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Low-Complexity Channel Estimation in Large-Scale MIMO using Polynomial Expansion
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. SUPELEC, Gif-sur-Yvette, France. (Alcatel-Lucent Chair on Flexible Radio)
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-3599-5584
SUPELEC, Gif-sur-Yvette, France. (Alcatel-Lucent Chair on Flexible Radio)
2013 (English)In: 2013 IEEE 24th International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC), IEEE conference proceedings, 2013, 1157-1162 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper considers pilot-based channel estimation in large-scale multiple-input multiple-output (MIMO) communication systems, also known as "massive MIMO". Unlike previous works on this topic, which mainly considered the impact of inter-cell disturbance due to pilot reuse (so-called pilot contamination), we are concerned with the computational complexity. The conventional minimum mean square error (MMSE) and minimum variance unbiased (MVU) channel estimators rely on inverting covariance matrices, which has cubic complexity in the multiplication of number of antennas at each side. Since this is extremely expensive when there are hundreds of antennas, we propose to approximate the inversion by an L-order matrix polynomial. A set of low-complexity Bayesian channel estimators, coined Polynomial ExpAnsion CHannel (PEACH) estimators, are introduced. The coefficients of the polynomials are optimized to yield small mean square error (MSE). We show numerically that near-optimal performance is achieved with low polynomial orders. In practice, the order L can be selected to balance between complexity and MSE. Interestingly, pilot contamination is beneficial to the PEACH estimators in the sense that smaller L can be used to achieve near-optimal MSEs.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2013. 1157-1162 p.
Keyword [en]
Channel estimator, Covariance matrices, Matrix polynomials, Minimum mean square errors (MMSE), Multiple-input multiple-output communication systems, Near-optimal performance, Pilot contaminations, Polynomial expansion
National Category
Signal Processing Telecommunications
Identifiers
URN: urn:nbn:se:kth:diva-133210DOI: 10.1109/PIMRC.2013.6666313ISI: 000346481201044Scopus ID: 2-s2.0-84893224418ISBN: 978-146736235-1 (print)OAI: oai:DiVA.org:kth-133210DiVA: diva2:659953
Conference
2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, PIMRC 2013; London; United Kingdom; 8 September 2013 through 11 September 2013
Note

QC 20140320

Available from: 2013-10-28 Created: 2013-10-28 Last updated: 2015-12-03Bibliographically approved

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