On Learning Generalized Wireless MAC Communication Protocols via a Feasible Multi-Agent Reinforcement Learning Framework

On Learning Generalized Wireless MAC Communication Protocols via a Feasible Multi-Agent Reinforcement Learning Framework

Automatically learning medium access control (MAC) communication protocols via multi-agent reinforcement learning (MARL) has received huge attention to cater to the extremely diverse real-world scenarios expected in 6G wireless networks. Several state-of-the-art solutions adopt the centralized train...

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Bibliographic Details
Main Authors: Luciano Miuccio, Salvatore Riolo, Sumudu Samarakoon, Mehdi Bennis, Daniela Panno
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Transactions on Machine Learning in Communications and Networking
Subjects:
6G
multi-agent reinforcement learning
abstraction
generalization
feasibility
protocol learning
Online Access:https://ieeexplore.ieee.org/document/10440615/
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