems are expected to address a wide range ofnon-stationary tasks. This poses challenges to traditional medium accesscontrol (MAC) protocols that are static and predefined. In response,data-driven MAC protocols have recently emerged, offering ability to tailortheir signaling messages for specific tasks. This article presents a novelcategorization of these data-driven MAC protocols into three levels: Level 1MAC. task-oriented neural protocols constructed using multi-agent deepreinforcement learning (MADRL); Level 2 MAC. neural network-oriented symbolicprotocols developed by converting Level 1 MAC outputs into explicit symbols;and Level 3 MAC. language-oriented semantic protocols harnessing large languagemodels (LLMs) and generative models. With this categorization, we aim toexplore the opportunities and challenges of each level by delving into theirfoundational techniques. Drawing from information theory and associatedprinciples as well as selected case studies, this study provides insights intothe trajectory of data-driven MAC protocols and sheds light on future researchdirections.

Towards Semantic Communication Protocols for 6G: From Protocol Learning to Language-Oriented Approaches