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Resource Allocation in Uplink NOMA Systems: A Hybrid-Decision-Based Multi-Agent Deep Reinforcement Learning Approach

Authors :
Xie, Xianzhong
Li, Min
Shi, Zhaoyuan
Yang, Helin
Huang, Qian
Xiong, Zehui
Source :
IEEE Transactions on Vehicular Technology; December 2023, Vol. 72 Issue: 12 p16760-16765, 6p
Publication Year :
2023

Abstract

This correspondence investigates a joint power control, channel selection, and user dynamic access scheme to maximize the sum rate of non-orthogonal multiple access (NOMA) systems. The highly dynamic and uncertain environment hinders the collection of accurate instantaneous channel state information (CSI) at the base station for centralized resource management. Therefore, we propose a hybrid-decision-based multi-agent actor-critic (HD-MAAC) approach to optimize the sum rate of the system. The proposed scheme improves the standard actor-critic (AC) algorithm to obtain both discrete and continuous actions. Simulation results verify that the proposed scheme achieves higher sum rate than that of existing popular schemes under different settings.

Details

Language :
English
ISSN :
00189545
Volume :
72
Issue :
12
Database :
Supplemental Index
Journal :
IEEE Transactions on Vehicular Technology
Publication Type :
Periodical
Accession number :
ejs64994465
Full Text :
https://doi.org/10.1109/TVT.2023.3289567