Cell-Free Massive MIMO in the Short Blocklength Regime for URLLC

Nasir, AA; Tuan, HD; Ngo, HQ; Duong, TQ; Poor, HV

Cell-Free Massive MIMO in the Short Blocklength Regime for URLLC

Nasir, AA Tuan, HD Ngo, HQ Duong, TQ Poor, HV

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Transactions on Wireless Communications, 2021, 20, (9), pp. 5861-5871
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Nasir, AA
dc.contributor.author	Tuan, HD
dc.contributor.author	Ngo, HQ
dc.contributor.author	Duong, TQ
dc.contributor.author	Poor, HV
dc.date.accessioned	2022-04-03T08:28:13Z
dc.date.available	2022-04-03T08:28:13Z
dc.date.issued	2021-01-01
dc.identifier.citation	IEEE Transactions on Wireless Communications, 2021, 20, (9), pp. 5861-5871
dc.identifier.issn	1536-1276
dc.identifier.issn	1558-2248
dc.identifier.uri	http://hdl.handle.net/10453/155902
dc.description.abstract	This paper considers cell-free massive MIMO (cfm-MIMO) for downlink ultra reliable and low-latency communication (URLLC). At the time of writing, cfm-MIMO has only been considered for communication in the long blocklength regime (LBR), whose throughput is determined by the Shannon capacity with the interference treated as Gaussian noise. Conjugate beamforming (CB) is often used as it requires only local channel state information (CSI) for implementation but its design is based on a large-scale nonconvex problem, which is computationally intractable. The rate function in URLLC is much more complex than the Shannon rate function. The paper proposes a special class of CB, which admits a low-scale optimization formulation for computational tractability. Accordingly, a new path-following algorithm, which generates a sequence of better feasible points and converges at least to a locally optimal solution, is developed for optimizing URLLC rates and cfm-MIMO energy efficiency. Furthermore, the paper also develops improper Gaussian signaling to improve both the Shannon rate and URLLC rate.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation	http://purl.org/au-research/grants/arc/DP190102501
dc.relation.ispartof	IEEE Transactions on Wireless Communications
dc.relation.isbasedon	10.1109/twc.2021.3070836
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.subject.classification	Networking & Telecommunications
dc.title	Cell-Free Massive MIMO in the Short Blocklength Regime for URLLC
dc.type	Journal Article
utslib.citation.volume	20
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	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-04-03T08:28:11Z
pubs.issue	9
pubs.publication-status	Published
pubs.volume	20
utslib.citation.issue	9

Abstract:

This paper considers cell-free massive MIMO (cfm-MIMO) for downlink ultra reliable and low-latency communication (URLLC). At the time of writing, cfm-MIMO has only been considered for communication in the long blocklength regime (LBR), whose throughput is determined by the Shannon capacity with the interference treated as Gaussian noise. Conjugate beamforming (CB) is often used as it requires only local channel state information (CSI) for implementation but its design is based on a large-scale nonconvex problem, which is computationally intractable. The rate function in URLLC is much more complex than the Shannon rate function. The paper proposes a special class of CB, which admits a low-scale optimization formulation for computational tractability. Accordingly, a new path-following algorithm, which generates a sequence of better feasible points and converges at least to a locally optimal solution, is developed for optimizing URLLC rates and cfm-MIMO energy efficiency. Furthermore, the paper also develops improper Gaussian signaling to improve both the Shannon rate and URLLC rate.

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http://hdl.handle.net/10453/155902