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A low-complexity channel training method for efficient SVD beamforming over MIMO channels

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Title: A low-complexity channel training method for efficient SVD beamforming over MIMO channels
Authors: Kettlun, F
Rosas, F
Oberli, C
Item Type: Journal Article
Abstract: Singular value decomposition (SVD) beamforming is an attractive tool for reducing the energy consumption of data transmissions in wireless sensor networks whose nodes are equipped with multiple antennas. However, this method is often not practical due to two important shortcomings: it requires channel state information at the transmitter and the computation of the SVD of the channel matrix is generally too complex. To deal with these issues, we propose a method for establishing an SVD beamforming link without requiring feedback of actual channel or SVD coefficients to the transmitter. Concretely, our method takes advantage of channel reciprocity and a power iteration algorithm (PIA) for determining the precoding and decoding singular vectors from received preamble sequences. A low-complexity version that performs no iterations is proposed and shown to have a signal-to-noise-ratio (SNR) loss within 1 dB of the bit error rate of SVD beamforming with least squares channel estimates. The low-complexity method significantly outperforms maximum ratio combining diversity and Alamouti coding. We also show that the computational cost of the proposed PIA-based method is less than the one of using the Golub–Reinsch algorithm for obtaining the SVD. The number of computations of the low-complexity version is an order of magnitude smaller than with Golub–Reinsch. This difference grows further with antenna array size.
Issue Date: Dec-2021
Date of Acceptance: 24-Jun-2021
URI: http://hdl.handle.net/10044/1/91180
DOI: 10.1186/s13638-021-02026-x
ISSN: 1687-1472
Publisher: Springer
Start Page: 1
End Page: 22
Journal / Book Title: Eurasip Journal on Wireless Communications and Networking
Volume: 2021
Issue: 1
Copyright Statement: © The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Keywords: 0806 Information Systems
0906 Electrical and Electronic Engineering
1005 Communications Technologies
Networking & Telecommunications
Publication Status: Published
Open Access location: https://link.springer.com/article/10.1186/s13638-021-02026-x
Article Number: 151
Online Publication Date: 2021-07-10
Appears in Collections:Department of Brain Sciences



This item is licensed under a Creative Commons License Creative Commons