Mididoddi, Chaitanya K, Ahmad, Eamonn J, Wang, Chao (2017) All-optical random sequence generation for compressive sensing detection of RF signals. In: Microwave Photonics (MWP), 2017 International Topical Meeting on. . pp. 1-4. IEEE E-ISBN 978-1-5386-0762-6. (doi:/10.1109/MWP.2017.8168639) (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:62820)
| 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: https://doi.org/10.1109/MWP.2017.8168639 |
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Abstract
Photonic compressive sensing is a promising data compression method and has been successfully applied in high-speed RF signal detection with greatly reduced requirement for receiver bandwidth. A key challenge is due to the electronic bottleneck in high-speed random sequence generation and mixing. In this work, we propose and experimentally demonstrated for the first time all-optical random sequence generation and mixing for compressive sensing detection of RF signals. The technique is based on photonic time stretch involving cascaded Mach-Zehnder Interferometers (MZIs) for spectral domain random mixing. In a proof-of-concept experiment, successful detection of 1 GHz RF signal with 25% compression ratio using only 50 MHz detection bandwidth has been demonstrated?
| Item Type: | Conference or workshop item (Paper) |
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| DOI/Identification number: | /10.1109/MWP.2017.8168639 |
| Uncontrolled keywords: | compressive sensing; data compression; dispersion; microwave photonics; photonic time stretch |
| Subjects: | T Technology > TK Electrical engineering. Electronics. Nuclear engineering > TK5101 Telecommunications > TK5103.59 Optical communications |
| Institutional Unit: | Schools > School of Engineering, Mathematics and Physics > Engineering |
| Former Institutional Unit: |
Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
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| Depositing User: | Chao Wang |
| Date Deposited: | 15 Aug 2017 15:42 UTC |
| Last Modified: | 20 May 2025 10:42 UTC |
| Resource URI: | https://kar.kent.ac.uk/id/eprint/62820 (The current URI for this page, for reference purposes) |
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https://orcid.org/0000-0002-0454-8079
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