Learning-based Reconfigurable Intelligent Surface-aided Rate-Splitting Multiple Access Networks
- Authors
- Hua, Duc-Thien; Do, Quang Tuan; Dao, Nhu-Ngoc; Nguyen, The-Vi; Lakew, Demeke Shumeye; Cho, Sungrae
- Issue Date
- Oct-2023
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Array signal processing; Deep reinforcement learning; Internet of Things; NOMA; rate-splitting multiple access; reconfigurable intelligent surface; Resource management; Uplink; Wireless communication; Wireless sensor networks
- Citation
- IEEE Internet of Things Journal, v.10, no.20, pp 1 - 1
- Pages
- 1
- Journal Title
- IEEE Internet of Things Journal
- Volume
- 10
- Number
- 20
- Start Page
- 1
- End Page
- 1
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/67277
- DOI
- 10.1109/JIOT.2023.3279196
- ISSN
- 2327-4662
- Abstract
- Rate-splitting multiple access (RSMA) and reconfigurable intelligent surface (RIS) techniques show promise in enhancing spectral efficiency in sixth-generation Internet of Things (IoT) networks. However, optimizing the synergy between these two methods is challenging due to the complex and dynamic environment. This study focuses on maximizing the sum-rate metric in RIS-assisted uplink multiantenna RSMA IoT networks to address this problem. We jointly optimized the base station beamforming design, power allocation, and RIS phase shifts to enhance the spectral efficiency with multiple mobile IoT devices present. The controlled parameters are continuous variables and the mathematical problem is non-concave, Therefore, we formulated the problem as a Markov decision process and used the deep deterministic policy gradient (DDPG) to determine the optimal joint actions. We proposed a safe action shaping process for the decision-making actor network to address constraint violations. Through a rigorous performance evaluation, we demonstrated that the DDPG approach with action shaping outperforms the current DDPG algorithm regarding the maximum achievable sum rate. IEEE
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