Digital Twin-Aided Learning for Managing Reconfigurable Intelligent Surface-Assisted, Uplink, User-Centric Cell-Free Systems

Cui, Y; Lv, T; Ni, W; Jamalipour, A

Digital Twin-Aided Learning for Managing Reconfigurable Intelligent Surface-Assisted, Uplink, User-Centric Cell-Free Systems

Cui, Y Lv, T Ni, W

Jamalipour, A

Permalink

Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Journal on Selected Areas in Communications, 2023, 41, (10), pp. 3175-3190
Issue Date:: 2023-10-01

In Progress

	Filename	Description	Size
	Digital_Twin-Aided_Learning_for_Managing_Reconfigurable_Intelligent_Surface-Assisted_Uplink_User-Centric_Cell-Free_Systems.pdf	Published version	14.56 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is being processed and is not currently available.

Full metadata record

Field	Value	Language
dc.contributor.author	Cui, Y
dc.contributor.author	Lv, T
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X
dc.contributor.author	Jamalipour, A
dc.date.accessioned	2024-04-16T04:36:17Z
dc.date.available	2024-04-16T04:36:17Z
dc.date.issued	2023-10-01
dc.identifier.citation	IEEE Journal on Selected Areas in Communications, 2023, 41, (10), pp. 3175-3190
dc.identifier.issn	0733-8716
dc.identifier.issn	1558-0008
dc.identifier.uri	http://hdl.handle.net/10453/177969
dc.description.abstract	This paper puts forth a new, reconfigurable intelligent surface (RIS)-Assisted, uplink, user-centric cell-free (UCCF) system managed with the assistance of a digital twin (DT). Specifically, we propose a novel learning framework that maximizes the sum-rate by jointly optimizing the access point and user association (AUA), power control, and RIS beamforming. This problem is challenging and has never been addressed due to its prohibitively large and complex solution space. Our framework decouples the AUA from the power control and RIS beamforming (PCRB) based on the different natures of their variables, hence reducing the solution space. A new position-Adaptive binary particle swarm optimization (PABPSO) method is designed for the AUA. Two twin-delayed deep deterministic policy gradient (TD3) models with new and refined state pre-processing layers are developed for the PCRB. Another important aspect is that a DT is leveraged to train the learning framework with its replay of channel estimates stored. The AUA, power control, and RIS beamforming are only tested in the physical environment at the end of selected epochs. Simulations show that using RISs contributes to considerable increases in the sum-rate of UCCF systems, and the DT dramatically reduces overhead with marginal performance loss. The proposed framework is superior to its alternatives in terms of sum-rate and convergence stability.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Journal on Selected Areas in Communications
dc.relation.isbasedon	10.1109/JSAC.2023.3310050
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.subject.classification	Networking & Telecommunications
dc.subject.classification	4006 Communications engineering
dc.subject.classification	4606 Distributed computing and systems software
dc.title	Digital Twin-Aided Learning for Managing Reconfigurable Intelligent Surface-Assisted, Uplink, User-Centric Cell-Free Systems
dc.type	Journal Article
utslib.citation.volume	41
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/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	in_progress	*
dc.date.updated	2024-04-16T04:36:13Z
pubs.issue	10
pubs.publication-status	Published
pubs.volume	41
utslib.citation.issue	10

Abstract:

This paper puts forth a new, reconfigurable intelligent surface (RIS)-Assisted, uplink, user-centric cell-free (UCCF) system managed with the assistance of a digital twin (DT). Specifically, we propose a novel learning framework that maximizes the sum-rate by jointly optimizing the access point and user association (AUA), power control, and RIS beamforming. This problem is challenging and has never been addressed due to its prohibitively large and complex solution space. Our framework decouples the AUA from the power control and RIS beamforming (PCRB) based on the different natures of their variables, hence reducing the solution space. A new position-Adaptive binary particle swarm optimization (PABPSO) method is designed for the AUA. Two twin-delayed deep deterministic policy gradient (TD3) models with new and refined state pre-processing layers are developed for the PCRB. Another important aspect is that a DT is leveraged to train the learning framework with its replay of channel estimates stored. The AUA, power control, and RIS beamforming are only tested in the physical environment at the end of selected epochs. Simulations show that using RISs contributes to considerable increases in the sum-rate of UCCF systems, and the DT dramatically reduces overhead with marginal performance loss. The proposed framework is superior to its alternatives in terms of sum-rate and convergence stability.

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

http://hdl.handle.net/10453/177969