Tensor Decompositions in Wireless Communications and MIMO Radar

The emergence of big data and the multidimensional nature of wireless communication signals present significant opportunities for exploiting the versatility of tensor decompositions in associated data analysis and signal processing. The uniqueness of tensor decompositions, unlike matrix-based method...

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Vydáno v:	IEEE journal of selected topics in signal processing Ročník 15; číslo 3; s. 438 - 453
Hlavní autoři:	Chen, Hongyang, Ahmad, Fauzia, Vorobyov, Sergiy, Porikli, Fatih
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.04.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algebra Analytical models CDMA Channel estimation Data analysis Decomposition Design parameters Mathematical models millimeter wave MIMO MIMO radar parallel factor analysis (PARAFAC) Parameter estimation Radar rank Signal processing symbol recovery Tensor analysis tensor decomposition tensor factorization Tensors transmit beamspace tucker model Wireless communication Wireless communications
ISSN:	1932-4553, 1941-0484
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Abstract	The emergence of big data and the multidimensional nature of wireless communication signals present significant opportunities for exploiting the versatility of tensor decompositions in associated data analysis and signal processing. The uniqueness of tensor decompositions, unlike matrix-based methods, can be guaranteed under very mild and natural conditions. Harnessing the power of multilinear algebra through tensor analysis in wireless signal processing, channel modeling, and parametric channel estimation provides greater flexibility in the choice of constraints on data properties and permits extraction of more general latent data components than matrix-based methods. Tensor analysis has also found applications in Multiple-Input Multiple-Output (MIMO) radar because of its ability to exploit the inherent higher-dimensional signal structures therein. In this paper, we provide a broad overview of tensor analysis in wireless communications and MIMO radar. More specifically, we cover topics including basic tensor operations, common tensor decompositions via canonical polyadic and Tucker factorization models, wireless communications applications ranging from blind symbol recovery to channel parameter estimation, and transmit beamspace design and target parameter estimation in MIMO radar.
AbstractList	The emergence of big data and the multidimensional nature of wireless communication signals present significant opportunities for exploiting the versatility of tensor decompositions in associated data analysis and signal processing. The uniqueness of tensor decompositions, unlike matrix-based methods, can be guaranteed under very mild and natural conditions. Harnessing the power of multilinear algebra through tensor analysis in wireless signal processing, channel modeling, and parametric channel estimation provides greater flexibility in the choice of constraints on data properties and permits extraction of more general latent data components than matrix-based methods. Tensor analysis has also found applications in Multiple-Input Multiple-Output (MIMO) radar because of its ability to exploit the inherent higher-dimensional signal structures therein. In this paper, we provide a broad overview of tensor analysis in wireless communications and MIMO radar. More specifically, we cover topics including basic tensor operations, common tensor decompositions via canonical polyadic and Tucker factorization models, wireless communications applications ranging from blind symbol recovery to channel parameter estimation, and transmit beamspace design and target parameter estimation in MIMO radar.
Author	Vorobyov, Sergiy Chen, Hongyang Porikli, Fatih Ahmad, Fauzia
Author_xml	– sequence: 1 givenname: Hongyang orcidid: 0000-0002-7626-0162 surname: Chen fullname: Chen, Hongyang email: dr.h.chen@ieee.org organization: Research Center for Intelligent Network, Zhejiang Lab, Hangzhou, China – sequence: 2 givenname: Fauzia orcidid: 0000-0002-1446-5154 surname: Ahmad fullname: Ahmad, Fauzia email: fauzia.ahmad@temple.edu organization: Department of Electrical and Computer Engineering, Temple University, Philadelphia, PA, USA – sequence: 3 givenname: Sergiy surname: Vorobyov fullname: Vorobyov, Sergiy email: sergiy.vorobyov@aalto.fi organization: Department of Signal Processing and Acoustics, Aalto University, Espoo, Finland – sequence: 4 givenname: Fatih orcidid: 0000-0002-1520-4466 surname: Porikli fullname: Porikli, Fatih email: fatih.porikli@gmail.com organization: Research School of Engineering, Australian National University, Canberra, Australia
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Snippet	The emergence of big data and the multidimensional nature of wireless communication signals present significant opportunities for exploiting the versatility of...
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SubjectTerms	Algebra Analytical models CDMA Channel estimation Data analysis Decomposition Design parameters Mathematical models millimeter wave MIMO MIMO radar parallel factor analysis (PARAFAC) Parameter estimation Radar rank Signal processing symbol recovery Tensor analysis tensor decomposition tensor factorization Tensors transmit beamspace tucker model Wireless communication Wireless communications
Title	Tensor Decompositions in Wireless Communications and MIMO Radar
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