Towards Semantic MAC Protocols for 6G: From Protocol Learning to Language-Oriented Approaches
Park, Jihong; Ko, Seung-Woo; Choi, Jinho; Kim, Seong-Lyun; Choi, Junil; Bennis, Mehdi (2024-11-05)
Park, Jihong
Ko, Seung-Woo
Choi, Jinho
Kim, Seong-Lyun
Choi, Junil
Bennis, Mehdi
IEEE
05.11.2024
J. Park, S. -W. Ko, J. Choi, S. -L. Kim, J. Choi and M. Bennis, "Toward Semantic MAC Protocols for 6G: From Protocol Learning to Language-Oriented Approaches," in IEEE BITS the Information Theory Magazine, vol. 4, no. 1, pp. 59-72, March 2024, doi: 10.1109/MBITS.2024.3491949.
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© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202502061495
https://urn.fi/URN:NBN:fi:oulu-202502061495
Tiivistelmä
Abstract
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.
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.
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