Efficient Soft MIMO Detection Algorithms Based on Differential Metrics

Wang Yueh Chang, Ming Xian Chang

研究成果: Conference contribution

摘要

The multiple-input multiple-output (MIMO) technology can make full use of spectrum and increase the communication throughput. In the coded MIMO system, the main challenge of soft detection is to efficiently generate the loglikelihood ratios (LLR) values for channel decoder. The exact maximum a posteriori (MAP) probability detection can guarantee the optimal performance, but its realization is difficult due to its enormous complexity. In this paper, we propose efficient soft detection algorithms based on differential metrics. We apply the differential metrics for the list sphere decoding, and propose the list gradient algorithm. We further propose a novel algorithm that can generate the values of LLR and provide a trade-off between performance and complexity. The proposed algorithms do not need the QR decomposition and matrix inversion. The proposed algorithms have fixed complexity, and are appropriate for pipelined hardware implementation. The numerical results verify the efficiency of our algorithms.

原文English
主出版物標題2017 IEEE 85th Vehicular Technology Conference, VTC Spring 2017 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781509059324
DOIs
出版狀態Published - 2017 十一月 14
事件85th IEEE Vehicular Technology Conference, VTC Spring 2017 - Sydney, Australia
持續時間: 2017 六月 42017 六月 7

出版系列

名字IEEE Vehicular Technology Conference
2017-June
ISSN(列印)1550-2252

Other

Other85th IEEE Vehicular Technology Conference, VTC Spring 2017
國家Australia
城市Sydney
期間17-06-0417-06-07

指紋

Multiple-input multiple-output (MIMO)
Metric
Log-likelihood Ratio
QR Decomposition
Detection Probability
Matrix Inversion
Multiple-input multiple-output (MIMO) Systems
Maximum a Posteriori
Gradient Algorithm
Hardware Implementation
Decoding
Throughput
Trade-offs
Verify
Numerical Results
Decomposition
Hardware
Communication

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

引用此文

Chang, W. Y., & Chang, M. X. (2017). Efficient Soft MIMO Detection Algorithms Based on Differential Metrics. 於 2017 IEEE 85th Vehicular Technology Conference, VTC Spring 2017 - Proceedings [8108240] (IEEE Vehicular Technology Conference; 卷 2017-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VTCSpring.2017.8108240
Chang, Wang Yueh ; Chang, Ming Xian. / Efficient Soft MIMO Detection Algorithms Based on Differential Metrics. 2017 IEEE 85th Vehicular Technology Conference, VTC Spring 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. (IEEE Vehicular Technology Conference).
@inproceedings{bccff68d013e448aad76a8a83e80ea00,
title = "Efficient Soft MIMO Detection Algorithms Based on Differential Metrics",
abstract = "The multiple-input multiple-output (MIMO) technology can make full use of spectrum and increase the communication throughput. In the coded MIMO system, the main challenge of soft detection is to efficiently generate the loglikelihood ratios (LLR) values for channel decoder. The exact maximum a posteriori (MAP) probability detection can guarantee the optimal performance, but its realization is difficult due to its enormous complexity. In this paper, we propose efficient soft detection algorithms based on differential metrics. We apply the differential metrics for the list sphere decoding, and propose the list gradient algorithm. We further propose a novel algorithm that can generate the values of LLR and provide a trade-off between performance and complexity. The proposed algorithms do not need the QR decomposition and matrix inversion. The proposed algorithms have fixed complexity, and are appropriate for pipelined hardware implementation. The numerical results verify the efficiency of our algorithms.",
author = "Chang, {Wang Yueh} and Chang, {Ming Xian}",
year = "2017",
month = "11",
day = "14",
doi = "10.1109/VTCSpring.2017.8108240",
language = "English",
series = "IEEE Vehicular Technology Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2017 IEEE 85th Vehicular Technology Conference, VTC Spring 2017 - Proceedings",
address = "United States",

}

Chang, WY & Chang, MX 2017, Efficient Soft MIMO Detection Algorithms Based on Differential Metrics. 於 2017 IEEE 85th Vehicular Technology Conference, VTC Spring 2017 - Proceedings., 8108240, IEEE Vehicular Technology Conference, 卷 2017-June, Institute of Electrical and Electronics Engineers Inc., 85th IEEE Vehicular Technology Conference, VTC Spring 2017, Sydney, Australia, 17-06-04. https://doi.org/10.1109/VTCSpring.2017.8108240

Efficient Soft MIMO Detection Algorithms Based on Differential Metrics. / Chang, Wang Yueh; Chang, Ming Xian.

2017 IEEE 85th Vehicular Technology Conference, VTC Spring 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. 8108240 (IEEE Vehicular Technology Conference; 卷 2017-June).

研究成果: Conference contribution

TY - GEN

T1 - Efficient Soft MIMO Detection Algorithms Based on Differential Metrics

AU - Chang, Wang Yueh

AU - Chang, Ming Xian

PY - 2017/11/14

Y1 - 2017/11/14

N2 - The multiple-input multiple-output (MIMO) technology can make full use of spectrum and increase the communication throughput. In the coded MIMO system, the main challenge of soft detection is to efficiently generate the loglikelihood ratios (LLR) values for channel decoder. The exact maximum a posteriori (MAP) probability detection can guarantee the optimal performance, but its realization is difficult due to its enormous complexity. In this paper, we propose efficient soft detection algorithms based on differential metrics. We apply the differential metrics for the list sphere decoding, and propose the list gradient algorithm. We further propose a novel algorithm that can generate the values of LLR and provide a trade-off between performance and complexity. The proposed algorithms do not need the QR decomposition and matrix inversion. The proposed algorithms have fixed complexity, and are appropriate for pipelined hardware implementation. The numerical results verify the efficiency of our algorithms.

AB - The multiple-input multiple-output (MIMO) technology can make full use of spectrum and increase the communication throughput. In the coded MIMO system, the main challenge of soft detection is to efficiently generate the loglikelihood ratios (LLR) values for channel decoder. The exact maximum a posteriori (MAP) probability detection can guarantee the optimal performance, but its realization is difficult due to its enormous complexity. In this paper, we propose efficient soft detection algorithms based on differential metrics. We apply the differential metrics for the list sphere decoding, and propose the list gradient algorithm. We further propose a novel algorithm that can generate the values of LLR and provide a trade-off between performance and complexity. The proposed algorithms do not need the QR decomposition and matrix inversion. The proposed algorithms have fixed complexity, and are appropriate for pipelined hardware implementation. The numerical results verify the efficiency of our algorithms.

UR - http://www.scopus.com/inward/record.url?scp=85040577417&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85040577417&partnerID=8YFLogxK

U2 - 10.1109/VTCSpring.2017.8108240

DO - 10.1109/VTCSpring.2017.8108240

M3 - Conference contribution

AN - SCOPUS:85040577417

T3 - IEEE Vehicular Technology Conference

BT - 2017 IEEE 85th Vehicular Technology Conference, VTC Spring 2017 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Chang WY, Chang MX. Efficient Soft MIMO Detection Algorithms Based on Differential Metrics. 於 2017 IEEE 85th Vehicular Technology Conference, VTC Spring 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. 8108240. (IEEE Vehicular Technology Conference). https://doi.org/10.1109/VTCSpring.2017.8108240