Deep Reinforcement Learning-Based Sum-Rate Maximization for Uplink Multi-user SIMO-RSMA Systems

Thanh Phung Truong; Tri Hai Nguyen; Anh Tien Tran; Si Van Tien Tran; Van Dat Tuong; Luong Vuong Nguyen; Woongsoo Na; Laihyuk Park; Sungrae Cho

doi:10.1007/978-3-031-46573-4_4

Deep Reinforcement Learning-Based Sum-Rate Maximization for Uplink Multi-user SIMO-RSMA Systems

Thanh Phung Truong
, Tri Hai Nguyen
, Anh Tien Tran
, Si Van Tien Tran
, Van Dat Tuong
, Luong Vuong Nguyen
, Woongsoo Na
, Laihyuk Park
, Sungrae Cho

Dept. of Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

4 Scopus citations

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.

Original language	English
Title of host publication	Lecture Notes on Data Engineering and Communications Technologies
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	36-45
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-031-46573-4_4
State	Published - 2023

Publication series

Name	Lecture Notes on Data Engineering and Communications Technologies
Volume	187
ISSN (Print)	2367-4512
ISSN (Electronic)	2367-4520

Keywords

DRL
SIMO-RSMA
sum-rate maximization
uplink

Access to Document

10.1007/978-3-031-46573-4_4

Cite this

Truong, T. P., Nguyen, T. H., Tran, A. T., Tran, S. V. T., Tuong, V. D., Nguyen, L. V., Na, W., Park, L., & Cho, S. (2023). Deep Reinforcement Learning-Based Sum-Rate Maximization for Uplink Multi-user SIMO-RSMA Systems. In Lecture Notes on Data Engineering and Communications Technologies (pp. 36-45). (Lecture Notes on Data Engineering and Communications Technologies; Vol. 187). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-46573-4_4

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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.",

keywords = "DRL, SIMO-RSMA, sum-rate maximization, uplink",

author = "Truong, \{Thanh Phung\} and Nguyen, \{Tri Hai\} and Tran, \{Anh Tien\} and Tran, \{Si Van Tien\} and Tuong, \{Van Dat\} and Nguyen, \{Luong Vuong\} and Woongsoo Na and Laihyuk Park and Sungrae Cho",

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Truong, TP, Nguyen, TH, Tran, AT, Tran, SVT, Tuong, VD, Nguyen, LV, Na, W, Park, L & Cho, S 2023, Deep Reinforcement Learning-Based Sum-Rate Maximization for Uplink Multi-user SIMO-RSMA Systems. in Lecture Notes on Data Engineering and Communications Technologies. Lecture Notes on Data Engineering and Communications Technologies, vol. 187, Springer Science and Business Media Deutschland GmbH, pp. 36-45. https://doi.org/10.1007/978-3-031-46573-4_4

Deep Reinforcement Learning-Based Sum-Rate Maximization for Uplink Multi-user SIMO-RSMA Systems. / Truong, Thanh Phung; Nguyen, Tri Hai; Tran, Anh Tien et al.
Lecture Notes on Data Engineering and Communications Technologies. Springer Science and Business Media Deutschland GmbH, 2023. p. 36-45 (Lecture Notes on Data Engineering and Communications Technologies; Vol. 187).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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AU - Truong, Thanh Phung

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AU - Tuong, Van Dat

AU - Nguyen, Luong Vuong

AU - Na, Woongsoo

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AU - Cho, Sungrae

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Truong TP, Nguyen TH, Tran AT, Tran SVT, Tuong VD, Nguyen LV et al. Deep Reinforcement Learning-Based Sum-Rate Maximization for Uplink Multi-user SIMO-RSMA Systems. In Lecture Notes on Data Engineering and Communications Technologies. Springer Science and Business Media Deutschland GmbH. 2023. p. 36-45. (Lecture Notes on Data Engineering and Communications Technologies). doi: 10.1007/978-3-031-46573-4_4