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Subspace Estimation and Hybrid Precoding for Wideband Millimeter-Wave MIMO Systems
KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-9442-671X
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-3599-5584
2016 (English)In: 2016 50th Asilomar Conference on Signals, Systems and Computers, IEEE Computer Society, 2016, 286-290 p., 7869043Conference paper, Published paper (Refereed)
Abstract [en]

There has been growing interest in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems, which would likely employ hybrid analog-digital precoding with large-scale analog arrays deployed at wide bandwidths. Primary challenges here are how to efficiently estimate the large-dimensional frequency-selective channels and customize the wideband hybrid analog-digital precoders and combiners. To address these challenges, we propose a low-overhead channel subspace estimation technique for the wideband hybrid analog-digital MIMO precoding systems. We first show that the Gram matrix of the frequency-selective channel can be decomposed into frequency-flat and frequency-selective components. Based on this, the Arnoldi approach, leveraging channel reciprocity and time-reversed echoing, is employed to estimate a frequency-flat approximation of the frequency-selective mmWave channels, which is used to design the analog parts. After the analog precoder and combiner design, the low-dimensional frequency-selective channels are estimated using conventional pilot-based channel sounding. Numerical results show that considerable improvement in data-rate performance is possible.

Place, publisher, year, edition, pages
IEEE Computer Society, 2016. 286-290 p., 7869043
Keyword [en]
Millimeter wave MIMO, wideband sparse subspace estimation, hybrid analog-digital precoding, Arnoldi iteration
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-198608DOI: 10.1109/ACSSC.2016.7869043ISI: 000406057400050Scopus ID: 2-s2.0-85016238452ISBN: 9781538639542 (print)OAI: oai:DiVA.org:kth-198608DiVA: diva2:1057599
Conference
50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016, Asilomar Hotel and Conference Grounds, Pacific Grove, United States, 6 November 2016 through 9 November 2016
Note

QC 20170110

Available from: 2016-12-19 Created: 2016-12-19 Last updated: 2017-08-25Bibliographically approved

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Publisher's full textScopushttp://www.asilomarsscconf.org/

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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  • modern-language-association-8th-edition
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Output format
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