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Low-Complexity Polynomial Channel Estimation in Large-Scale MIMO with Arbitrary Statistics
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. Alcatel-Lucent Chair on Flexible Radio, Supélec, Gif-sur-Yvette, France; Department of Electrical Engineering (ISY), Linköping University, Linköping, Sweden.
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-3599-5584
Alcatel-Lucent Chair on Flexible Radio, SUPELEC, Gif-sur-Yvette, France.
2014 (English)In: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, E-ISSN 1941-0484, Vol. 8, no 5, 815-830 p.Article in journal (Refereed) Published
Abstract [en]

This paper considers pilot-based channel estimation in large-scale multiple-input multiple-output (MIMO) communication systems, also known as “massive MIMO”, where there are hundreds of antennas at one side of the link. Motivated by the fact that computational complexity is one of the main challenges in such systems, a set of low-complexity Bayesian channel estimators, coined Polynomial ExpAnsion CHannel (PEACH) estimators, are introduced for arbitrary channel and interference statistics. While the conventional minimum mean square error (MMSE) estimator has cubic complexity in the dimension of the covariance matrices, due to an inversion operation, our proposed estimators significantly reduce this to square complexity by approximating the inverse by a L-degree matrix polynomial. The coefficients of the polynomial are optimized to minimize the mean square error (MSE) of the estimate. We show numerically that near-optimal MSEs are achieved with low polynomial degrees. We also derive the exact computational complexity of the proposed estimators, in terms of the floating-point operations (FLOPs), by which we prove that the proposed estimators outperform the conventional estimators in large-scale MIMO systems of practical dimensions while providing a reasonable MSEs. Moreover, we show that L needs not scale with the system dimensions to maintain a certain normalized MSE. By analyzing different interference scenarios, we observe that the relative MSE loss of using the low-complexity PEACH estimators is smaller in realistic scenarios with pilot contamination. On the other hand, PEACH estimators are not well suited for noise-limited scenarios with high pilot power; therefore, we also introduce the low-complexity diagonalized estimator that performs well in this regime. Finally, we also investigate numerically how the estimation performance is affected by having imperfect statistical knowledge. High robustness is achieved for large-dimensional matrices by us- ng a new covariance estimate which is an affine function of the sample covariance matrix and a regularization term.

Place, publisher, year, edition, pages
2014. Vol. 8, no 5, 815-830 p.
Keyword [en]
Channel estimation, large-scale MIMO, polyno- mial expansion, pilot contamination, spatial correlation
National Category
Signal Processing Telecommunications
Identifiers
URN: urn:nbn:se:kth:diva-133213DOI: 10.1109/JSTSP.2014.2316063ISI: 000342283600007Scopus ID: 2-s2.0-84907218156OAI: oai:DiVA.org:kth-133213DiVA: diva2:659965
Funder
EU, European Research Council, 305123 MORESwedish Research Council, 2012-228
Note

QC 21041027. Updated from accepted to published.

Available from: 2013-10-28 Created: 2013-10-28 Last updated: 2017-12-06Bibliographically approved

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Bengtsson, Mats

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