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Photonics-enabled sub-Nyquist radio frequency sensing based on temporal channelization and compressive sensing

Wang, Chao, Gomes, Nathan J. (2014) Photonics-enabled sub-Nyquist radio frequency sensing based on temporal channelization and compressive sensing. In: Microwave Photonics (MWP) and the 2014 9th Asia-Pacific Microwave Photonics Conference (APMP), 2014 International Topical Meeting on. . pp. 335-338. (doi:10.1109/MWP.2014.6994567) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:51265)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
http://doi.org/10.1109/MWP.2014.6994567

Abstract

A novel approach to sensing broadband radio frequency (RF) spectrum beyond the Nyquist limit based on photonic temporal channelization and compressive sensing is proposed. A spectrally-sparse RF signal with unknown frequencies is modulated onto a highly chirped optical pulse. An optical channelizer slices the modulated pulse spectrum, which is equivalent to temporally sampling the RF waveform thanks to the dispersion-induced wavelength-to-time mapping. This serial-to-parallel conversion avoids the use of a high-speed detector and digitizer. Furthermore, compressive sensing with optical random demodulation is achieved using a spatial light modulator, enabling the system to capture the wideband multi-tone RF signal with a sampling rate far lower than the Nyquist rate. It is demonstrated that the temporal channelization system with a channel spacing of 20 GHz achieves RF spectrum sensing with a high resolution of 196 MHz. With an equivalent sampling rate of only 25 GHz, a 50-GHz broadband two-tone RF signal can be captured and reconstructed by the system thanks to compressive sensing with a compression ratio of 4.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/MWP.2014.6994567
Subjects: T Technology
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Tina Thompson
Date Deposited: 29 Oct 2015 10:33 UTC
Last Modified: 05 Nov 2024 10:37 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/51265 (The current URI for this page, for reference purposes)

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