Lossless Dimension Reduction for Integer Least Squares with Application to Sphere Decoding

Loading...
Thumbnail Image
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2020
Major/Subject
Mcode
Degree programme
Language
en
Pages
15
6547-6561
Series
IEEE Transactions on Signal Processing, Volume 68
Abstract
Minimum achievable complexity (MAC) for a maximum likelihood (ML) performance-Achieving detection algorithm is derived. Using the derived MAC, we prove that the conventional sphere decoding (SD) algorithms suffer from an inherent weakness at low SNRs. To find a solution for the low SNR deficiency, we analyze the effect of zero-forcing (ZF) and minimum mean square error (MMSE) linearly detected symbols on the MAC and demonstrate that although they both improve the SD algorithm in terms of the computational complexity, the MMSE linearly detected point has a vital difference at low SNRs. By exploiting the information provided by the MMSE of linear method, we prove the existence of a lossless dimension reduction which can be interpreted as the feasibility of a detection method which is capable of detecting the ML symbol without visiting any nodes at low and high SNRs. We also propose a lossless dimension reduction-Aided detection method which achieves the promised complexity bounds marginally and reduces the overall computational complexity significantly, while obtaining the ML performance. The theoretical analysis is corroborated with numerical simulations.
Description
Keywords
Computational complexity, dimension reduction, integer least squares, maximum likelihood, MIMO detection, minimum mean square error, sphere decoding, tree-search methods
Other note
Citation
Neinavaie , M , Derakhtian , M & Vorobyov , S A 2020 , ' Lossless Dimension Reduction for Integer Least Squares with Application to Sphere Decoding ' , IEEE Transactions on Signal Processing , vol. 68 , 9258407 , pp. 6547-6561 . https://doi.org/10.1109/TSP.2020.3037708