Efficient Soft MIMO Detection Algorithms Based on Differential Metrics

Wang Yueh Chang, Ming Xian Chang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publication2017 IEEE 85th Vehicular Technology Conference, VTC Spring 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509059324
Publication statusPublished - 2017 Nov 14
Event85th IEEE Vehicular Technology Conference, VTC Spring 2017 - Sydney, Australia
Duration: 2017 Jun 42017 Jun 7

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252


Other85th IEEE Vehicular Technology Conference, VTC Spring 2017

All Science Journal Classification (ASJC) codes

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

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