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. 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.