Superposition Signal Input Decoding for Lattice Reduction-Aided MIMO Receivers

Satoshi DENNO; Koki KASHIHARA; Yafei HOU

doi:10.1587/transcom.2022EBP3051

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Superposition Signal Input Decoding for Lattice Reduction-Aided MIMO Receivers

Satoshi DENNO, Koki KASHIHARA, Yafei HOU

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Summary :

This paper proposes a novel approach to low complexity soft input decoding for lattice reduction-aided MIMO receivers. The proposed approach feeds a soft input decoder with soft signals made from hard decision signals generated by using a lattice reduction-aided linear detector. The soft signal is a weighted-sum of some candidate vectors that are near by the hard decision signal coming out from the lattice reduction-aided linear detector. This paper proposes a technique to adjust the weight adapt to the channel for the higher transmission performance. Furthermore, we propose to introduce a coefficient that is used for the weights in order to enhance the transmission performance. The transmission performance is evaluated in a 4×4 MIMO channel. When a linear MMSE filter or a serial interference canceller is used as the linear detector, the proposed technique achieves about 1.0dB better transmission performance at the BER of 10^-5 than the decoder fed with the hard decision signals. In addition, the low computational complexity of the proposed technique is quantitatively evaluated.

Publication: IEICE TRANSACTIONS on Communications Vol.E106-B No.2 pp.184-192

Publication Date: 2023/02/01

Publicized: 2022/08/01

Online ISSN: 1745-1345

DOI: 10.1587/transcom.2022EBP3051

Type of Manuscript: PAPER

Category: Wireless Communication Technologies

Authors

Satoshi DENNO
  Okayama University
Koki KASHIHARA
  Okayama University
Yafei HOU
  Okayama University

Keyword

soft decision, weighted sum, lattice reduction, linear detectors, low computational complexity

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Satoshi DENNO, Koki KASHIHARA, Yafei HOU, "Superposition Signal Input Decoding for Lattice Reduction-Aided MIMO Receivers" in IEICE TRANSACTIONS on Communications, vol. E106-B, no. 2, pp. 184-192, February 2023, doi: 10.1587/transcom.2022EBP3051.
Abstract: This paper proposes a novel approach to low complexity soft input decoding for lattice reduction-aided MIMO receivers. The proposed approach feeds a soft input decoder with soft signals made from hard decision signals generated by using a lattice reduction-aided linear detector. The soft signal is a weighted-sum of some candidate vectors that are near by the hard decision signal coming out from the lattice reduction-aided linear detector. This paper proposes a technique to adjust the weight adapt to the channel for the higher transmission performance. Furthermore, we propose to introduce a coefficient that is used for the weights in order to enhance the transmission performance. The transmission performance is evaluated in a 4×4 MIMO channel. When a linear MMSE filter or a serial interference canceller is used as the linear detector, the proposed technique achieves about 1.0dB better transmission performance at the BER of 10^-5 than the decoder fed with the hard decision signals. In addition, the low computational complexity of the proposed technique is quantitatively evaluated.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2022EBP3051/_p

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@ARTICLE{e106-b_2_184,
author={Satoshi DENNO, Koki KASHIHARA, Yafei HOU, },
journal={IEICE TRANSACTIONS on Communications},
title={Superposition Signal Input Decoding for Lattice Reduction-Aided MIMO Receivers},
year={2023},
volume={E106-B},
number={2},
pages={184-192},
abstract={This paper proposes a novel approach to low complexity soft input decoding for lattice reduction-aided MIMO receivers. The proposed approach feeds a soft input decoder with soft signals made from hard decision signals generated by using a lattice reduction-aided linear detector. The soft signal is a weighted-sum of some candidate vectors that are near by the hard decision signal coming out from the lattice reduction-aided linear detector. This paper proposes a technique to adjust the weight adapt to the channel for the higher transmission performance. Furthermore, we propose to introduce a coefficient that is used for the weights in order to enhance the transmission performance. The transmission performance is evaluated in a 4×4 MIMO channel. When a linear MMSE filter or a serial interference canceller is used as the linear detector, the proposed technique achieves about 1.0dB better transmission performance at the BER of 10^-5 than the decoder fed with the hard decision signals. In addition, the low computational complexity of the proposed technique is quantitatively evaluated.},
keywords={},
doi={10.1587/transcom.2022EBP3051},
ISSN={1745-1345},
month={February},}

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TY - JOUR
TI - Superposition Signal Input Decoding for Lattice Reduction-Aided MIMO Receivers
T2 - IEICE TRANSACTIONS on Communications
SP - 184
EP - 192
AU - Satoshi DENNO
AU - Koki KASHIHARA
AU - Yafei HOU
PY - 2023
DO - 10.1587/transcom.2022EBP3051
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E106-B
IS - 2
JA - IEICE TRANSACTIONS on Communications
Y1 - February 2023
AB - This paper proposes a novel approach to low complexity soft input decoding for lattice reduction-aided MIMO receivers. The proposed approach feeds a soft input decoder with soft signals made from hard decision signals generated by using a lattice reduction-aided linear detector. The soft signal is a weighted-sum of some candidate vectors that are near by the hard decision signal coming out from the lattice reduction-aided linear detector. This paper proposes a technique to adjust the weight adapt to the channel for the higher transmission performance. Furthermore, we propose to introduce a coefficient that is used for the weights in order to enhance the transmission performance. The transmission performance is evaluated in a 4×4 MIMO channel. When a linear MMSE filter or a serial interference canceller is used as the linear detector, the proposed technique achieves about 1.0dB better transmission performance at the BER of 10^-5 than the decoder fed with the hard decision signals. In addition, the low computational complexity of the proposed technique is quantitatively evaluated.
ER -