Blind source separation with integrated photonics and reduced dimensional statistics

Philip Y. Ma, Alexander N. Tait, Weipeng Zhang, Emir Ali Karahan, Thomas Ferreira de Lima, Chaoran Huang, Bhavin J. Shastri, Paul R. Prucnal

Research output: Contribution to journalArticlepeer-review

Abstract

Microwave communications have witnessed an incipient proliferation of multi-antenna and opportunistic technologies in the wake of an ever-growing demand for spectrum resources, while facing increasingly difficult network management over widespread channel interference and heterogeneous wireless broadcasting. Radio frequency (RF) blind source separation (BSS) is a powerful technique for demixing mixtures of unknown signals with minimal assumptions, but relies on frequency dependent RF electronics and prior knowledge of the target frequency band. We propose photonic BSS with unparalleled frequency agility supported by the tremendous bandwidths of photonic channels and devices. Specifically, our approach adopts an RF photonic front-end to process RF signals at various frequency bands within the same array of integrated microring resonators, and implements a novel two-step photonic BSS pipeline to reconstruct source identities from the reduced dimensional statistics of front-end output. We verify the feasibility and robustness of our approach by performing the first proof-of-concept photonic BSS experiments on mixed-over-the-air RF signals across multiple frequency bands. The proposed technique lays the groundwork for further research in interference cancellation, radio communications, and photonic information processing.

Original languageEnglish (US)
Pages (from-to)6494-6497
Number of pages4
JournalChinese Optics Letters
Volume45
Issue number23
DOIs
StatePublished - Dec 1 2020

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Blind source separation with integrated photonics and reduced dimensional statistics'. Together they form a unique fingerprint.

Cite this