Deep Reinforcement Learning-based Power Allocation for Downlink RSMA System

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (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 languageEnglish
Title of host publicationICTC 2022 - 13th International Conference on Information and Communication Technology Convergence
Subtitle of host publicationAccelerating Digital Transformation with ICT Innovation
PublisherIEEE Computer Society
Pages775-777
Number of pages3
ISBN (Electronic)9781665499392
DOIs
Publication statusPublished - 2022
Event13th International Conference on Information and Communication Technology Convergence, ICTC 2022 - Jeju Island, Korea, Republic of
Duration: 2022 Oct 192022 Oct 21

Publication series

NameInternational Conference on ICT Convergence
Volume2022-October
ISSN (Print)2162-1233
ISSN (Electronic)2162-1241

Conference

Conference13th International Conference on Information and Communication Technology Convergence, ICTC 2022
Country/TerritoryKorea, Republic of
CityJeju Island
Period22/10/1922/10/21

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

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