Broadband physical layer cognitive radio with an integrated photonic processor for blind source separation

Weipeng Zhang (), Alexander Tait, Chaoran Huang, Thomas Ferreira de Lima, Simon Bilodeau, Eric C. Blow, Aashu Jha, Bhavin J. Shastri and Paul Prucnal ()
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Weipeng Zhang: Princeton University
Alexander Tait: Queen’s University
Chaoran Huang: The Chinese University of Hong Kong
Thomas Ferreira de Lima: Princeton University
Simon Bilodeau: Princeton University
Eric C. Blow: Princeton University
Aashu Jha: Princeton University
Bhavin J. Shastri: Queen’s University
Paul Prucnal: Princeton University

Nature Communications, 2023, vol. 14, issue 1, 1-10

Abstract: Abstract 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.

Date: 2023
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DOI: 10.1038/s41467-023-36814-4

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