Chih-Sheng Sung

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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(More)
In this paper, a new search strategy based on a derived cumulative distribution function (cdf) is proposed. It is shown that incorporating detection ordering into the conventional K-Best sphere decoding algorithm (SDA) offers a systematic method for determining the numbers of required ML search layers. With the features, applying the proposed search(More)
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