Tensor decompositions in wireless communications and mimo radar

Hongyang Chen, Fauzia Ahmad, Sergiy Vorobyov, Fatih Porikli

Tutkimustuotos: LehtiartikkeliReview Articlevertaisarvioitu

51 Lataukset (Pure)

Abstrakti

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.

AlkuperäiskieliEnglanti
Artikkeli9362250
Sivut438-453
Sivumäärä16
JulkaisuIEEE Journal on Selected Topics in Signal Processing
Vuosikerta15
Numero3
Varhainen verkossa julkaisun päivämäärä2021
DOI - pysyväislinkit
TilaJulkaistu - huhtikuuta 2021
OKM-julkaisutyyppiA2 Arvio tiedejulkaisuussa (artikkeli)

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