Implicit vs. Explicit Approximate Matrix Inversion for Wideband Massive MU-MIMO Data Detection
dc.citation.journalTitle | Journal of Signal Processing Systems | |
dc.contributor.author | Wu, Michael | |
dc.contributor.author | Yin, Bei | |
dc.contributor.author | Li, Kaipeng | |
dc.contributor.author | Dick, Chris | |
dc.contributor.author | Cavallaro, Joseph R. | |
dc.contributor.author | Studer, Christoph | |
dc.date.accessioned | 2017-12-13T15:02:25Z | |
dc.date.available | 2017-12-13T15:02:25Z | |
dc.date.issued | 2017 | |
dc.description.abstract | Massive 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. | |
dc.identifier.citation | Wu, 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. | |
dc.identifier.digital | 2017_JSPS_SI_Kaipeng_GC16 | |
dc.identifier.doi | https://doi.org/10.1007/s11265-017-1313-z | |
dc.identifier.uri | https://hdl.handle.net/1911/98876 | |
dc.language.iso | eng | |
dc.publisher | Springer | |
dc.rights | This is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Springer. | |
dc.subject.keyword | equalization | |
dc.subject.keyword | linear data detection | |
dc.subject.keyword | massive multi-user MIMO | |
dc.subject.keyword | matrix inversion | |
dc.subject.keyword | Neumann series expansion | |
dc.subject.keyword | SC-FDMA | |
dc.subject.keyword | OFDM | |
dc.title | Implicit vs. Explicit Approximate Matrix Inversion for Wideband Massive MU-MIMO Data Detection | |
dc.type | Journal article | |
dc.type.dcmi | Text | |
dc.type.publication | post-print |
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