Scalable Wideband Principal Component Analysis via Microwave Photonics

Thomas Ferreira De Lima; Alexander N. Tait; Mitchell A. Nahmias; Bhavin J. Shastri; Paul R. Prucnal

doi:10.1109/JPHOT.2016.2538759

Scalable Wideband Principal Component Analysis via Microwave Photonics

Thomas Ferreira De Lima, Alexander N. Tait, Mitchell A. Nahmias, Bhavin J. Shastri, Paul R. Prucnal

Research output: Contribution to journal › Article › peer-review

11 Scopus citations

Abstract

Microwave photonics (MWP) provides advantages in bandwidth performance and fan-in scalability that are far superior to electronic counterparts. Processing of many channels at high bandwidths is not easily achievable in any electronic implementation. We consider an MWP system that iteratively performs principal component analysis (PCA) on partially correlated, eight-channel, and 13-GBd signals. The system that is presented is able to adapt to oscillations in interchannel correlations and follow changing principal components. Wideband multidimensional techniques are relevant to > 10-GHz radio systems and could bring solutions for intelligent radio communications and information sensing.

Original language	English (US)
Article number	7426292
Journal	IEEE Photonics Journal
Volume	8
Issue number	2
DOIs	https://doi.org/10.1109/JPHOT.2016.2538759
State	Published - Apr 2016

All Science Journal Classification (ASJC) codes

Atomic and Molecular Physics, and Optics
Electrical and Electronic Engineering

Keywords

Information processing
Microwave photonics signal processing
Optical neural systems

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10.1109/JPHOT.2016.2538759

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title = "Scalable Wideband Principal Component Analysis via Microwave Photonics",

abstract = "Microwave photonics (MWP) provides advantages in bandwidth performance and fan-in scalability that are far superior to electronic counterparts. Processing of many channels at high bandwidths is not easily achievable in any electronic implementation. We consider an MWP system that iteratively performs principal component analysis (PCA) on partially correlated, eight-channel, and 13-GBd signals. The system that is presented is able to adapt to oscillations in interchannel correlations and follow changing principal components. Wideband multidimensional techniques are relevant to > 10-GHz radio systems and could bring solutions for intelligent radio communications and information sensing.",

keywords = "Information processing, Microwave photonics signal processing, Optical neural systems",

author = "{Ferreira De Lima}, Thomas and Tait, {Alexander N.} and Nahmias, {Mitchell A.} and Shastri, {Bhavin J.} and Prucnal, {Paul R.}",

note = "Funding Information: This work was supported by Lockheed Martin Corporation under Award 4101916239. Publisher Copyright: {\textcopyright} 2016 IEEE.",

year = "2016",

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doi = "10.1109/JPHOT.2016.2538759",

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AU - Ferreira De Lima, Thomas

AU - Tait, Alexander N.

AU - Nahmias, Mitchell A.

AU - Shastri, Bhavin J.

AU - Prucnal, Paul R.

PY - 2016/4

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AB - Microwave photonics (MWP) provides advantages in bandwidth performance and fan-in scalability that are far superior to electronic counterparts. Processing of many channels at high bandwidths is not easily achievable in any electronic implementation. We consider an MWP system that iteratively performs principal component analysis (PCA) on partially correlated, eight-channel, and 13-GBd signals. The system that is presented is able to adapt to oscillations in interchannel correlations and follow changing principal components. Wideband multidimensional techniques are relevant to > 10-GHz radio systems and could bring solutions for intelligent radio communications and information sensing.

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KW - Microwave photonics signal processing

KW - Optical neural systems

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Scalable Wideband Principal Component Analysis via Microwave Photonics

Abstract

All Science Journal Classification (ASJC) codes

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