Constant Wideband Compressive Spectrum Sensing with Cascade Forward-Backward Propagating and Prior Knowledge Refining

Liu, J; Huang, XL; Yu, S

Constant Wideband Compressive Spectrum Sensing with Cascade Forward-Backward Propagating and Prior Knowledge Refining

Liu, J Huang, XL Yu, S

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Wireless Communications, 2023, PP, (99), pp. 1-1
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Liu, J
dc.contributor.author	Huang, XL
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.date.accessioned	2024-03-12T00:57:41Z
dc.date.available	2024-03-12T00:57:41Z
dc.date.issued	2023-01-01
dc.identifier.citation	IEEE Transactions on Wireless Communications, 2023, PP, (99), pp. 1-1
dc.identifier.issn	1536-1276
dc.identifier.issn	1558-2248
dc.identifier.uri	http://hdl.handle.net/10453/176522
dc.description.abstract	Compressive spectrum sensing (CSS) is regarded as one of the promising techniques to detect wideband spectrum holes. How to formulate an accurate prior knowledge in CSS is still an important and open topic. In this paper, we propose a novel CSS algorithm with cascade forward-backward propagation and prior knowledge refining. Firstly, the wideband signal reconstruction model is formulated as a ℓ<sub><italic>p</italic></sub>-norm (0 < <italic>p</italic> < 1) nonconvex optimization problem where the temporal correlation between continuous observed spectrum signals is exploited to improve the reconstruction accuracy of the current spectrum signal, and such nonconvex optimization problem is solved by iteratively reweighted least squares algorithm. Then, the multiple reconstructed versions of the current spectrum signal obtained by forward-backward propagation are fused into a prior. Finally, such fused prior is modified adaptively in each iteration, and thus significantly improves the accuracy of spectrum decision and speeds up the convergence. The simulation results show that compared with other CSS schemes based on weighted ℓ<sub>1</sub> minimization, ℓ<sub>1</sub>-ℓ<sub>1</sub> minimization, ℓ<sub>1</sub>-ℓ<sub>2</sub> minimization, and maximizing correlation, the proposed scheme has more than 7% and 10% detection performance improvement when the probability of false alarm is 10%, under received signal-to-noise ratio as -10 dB and -5 dB, respectively.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Wireless Communications
dc.relation.isbasedon	10.1109/TWC.2023.3292385
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.subject.classification	4006 Communications engineering
dc.subject.classification	4008 Electrical engineering
dc.subject.classification	4606 Distributed computing and systems software
dc.title	Constant Wideband Compressive Spectrum Sensing with Cascade Forward-Backward Propagating and Prior Knowledge Refining
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 Computer Science
pubs.organisational-group	University of Technology Sydney/Strength - CCSP - Centre for Cyber Security and Privacy
utslib.copyright.status	closed_access	*
dc.date.updated	2024-03-12T00:57:40Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Compressive spectrum sensing (CSS) is regarded as one of the promising techniques to detect wideband spectrum holes. How to formulate an accurate prior knowledge in CSS is still an important and open topic. In this paper, we propose a novel CSS algorithm with cascade forward-backward propagation and prior knowledge refining. Firstly, the wideband signal reconstruction model is formulated as a ℓ_p-norm (0 < p < 1) nonconvex optimization problem where the temporal correlation between continuous observed spectrum signals is exploited to improve the reconstruction accuracy of the current spectrum signal, and such nonconvex optimization problem is solved by iteratively reweighted least squares algorithm. Then, the multiple reconstructed versions of the current spectrum signal obtained by forward-backward propagation are fused into a prior. Finally, such fused prior is modified adaptively in each iteration, and thus significantly improves the accuracy of spectrum decision and speeds up the convergence. The simulation results show that compared with other CSS schemes based on weighted ℓ₁ minimization, ℓ₁-ℓ₁ minimization, ℓ₁-ℓ₂ minimization, and maximizing correlation, the proposed scheme has more than 7% and 10% detection performance improvement when the probability of false alarm is 10%, under received signal-to-noise ratio as -10 dB and -5 dB, respectively.

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http://hdl.handle.net/10453/176522