A Multi-Agent Reinforcement Learning Approach for Massive Access in NOMA-URLLC Networks | IEEE Journals & Magazine | IEEE Xplore

A Multi-Agent Reinforcement Learning Approach for Massive Access in NOMA-URLLC Networks


Abstract:

Ultra-reliable low-latency communication (URLLC) enables diverse applications with rigorous latency and reliability requirements. To provide a wide range of services, the...Show More

Abstract:

Ultra-reliable low-latency communication (URLLC) enables diverse applications with rigorous latency and reliability requirements. To provide a wide range of services, the future beyond fifth (B5G) systems are expected to support a large number of URLLC users. In this paper, we propose a joint sub-channel allocation and power control method to support massive access for non-orthogonal multiple access aided URLLC (NOMA-URLLC) networks. We model the problem of maximizing the number of successful access users as a multi-agent reinforcement learning problem. A deep Q-network-based multi-agent reinforcement learning (DQN-MARL) algorithm is proposed to tackle the problem while guaranteeing reliability and latency requirements of URLLC services. Simulation results show that the proposed DQN-MARL algorithm significantly improves the successful access probability in massive access scenarios compared with the existing schemes.
Published in: IEEE Transactions on Vehicular Technology ( Volume: 72, Issue: 12, December 2023)
Page(s): 16799 - 16804
Date of Publication: 06 July 2023

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