Learning Random Access Schemes for Massive Machine-Type Communication With MARL

Learning Random Access Schemes for Massive Machine-Type Communication With MARL

This paper investigates various multi-agent reinforcement learning (MARL) techniques for designing grant-free random access (RA) schemes suitable for low-complexity, low-power battery-operated devices in massive machine-type communication (mMTC). Previous studies on RA with MARL have shown limitatio...

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Bibliographic Details
Main Authors: Muhammad Awais Jadoon, Adriano Pastore, Monica Navarro, Alvaro Valcarce
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Transactions on Machine Learning in Communications and Networking
Subjects:
Massive machine-type communications
MARL
reinforcement learning
grant-free random access
scalability
Online Access:https://ieeexplore.ieee.org/document/10366306/
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