On the Joint Optimization of Signal Constellations and Bit Mappings

Ranaweera, S; Jayawickrama, BA; He, Y; Ngo, QT; Liu, RP

On the Joint Optimization of Signal Constellations and Bit Mappings

Ranaweera, S Jayawickrama, BA He, Y

Ngo, QT Liu, RP

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Communications Letters, 2024, PP, (99), pp. 1-1
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Ranaweera, S
dc.contributor.author	Jayawickrama, BA
dc.contributor.author	He, Y https://orcid.org/0000-0003-1603-9375
dc.contributor.author	Ngo, QT
dc.contributor.author	Liu, RP
dc.date.accessioned	2024-10-09T03:44:58Z
dc.date.available	2024-10-09T03:44:58Z
dc.date.issued	2024-01-01
dc.identifier.citation	IEEE Communications Letters, 2024, PP, (99), pp. 1-1
dc.identifier.issn	1089-7798
dc.identifier.issn	1558-2558
dc.identifier.uri	http://hdl.handle.net/10453/181332
dc.description.abstract	This letter presents an enhanced gradient search algorithm for jointly optimizing signal constellations and bit mappings. This approach stands in contrast to the recent trend of utilizing deep learning (DL) to model communication systems as end-to-end (E2E) systems, which often involve a large number of learnable parameters and high computational complexity during training. We enhance the efficiency of the gradient search algorithm by leveraging the symmetrical properties of the I/Q plane, particularly in higher modulation orders. The resulting constellations are evaluated in terms of bit error rate (BER) performance under additive white Gaussian noise and Rayleigh flat fading channels. Our findings indicate that the optimized constellations obtained via the enhanced gradient search algorithm outperform an attention-empowered DL-based E2E system in terms of BER across both channels, notably in higher signal-to-noise ratio regimes, without encountering error floor issues.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Communications Letters
dc.relation.isbasedon	10.1109/LCOMM.2024.3456823
dc.rights	info:eu-repo/semantics/openAccess
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	4009 Electronics, sensors and digital hardware
dc.subject.classification	4606 Distributed computing and systems software
dc.title	On the Joint Optimization of Signal Constellations and Bit Mappings
dc.type	Journal Article
utslib.citation.volume	PP
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
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Global Big Data Technologies Centre (GBDTC)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Global Big Data Technologies Centre (GBDTC)/Global Big Data Technologies Centre (GBDTC) Associate Members
utslib.copyright.status	open_access	*
dc.date.updated	2024-10-09T03:44:54Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

This letter presents an enhanced gradient search algorithm for jointly optimizing signal constellations and bit mappings. This approach stands in contrast to the recent trend of utilizing deep learning (DL) to model communication systems as end-to-end (E2E) systems, which often involve a large number of learnable parameters and high computational complexity during training. We enhance the efficiency of the gradient search algorithm by leveraging the symmetrical properties of the I/Q plane, particularly in higher modulation orders. The resulting constellations are evaluated in terms of bit error rate (BER) performance under additive white Gaussian noise and Rayleigh flat fading channels. Our findings indicate that the optimized constellations obtained via the enhanced gradient search algorithm outperform an attention-empowered DL-based E2E system in terms of BER across both channels, notably in higher signal-to-noise ratio regimes, without encountering error floor issues.

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http://hdl.handle.net/10453/181332