Implicit vs. Explicit Approximate Matrix Inversion for Wideband Massive MU-MIMO Data Detection

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dc.citation.journalTitleJournal of Signal Processing Systemsen_US
dc.contributor.authorWu, Michaelen_US
dc.contributor.authorYin, Beien_US
dc.contributor.authorLi, Kaipengen_US
dc.contributor.authorDick, Chrisen_US
dc.contributor.authorCavallaro, Joseph R.en_US
dc.contributor.authorStuder, Christophen_US
dc.date.accessioned2017-12-13T15:02:25Zen_US
dc.date.available2017-12-13T15:02:25Zen_US
dc.date.issued2017en_US
dc.description.abstractMassive multi-user (MU) MIMO wireless technology promises improved spectral efficiency compared to that of traditional cellular systems. While data-detection algorithms that rely on linear equalization achieve near-optimal error-rate performance for massive MU-MIMO systems, they require the solution to large linear systems at high throughput and low latency, which results in excessively high receiver complexity. In this paper, we investigate a variety of exact and approximate equalization schemes that solve the system of linear equations either explicitly (requiring the computation of a matrix inverse) or implicitly (by directly computing the solution vector). We analyze the associated performance/complexity trade-offs, and we show that for small base-station (BS)-to-user-antenna ratios, exact and implicit data detection using the Cholesky decomposition achieves near-optimal performance at low complexity. In contrast, implicit data detection using approximate equalization methods results in the best trade-off for large BS-to-user-antenna ratios. By combining the advantages of exact, approximate, implicit, and explicit matrix inversion, we develop a new frequency-adaptive e qualizer (FADE), which outperforms existing data-detection methods in terms of performance and complexity for wideband massive MU-MIMO systems.en_US
dc.identifier.citationWu, Michael, Yin, Bei, Li, Kaipeng, et al.. "Implicit vs. Explicit Approximate Matrix Inversion for Wideband Massive MU-MIMO Data Detection." <i>Journal of Signal Processing Systems,</i> (2017) Springer: https://doi.org/10.1007/s11265-017-1313-z.en_US
dc.identifier.digital2017_JSPS_SI_Kaipeng_GC16en_US
dc.identifier.doihttps://doi.org/10.1007/s11265-017-1313-zen_US
dc.identifier.urihttps://hdl.handle.net/1911/98876en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.rightsThis is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Springer.en_US
dc.subject.keywordequalizationen_US
dc.subject.keywordlinear data detectionen_US
dc.subject.keywordmassive multi-user MIMOen_US
dc.subject.keywordmatrix inversionen_US
dc.subject.keywordNeumann series expansionen_US
dc.subject.keywordSC-FDMAen_US
dc.subject.keywordOFDMen_US
dc.titleImplicit vs. Explicit Approximate Matrix Inversion for Wideband Massive MU-MIMO Data Detectionen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpost-printen_US
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