Abstract

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.

Highlights

  • Next-generation wireless communication systems are expected to provide users with higher data rate services for video, audio, data, and voice signals

  • Various signal detection schemes can be adopted in multiple-input multiple-output (MIMO) systems, such as linear detection, successive interference cancellation (SIC) [7, 8], and maximum-likelihood (ML) detection

  • By the proposed criterion in (18)-(19), the system performance is insensitive to the choice of K and the number of candidates generated by the candidate sequence generator (CSG) module

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Summary

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

A low-complexity near-ML K-Best sphere decoder is proposed. 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. 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.

Introduction
Signal Model and K-Best SDA
Proposed Sorting Algorithm and Hardware Architecture
Add offset
Proposed Search Strategy for Near-ML Performance
Computer Simulations and Discussions
Findings
Conclusions
Full Text
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