RSMA for Uplink MIMO Systems: DRL-Based Achievable System Sum Rate Maximization
- Authors
- Truong, Thanh Phung; Tuyen Nguyen, Thi My; Nguyen, The-Vi; Dao, Nhu-Ngoc; Cho, 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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Collections - College of Software > School of Computer Science and Engineering > 1. Journal Articles
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