mlsanityData to 7 Oct 2026

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

arXiv 2310.09506 ↗First posted 14 Oct 2023cs.IT cs.AI cs.LGcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 25 of its 30 references that are in the map. First posted on arXiv 14 Oct 2023.

Builds on

25 of 30 references.

References · 25

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13 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 1; 2026: 2.

7 papers.