Blind source separation with integrated photonics and reduced dimensional statistics

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.

Photonic matrix multiplication lights up photonic accelerator and beyond

“…In 2014, Tait and his colleagues proposed using MRR arrays as a matrix computation method primitive for photonic neural networks 82 and achieved continuous matrix values from -1 to 1 by continuously tuning the MRRs. The WDM-MVM was further used for photonic weight banks 83 – 86 , principal component analysis (PCA) 87 , independent component analysis (ICA) 86 , blind source separation (BSS) 88 , TeraMAC neuromorphic photonic processor 18 , the optical SNN 89 , TeraMAC photonic tensor core 90 , optical CNN 91 – 93 , and photonic convolutional accelerator for the ONN 16 , 94 .…”

“…7e , photonic ICA retrieved the corresponding independent components (ICs) from the received mixture waveforms. By combining the photonic PCA and ICA together, a two-step procedure for a complete photonic BSS pipeline was achieved 88 . The BSS is a powerful technique for achieving signal decomposition with minimal knowledge on either the source characteristics or the mixing process.…”

“…The BSS is a powerful technique for achieving signal decomposition with minimal knowledge on either the source characteristics or the mixing process. Figure 7f gives an example of ICs retrieved from mixed radio-frequency waveforms with the BSS technique 88 .

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“…e Photonic independent component analysis 86 . f Radio-frequency blind source separation 88 . a , b Reproduced from ref.…”

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

“…Nevertheless, the high tuning sensitivity of MRRs incurs vulnerability to environmental fluctuations and thermal crosstalk between adjacent MRRs. As a result, the tuning accuracy was constrained to around 7 bits [26,27], and the related BSS performance was limited, as reflected in low signal-tointerference ratios (SIR) and large unwanted signal residuals [28]. We recently developed a dithering control method [29] that improves the tuning accuracy of MRRs beyond 9 bits.…”