Abstract
This research aims to investigate a sum-rate maximization problem in uplink multi-user single-input multiple-output (SIMO) rate splitting multiple access (RSMA) systems. In these systems, Internet of Things devices (IoTDs) act as single-antenna nodes transmitting data to the multiple-antenna base station (BS) utilizing the RSMA technique. The optimization process includes determining the transmit powers of the IoTDs, decoding order, and receive beamforming vector at the BS. To achieve this goal, the problem is transformed into a deep reinforcement learning (DRL) framework, where a post-actor processing stage is proposed and a deep deterministic policy gradient (DDPG)-based approach is applied to tackle the issue. Via simulation results, we show that the proposed approach outperforms some benchmark schemes.
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Acknowledgment
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2023-RS-2022-00156353) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation). It was also supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1F1A1062881).
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Truong, T.P. et al. (2023). Deep Reinforcement Learning-Based Sum-Rate Maximization for Uplink Multi-user SIMO-RSMA Systems. In: Dao, NN., Thinh, T.N., Nguyen, N.T. (eds) Intelligence of Things: Technologies and Applications. ICIT 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 187. Springer, Cham. https://doi.org/10.1007/978-3-031-46573-4_4
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DOI: https://doi.org/10.1007/978-3-031-46573-4_4
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