Optimal Power Allocation for Rate Splitting Communications with Deep Reinforcement Learning

Hieu, NQ; Hoang, DT; Niyato, D; Kim, DI

Optimal Power Allocation for Rate Splitting Communications with Deep Reinforcement Learning

Hieu, NQ Hoang, DT Niyato, D Kim, DI

Permalink

Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Wireless Communications Letters, 2021, 10, (12), pp. 2820-2823
Issue Date:: 2021-12-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

The embargo period expires on 31 Dec 2023

Adobe PDF

Download Accepted versionAdobe PDF (586.34 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Hieu, NQ
dc.contributor.author	Hoang, DT
dc.contributor.author	Niyato, D
dc.contributor.author	Kim, DI
dc.date.accessioned	2022-05-19T03:47:46Z
dc.date.available	2022-05-19T03:47:46Z
dc.date.issued	2021-12-01
dc.identifier.citation	IEEE Wireless Communications Letters, 2021, 10, (12), pp. 2820-2823
dc.identifier.issn	2162-2337
dc.identifier.issn	2162-2345
dc.identifier.uri	http://hdl.handle.net/10453/157524
dc.description.abstract	This letter introduces a novel framework to optimize the power allocation for users in a Rate Splitting Multiple Access (RSMA) network. In the network, messages intended for users are split into different parts that are a single common part and respective private parts. This mechanism enables RSMA to flexibly manage interference and thus enhance energy and spectral efficiency. Although possessing outstanding advantages, optimizing power allocation in RSMA is very challenging under the uncertainty of the communication channel and the transmitter has limited knowledge of the channel information. To solve the problem, we first develop a Markov Decision Process framework to model the dynamic of the communication channel. The deep reinforcement algorithm is then proposed to find the optimal power allocation policy for the transmitter without requiring any prior information of the channel. The simulation results show that the proposed scheme can outperform baseline schemes in terms of average sum-rate under different power and QoS requirements.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Wireless Communications Letters
dc.relation.isbasedon	10.1109/LWC.2021.3118441
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.title	Optimal Power Allocation for Rate Splitting Communications with Deep Reinforcement Learning
dc.type	Journal Article
utslib.citation.volume	10
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1005 Communications Technologies
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2023-12-31T00:00:00+1000Z
dc.date.updated	2022-05-19T03:47:45Z
pubs.issue	12
pubs.publication-status	Published
pubs.volume	10
utslib.citation.issue	12

Abstract:

This letter introduces a novel framework to optimize the power allocation for users in a Rate Splitting Multiple Access (RSMA) network. In the network, messages intended for users are split into different parts that are a single common part and respective private parts. This mechanism enables RSMA to flexibly manage interference and thus enhance energy and spectral efficiency. Although possessing outstanding advantages, optimizing power allocation in RSMA is very challenging under the uncertainty of the communication channel and the transmitter has limited knowledge of the channel information. To solve the problem, we first develop a Markov Decision Process framework to model the dynamic of the communication channel. The deep reinforcement algorithm is then proposed to find the optimal power allocation policy for the transmitter without requiring any prior information of the channel. The simulation results show that the proposed scheme can outperform baseline schemes in terms of average sum-rate under different power and QoS requirements.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/157524