Various efficient generalized sphere decoding (GSD) algorithms have been proposed to approach optimal ML performance for underdetermined linear systems, by transforming the original problem into the full-column-rank o...Various efficient generalized sphere decoding (GSD) algorithms have been proposed to approach optimal ML performance for underdetermined linear systems, by transforming the original problem into the full-column-rank one so that standard SD can be fully applied. However, their design parameters are heuristically set based on observation or the possibility of an ill-conditioned transformed matrix can affect their searching efficiency. This paper presents a better transformation to alleviate the ill-conditioned structure and provides a systematic approach to select design parameters for various GSD algorithms in order to high efficiency. Simulation results on the searching performance confirm that the proposed techniques can provide significant improvement.展开更多
文摘Various efficient generalized sphere decoding (GSD) algorithms have been proposed to approach optimal ML performance for underdetermined linear systems, by transforming the original problem into the full-column-rank one so that standard SD can be fully applied. However, their design parameters are heuristically set based on observation or the possibility of an ill-conditioned transformed matrix can affect their searching efficiency. This paper presents a better transformation to alleviate the ill-conditioned structure and provides a systematic approach to select design parameters for various GSD algorithms in order to high efficiency. Simulation results on the searching performance confirm that the proposed techniques can provide significant improvement.
