A Near-ML Complex K-Best Decoder with Efficient Search Design for MIMO Systems

Open Access
Research Article


A low-complexity near-ML K-Best sphere decoder is proposed. The development of the proposed K-Best sphere decoding algorithm (SDA) involves two stages. First, a new candidate sequence generator (CSG) is proposed. The CSG directly operates in the complex plane and efficiently generates sorted candidate sequences with precise path weights. Using the CSG and an associated parallel comparator, the proposed K-Best SDA can avoid performing a large amount of path weight evaluations and sorting. Next, a new search strategy based on a derived cumulative distribution function (cdf), and an associated efficient procedure is proposed. This search procedure can be directly manipulated in the complex plane and performs ML search in a few preceding layers. It is shown that incorporating detection ordering into the proposed SDA offers a systematic method for determining the numbers of required ML search layers. With the above features, the proposed SDA is shown to provide near ML performance with a lower complexity requirement than conventional K-Best SDAs.


Sorting Complex Plane Cumulative Distribution Function Search Procedure MIMO System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Publisher note

To access the full article, please see PDF.

Copyright information

© Chung-Jung Huang et al. 2010

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Authors and Affiliations

  • Chung-Jung Huang
    • 1
  • Chih-Sheng Sung
    • 1
  • Ta-Sung Lee
    • 1
  1. 1.Department of Electrical EngineeringNational Chiao Tung UniversityHsinchuTaiwan

Personalised recommendations