Deep Reinforcement Learning-based Power Allocation for Downlink RSMA System

Hoang Thi Huong Giang; Pham Duy Thanh; Haneul Ko; Sangheon Pack

doi:10.1109/ICTC55196.2022.9952717

Deep Reinforcement Learning-based Power Allocation for Downlink RSMA System

Hoang Thi Huong Giang
, Pham Duy Thanh
, Haneul Ko
, Sangheon Pack^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

10 Citations (Scopus)

Abstract

In this paper, we study the maximum sum rate for a downlink rate splitting multiple access (RSMA) system in long run. In the considered model, the RSMA technique is utilized at a powered base station (BS), where the data from the single BS is divided into two sub-data with different transmit power and receivers decode their received data using a successive decoding technique. To maximize the sum rate of the system, the optimization problem is first reformulated as a Markov decision process framework. Eventually, a deep reinforcement learning algorithm is applied to obtain the optimal solution and deal with the stochastic properties of the network environment. Simulation results show that the RSMA outperforms non-orthogonal multiple access (NOMA) and orthogonal frequency-division multiple access (OFDMA) in terms of system sum rate.

Original language	English
Title of host publication	ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence
Subtitle of host publication	Accelerating Digital Transformation with ICT Innovation
Publisher	IEEE Computer Society
Pages	775-777
Number of pages	3
ISBN (Electronic)	9781665499392
DOIs	https://doi.org/10.1109/ICTC55196.2022.9952717
Publication status	Published - 2022
Event	13th International Conference on Information and Communication Technology Convergence, ICTC 2022 - Jeju Island, Korea, Republic of Duration: 2022 Oct 19 → 2022 Oct 21

Publication series

Name	International Conference on ICT Convergence
Volume	2022-October
ISSN (Print)	2162-1233
ISSN (Electronic)	2162-1241

Conference

Conference	13th International Conference on Information and Communication Technology Convergence, ICTC 2022
Country/Territory	Korea, Republic of
City	Jeju Island
Period	22/10/19 → 22/10/21

Bibliographical note

Funding Information:
This work was supported by National Research Foundation (NRF) of Korea Grant funded by the Korean Government (MSIT) (No. 2021R1A4A3022102 and 2020R1A2C3006786).

Publisher Copyright:
© 2022 IEEE.

Keywords

deep reinforcement learning
Power allocation
RSMA

ASJC Scopus subject areas

Information Systems
Computer Networks and Communications

Access to Document

10.1109/ICTC55196.2022.9952717

Cite this

Huong Giang, H. T., Thanh, P. D., Ko, H., & Pack, S. (2022). Deep Reinforcement Learning-based Power Allocation for Downlink RSMA System. In ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence: Accelerating Digital Transformation with ICT Innovation (pp. 775-777). (International Conference on ICT Convergence; Vol. 2022-October). IEEE Computer Society. https://doi.org/10.1109/ICTC55196.2022.9952717

Deep Reinforcement Learning-based Power Allocation for Downlink RSMA System. / Huong Giang, Hoang Thi; Thanh, Pham Duy; Ko, Haneul et al.
ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence: Accelerating Digital Transformation with ICT Innovation. IEEE Computer Society, 2022. p. 775-777 (International Conference on ICT Convergence; Vol. 2022-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Huong Giang, HT, Thanh, PD, Ko, H & Pack, S 2022, Deep Reinforcement Learning-based Power Allocation for Downlink RSMA System. in ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence: Accelerating Digital Transformation with ICT Innovation. International Conference on ICT Convergence, vol. 2022-October, IEEE Computer Society, pp. 775-777, 13th International Conference on Information and Communication Technology Convergence, ICTC 2022, Jeju Island, Korea, Republic of, 22/10/19. https://doi.org/10.1109/ICTC55196.2022.9952717

Huong Giang HT, Thanh PD, Ko H, Pack S. Deep Reinforcement Learning-based Power Allocation for Downlink RSMA System. In ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence: Accelerating Digital Transformation with ICT Innovation. IEEE Computer Society. 2022. p. 775-777. (International Conference on ICT Convergence). doi: 10.1109/ICTC55196.2022.9952717

Huong Giang, Hoang Thi ; Thanh, Pham Duy ; Ko, Haneul et al. / Deep Reinforcement Learning-based Power Allocation for Downlink RSMA System. ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence: Accelerating Digital Transformation with ICT Innovation. IEEE Computer Society, 2022. pp. 775-777 (International Conference on ICT Convergence).

@inproceedings{5e4bbeee5cd34c8c8c083484ad3785bf,

title = "Deep Reinforcement Learning-based Power Allocation for Downlink RSMA System",

abstract = "In this paper, we study the maximum sum rate for a downlink rate splitting multiple access (RSMA) system in long run. In the considered model, the RSMA technique is utilized at a powered base station (BS), where the data from the single BS is divided into two sub-data with different transmit power and receivers decode their received data using a successive decoding technique. To maximize the sum rate of the system, the optimization problem is first reformulated as a Markov decision process framework. Eventually, a deep reinforcement learning algorithm is applied to obtain the optimal solution and deal with the stochastic properties of the network environment. Simulation results show that the RSMA outperforms non-orthogonal multiple access (NOMA) and orthogonal frequency-division multiple access (OFDMA) in terms of system sum rate.",

keywords = "deep reinforcement learning, Power allocation, RSMA",

author = "\{Huong Giang\}, \{Hoang Thi\} and Thanh, \{Pham Duy\} and Haneul Ko and Sangheon Pack",

note = "Funding Information: This work was supported by National Research Foundation (NRF) of Korea Grant funded by the Korean Government (MSIT) (No. 2021R1A4A3022102 and 2020R1A2C3006786). Publisher Copyright: {\textcopyright} 2022 IEEE.; 13th International Conference on Information and Communication Technology Convergence, ICTC 2022 ; Conference date: 19-10-2022 Through 21-10-2022",

year = "2022",

doi = "10.1109/ICTC55196.2022.9952717",

language = "English",

series = "International Conference on ICT Convergence",

publisher = "IEEE Computer Society",

pages = "775--777",

booktitle = "ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence",

}

TY - GEN

T1 - Deep Reinforcement Learning-based Power Allocation for Downlink RSMA System

AU - Huong Giang, Hoang Thi

AU - Thanh, Pham Duy

AU - Ko, Haneul

AU - Pack, Sangheon

N1 - Funding Information: This work was supported by National Research Foundation (NRF) of Korea Grant funded by the Korean Government (MSIT) (No. 2021R1A4A3022102 and 2020R1A2C3006786). Publisher Copyright: © 2022 IEEE.

PY - 2022

Y1 - 2022

N2 - In this paper, we study the maximum sum rate for a downlink rate splitting multiple access (RSMA) system in long run. In the considered model, the RSMA technique is utilized at a powered base station (BS), where the data from the single BS is divided into two sub-data with different transmit power and receivers decode their received data using a successive decoding technique. To maximize the sum rate of the system, the optimization problem is first reformulated as a Markov decision process framework. Eventually, a deep reinforcement learning algorithm is applied to obtain the optimal solution and deal with the stochastic properties of the network environment. Simulation results show that the RSMA outperforms non-orthogonal multiple access (NOMA) and orthogonal frequency-division multiple access (OFDMA) in terms of system sum rate.

AB - In this paper, we study the maximum sum rate for a downlink rate splitting multiple access (RSMA) system in long run. In the considered model, the RSMA technique is utilized at a powered base station (BS), where the data from the single BS is divided into two sub-data with different transmit power and receivers decode their received data using a successive decoding technique. To maximize the sum rate of the system, the optimization problem is first reformulated as a Markov decision process framework. Eventually, a deep reinforcement learning algorithm is applied to obtain the optimal solution and deal with the stochastic properties of the network environment. Simulation results show that the RSMA outperforms non-orthogonal multiple access (NOMA) and orthogonal frequency-division multiple access (OFDMA) in terms of system sum rate.

KW - deep reinforcement learning

KW - Power allocation

KW - RSMA

UR - https://www.scopus.com/pages/publications/85143251548

U2 - 10.1109/ICTC55196.2022.9952717

DO - 10.1109/ICTC55196.2022.9952717

M3 - Conference contribution

AN - SCOPUS:85143251548

T3 - International Conference on ICT Convergence

SP - 775

EP - 777

BT - ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence

PB - IEEE Computer Society

T2 - 13th International Conference on Information and Communication Technology Convergence, ICTC 2022

Y2 - 19 October 2022 through 21 October 2022

ER -

Deep Reinforcement Learning-based Power Allocation for Downlink RSMA System

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this