DRL-Assisted Dynamic Subconnected Hybrid Precoding for Multi-Layer THz mMIMO-NOMA System

Shahjalal, M; Rahman, MH; Alam, MM; Chowdhury, MZ; Jang, YM

DRL-Assisted Dynamic Subconnected Hybrid Precoding for Multi-Layer THz mMIMO-NOMA System

Shahjalal, M Rahman, MH Alam, MM Chowdhury, MZ Jang, YM

Permalink

Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Vehicular Technology, 2024, PP, (99), pp. 1-13
Issue Date:: 2024-01-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Download Published versionAdobe PDF (3.23 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Shahjalal, M
dc.contributor.author	Rahman, MH
dc.contributor.author	Alam, MM
dc.contributor.author	Chowdhury, MZ
dc.contributor.author	Jang, YM
dc.date.accessioned	2024-08-21T05:42:55Z
dc.date.available	2024-08-21T05:42:55Z
dc.date.issued	2024-01-01
dc.identifier.citation	IEEE Transactions on Vehicular Technology, 2024, PP, (99), pp. 1-13
dc.identifier.issn	0018-9545
dc.identifier.issn	1939-9359
dc.identifier.uri	http://hdl.handle.net/10453/180507
dc.description.abstract	Massive multiple-input multiple-output (mMIMO) techniques can be combined with the non-orthogonal multiple access (NOMA) scheme in terahertz (THz) communication to achieve multiplexing gains and satisfy the ultra-high capacity and massive connectivity requirements. However, the development of a near-optimal solution for energy and spectral efficiency problems in a dynamic wireless cellular environment remains challenging. In this paper, a cooperative THz mMIMO-NOMA enabled base station is established to optimize the power consumption and maximize the spectral efficiency. A multi-layer mMIMO antenna architecture is used to perform dynamic sub-connected hybrid precoding in each layer. The fuzzy c-means clustering algorithm is used to group densely located users into clusters to efficiently use the power coefficients. To optimize the power distribution constraints and coordination of the hybrid precoding structure, a multi-agent deep reinforcement learning algorithm is developed, which operates in a distributive manner. Each base station layer involves an agent that trains a deep Q-network, and optimal actions are executed by sharing exchangeable network parameters among layers. The simulation results indicate that the proposed scheme is able to learn the trade-off between maximization of the energy efficiency and overall system capacity.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Vehicular Technology
dc.relation.isbasedon	10.1109/TVT.2024.3385494
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 10 Technology
dc.subject.classification	Automobile Design & Engineering
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	DRL-Assisted Dynamic Subconnected Hybrid Precoding for Multi-Layer THz mMIMO-NOMA System
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	10 Technology
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2024-08-21T05:42:53Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Massive multiple-input multiple-output (mMIMO) techniques can be combined with the non-orthogonal multiple access (NOMA) scheme in terahertz (THz) communication to achieve multiplexing gains and satisfy the ultra-high capacity and massive connectivity requirements. However, the development of a near-optimal solution for energy and spectral efficiency problems in a dynamic wireless cellular environment remains challenging. In this paper, a cooperative THz mMIMO-NOMA enabled base station is established to optimize the power consumption and maximize the spectral efficiency. A multi-layer mMIMO antenna architecture is used to perform dynamic sub-connected hybrid precoding in each layer. The fuzzy c-means clustering algorithm is used to group densely located users into clusters to efficiently use the power coefficients. To optimize the power distribution constraints and coordination of the hybrid precoding structure, a multi-agent deep reinforcement learning algorithm is developed, which operates in a distributive manner. Each base station layer involves an agent that trains a deep Q-network, and optimal actions are executed by sharing exchangeable network parameters among layers. The simulation results indicate that the proposed scheme is able to learn the trade-off between maximization of the energy efficiency and overall system capacity.

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

http://hdl.handle.net/10453/180507