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Broadband physical layer cognitive radio with an integrated photonic processor for blind source separation
The expansion of telecommunications incurs increasingly severe crosstalk and interference, and a physical layer cognitive method, called blind source separation (BSS), can effectively address these issues. BSS requires minimal prior knowledge to recover signals from their mixtures, agnostic to the c...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9971366/ https://www.ncbi.nlm.nih.gov/pubmed/36849533 http://dx.doi.org/10.1038/s41467-023-36814-4 |
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author | Zhang, Weipeng Tait, Alexander Huang, Chaoran Ferreira de Lima, Thomas Bilodeau, Simon Blow, Eric C. Jha, Aashu Shastri, Bhavin J. Prucnal, Paul |
author_facet | Zhang, Weipeng Tait, Alexander Huang, Chaoran Ferreira de Lima, Thomas Bilodeau, Simon Blow, Eric C. Jha, Aashu Shastri, Bhavin J. Prucnal, Paul |
author_sort | Zhang, Weipeng |
collection | PubMed |
description | The expansion of telecommunications incurs increasingly severe crosstalk and interference, and a physical layer cognitive method, called blind source separation (BSS), can effectively address these issues. BSS requires minimal prior knowledge to recover signals from their mixtures, agnostic to the carrier frequency, signal format, and channel conditions. However, previous electronic implementations did not fulfil this versatility due to the inherently narrow bandwidth of radio-frequency (RF) components, the high energy consumption of digital signal processors (DSP), and their shared weaknesses of low scalability. Here, we report a photonic BSS approach that inherits the advantages of optical devices and fully fulfils its “blindness” aspect. Using a microring weight bank integrated on a photonic chip, we demonstrate energy-efficient, wavelength-division multiplexing (WDM) scalable BSS across 19.2 GHz processing bandwidth. Our system also has a high (9-bit) resolution for signal demixing thanks to a recently developed dithering control method, resulting in higher signal-to-interference ratios (SIR) even for ill-conditioned mixtures. |
format | Online Article Text |
id | pubmed-9971366 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-99713662023-03-01 Broadband physical layer cognitive radio with an integrated photonic processor for blind source separation Zhang, Weipeng Tait, Alexander Huang, Chaoran Ferreira de Lima, Thomas Bilodeau, Simon Blow, Eric C. Jha, Aashu Shastri, Bhavin J. Prucnal, Paul Nat Commun Article The expansion of telecommunications incurs increasingly severe crosstalk and interference, and a physical layer cognitive method, called blind source separation (BSS), can effectively address these issues. BSS requires minimal prior knowledge to recover signals from their mixtures, agnostic to the carrier frequency, signal format, and channel conditions. However, previous electronic implementations did not fulfil this versatility due to the inherently narrow bandwidth of radio-frequency (RF) components, the high energy consumption of digital signal processors (DSP), and their shared weaknesses of low scalability. Here, we report a photonic BSS approach that inherits the advantages of optical devices and fully fulfils its “blindness” aspect. Using a microring weight bank integrated on a photonic chip, we demonstrate energy-efficient, wavelength-division multiplexing (WDM) scalable BSS across 19.2 GHz processing bandwidth. Our system also has a high (9-bit) resolution for signal demixing thanks to a recently developed dithering control method, resulting in higher signal-to-interference ratios (SIR) even for ill-conditioned mixtures. Nature Publishing Group UK 2023-02-27 /pmc/articles/PMC9971366/ /pubmed/36849533 http://dx.doi.org/10.1038/s41467-023-36814-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Zhang, Weipeng Tait, Alexander Huang, Chaoran Ferreira de Lima, Thomas Bilodeau, Simon Blow, Eric C. Jha, Aashu Shastri, Bhavin J. Prucnal, Paul Broadband physical layer cognitive radio with an integrated photonic processor for blind source separation |
title | Broadband physical layer cognitive radio with an integrated photonic processor for blind source separation |
title_full | Broadband physical layer cognitive radio with an integrated photonic processor for blind source separation |
title_fullStr | Broadband physical layer cognitive radio with an integrated photonic processor for blind source separation |
title_full_unstemmed | Broadband physical layer cognitive radio with an integrated photonic processor for blind source separation |
title_short | Broadband physical layer cognitive radio with an integrated photonic processor for blind source separation |
title_sort | broadband physical layer cognitive radio with an integrated photonic processor for blind source separation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9971366/ https://www.ncbi.nlm.nih.gov/pubmed/36849533 http://dx.doi.org/10.1038/s41467-023-36814-4 |
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