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RSMA for Uplink MIMO Systems: DRL-Based Achievable System Sum Rate Maximization

Authors
Truong, Thanh PhungTuyen Nguyen, Thi MyNguyen, The-ViDao, Nhu-NgocCho, Sungrae
Issue Date
Dec-2023
Publisher
Institute of Electrical and Electronics Engineers Inc.
Keywords
deep reinforcement learning; multiple-input multiple-output; Rate splitting multiple access
Citation
2023 IEEE Globecom Workshops, GC Wkshps 2023, pp 878 - 883
Pages
6
Journal Title
2023 IEEE Globecom Workshops, GC Wkshps 2023
Start Page
878
End Page
883
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/73572
DOI
10.1109/GCWkshps58843.2023.10464584
Abstract
This research studies an uplink multiple-input multiple-output (MIMO) system with rate-splitting multiple access (RSMA) techniques. In this regard, we formulate a problem to maximize the achievable system sum rate by optimizing the precoding matrices at the users and the decoding order of all sub-messages received at the base station (BS). To solve the problem that combines continuous variables and the decoding order, we propose a deep reinforcement learning (DRL) framework that integrates a graph-based searching strategy into the deep deterministic policy gradient (DDPG) algorithm. The simulation results prove the convergence in the training of the proposed framework and its performance in various scenarios. Further-more, the results also open up an attractive research aspect when considering the sum rate maximization problem in RSMA-enhanced MIMO systems. © 2023 IEEE.
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소프트웨어대학 (소프트웨어학부)
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