Towards Semantic Communication Protocols for 6G: From Protocol Learning
  to Language-Oriented Approaches
Jihong Park Seung-Woo Ko Jinho Choi Seong-Lyun Kim Mehdi Bennis
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.