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Efficient Lattice Reduction Aided Detectors Under Realistic MIMO Channels.

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Title: Efficient Lattice Reduction Aided Detectors Under Realistic MIMO Channels.
Authors: Mussi, Alex1 alexmmussi@gmail.com, Costa, Bruno1, Abrão, Taufik1 taufik@uel.br
Source: Wireless Personal Communications. Jul2017, Vol. 95 Issue 2, p947-978. 32p.
Subjects: Performance of MIMO systems, Signal detection, Maximum likelihood detection, Interference (Telecommunication), Statistical correlation
Abstract: This contribution analyses the performance of efficient multiple-input-multiple-output (MIMO) detectors under correlated channels and imperfect coefficients channel estimation. A number of signal detection principles and techniques, including the minimum mean squared error detector with and without ordered successive interference cancellation; the sphere decoding MIMO detection, as well as promising near-orthogonal transformation techniques combined with these detectors, namely the lattice reduction and the QR decomposition are analysed under the perspective of complexity-performance tradeoff. While in most of available works perfect channel state information and uncorrelated channels have been considered, herein the complexity-performance tradeoff has been analysed and compared with the maximum likelihood (ML) limit under specific but practical scenarios of interest, namely: high spectral efficiency scenario; channel error estimates; channel/antenna correlation; combined channel errors and correlated channels. Under performance-complexity perspective, the optimum ML-MIMO detector is deployed as reference aiming to evaluate the efficiency and performance degradation of those sub-optimal MIMO detectors operating under hostile channel conditions. [ABSTRACT FROM AUTHOR]
Copyright of Wireless Personal Communications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="AR" term="%22Mussi%2C+Alex%22">Mussi, Alex</searchLink><relatesTo>1</relatesTo><i> alexmmussi@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Costa%2C+Bruno%22">Costa, Bruno</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Abrão%2C+Taufik%22">Abrão, Taufik</searchLink><relatesTo>1</relatesTo><i> taufik@uel.br</i>
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  Data: This contribution analyses the performance of efficient multiple-input-multiple-output (MIMO) detectors under correlated channels and imperfect coefficients channel estimation. A number of signal detection principles and techniques, including the minimum mean squared error detector with and without ordered successive interference cancellation; the sphere decoding MIMO detection, as well as promising near-orthogonal transformation techniques combined with these detectors, namely the lattice reduction and the QR decomposition are analysed under the perspective of complexity-performance tradeoff. While in most of available works perfect channel state information and uncorrelated channels have been considered, herein the complexity-performance tradeoff has been analysed and compared with the maximum likelihood (ML) limit under specific but practical scenarios of interest, namely: high spectral efficiency scenario; channel error estimates; channel/antenna correlation; combined channel errors and correlated channels. Under performance-complexity perspective, the optimum ML-MIMO detector is deployed as reference aiming to evaluate the efficiency and performance degradation of those sub-optimal MIMO detectors operating under hostile channel conditions. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Wireless Personal Communications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Jul2017
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