Complex CNN CSI Enhancer for Integrated Sensing and Communications

Chen, X; Feng, Z; Zhang, JA; Gao, F; Yuan, X; Yang, Z; Zhang, P

Complex CNN CSI Enhancer for Integrated Sensing and Communications

Chen, X Feng, Z Zhang, JA Gao, F Yuan, X Yang, Z Zhang, P

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Journal on Selected Topics in Signal Processing, 2024, PP, (99), pp. 1-15
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Chen, X
dc.contributor.author	Feng, Z
dc.contributor.author	Zhang, JA
dc.contributor.author	Gao, F
dc.contributor.author	Yuan, X
dc.contributor.author	Yang, Z
dc.contributor.author	Zhang, P
dc.date.accessioned	2024-08-07T05:46:36Z
dc.date.available	2024-08-07T05:46:36Z
dc.date.issued	2024-01-01
dc.identifier.citation	IEEE Journal on Selected Topics in Signal Processing, 2024, PP, (99), pp. 1-15
dc.identifier.issn	1932-4553
dc.identifier.issn	1941-0484
dc.identifier.uri	http://hdl.handle.net/10453/180352
dc.description.abstract	In this paper, we propose a novel complex convolutional neural network (CNN) CSI enhancer for integrated sensing and communications (ISAC), which exploits the correlation between the sensing parameters (such as angle-of-arrival and range) and the channel state information (CSI) to significantly improve the CSI estimation accuracy and further enhance the sensing accuracy. Within the CNN CSI enhancer, we use the complex-valued computation layers to form the CNN, which maintains the phase information of CSI. We also transform the CSI into the sparse angle-delay domain, leading to heatmap images with prominent peaks that can be efficiently processed by CNN. Based on the enhanced CSI outputs, we further propose a novel biased fast Fourier transform (FFT)-based sensing scheme for improving the range sensing accuracy, by artificially introducing phase biasing terms. Extensive simulation results show that the ISAC complex CNN CSI enhancer can converge within 30 training epochs. The normalized mean square error (NMSE) of its CSI estimates is about 20 dB lower than that of the linear minimum mean square error (LMMSE) estimator, and the bit error rate (BER) of demodulation using the enhanced CSI estimation approaches that with perfect CSI. Finally, the range estimation mean square error (MSE) of the proposed biased FFT-based sensing method approaches that of the subspace-based sensing method, at a much lower complexity.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Journal on Selected Topics in Signal Processing
dc.relation.isbasedon	10.1109/JSTSP.2024.3405851
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.subject.classification	Networking & Telecommunications
dc.subject.classification	4006 Communications engineering
dc.subject.classification	4603 Computer vision and multimedia computation
dc.title	Complex CNN CSI Enhancer for Integrated Sensing and Communications
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	0801 Artificial Intelligence and Image Processing
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
pubs.organisational-group	University of Technology Sydney/All Manual Groups
pubs.organisational-group	University of Technology Sydney/All Manual Groups/Global Big Data Technologies Research Centre (GBDTC)
utslib.copyright.status	closed_access	*
dc.date.updated	2024-08-07T05:46:35Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

In this paper, we propose a novel complex convolutional neural network (CNN) CSI enhancer for integrated sensing and communications (ISAC), which exploits the correlation between the sensing parameters (such as angle-of-arrival and range) and the channel state information (CSI) to significantly improve the CSI estimation accuracy and further enhance the sensing accuracy. Within the CNN CSI enhancer, we use the complex-valued computation layers to form the CNN, which maintains the phase information of CSI. We also transform the CSI into the sparse angle-delay domain, leading to heatmap images with prominent peaks that can be efficiently processed by CNN. Based on the enhanced CSI outputs, we further propose a novel biased fast Fourier transform (FFT)-based sensing scheme for improving the range sensing accuracy, by artificially introducing phase biasing terms. Extensive simulation results show that the ISAC complex CNN CSI enhancer can converge within 30 training epochs. The normalized mean square error (NMSE) of its CSI estimates is about 20 dB lower than that of the linear minimum mean square error (LMMSE) estimator, and the bit error rate (BER) of demodulation using the enhanced CSI estimation approaches that with perfect CSI. Finally, the range estimation mean square error (MSE) of the proposed biased FFT-based sensing method approaches that of the subspace-based sensing method, at a much lower complexity.

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http://hdl.handle.net/10453/180352