Title | Apriori-LLR-Threshold-Assisted K-Best Sphere Detection for MIMO Channels |
Authors | Li Wang, Xu Lei, Shen Chen, Lajos Hanzo |
Journal/Conference name | VTC 2008 Spring |
Date of Publication | 11-14 May 2008 |
Work Area | Delivery Efficiency |
Abstract | When the maximum number of best candidates retained at each tree search level of the K-Best Sphere Detection (SD) is kept low for the sake of maintaining a low memory requirement and computational complexity, the SD may result in a considerable performance degradation in comparison to the full-search based Maximum Likelihood (ML) detector. In order to circumvent this problem, in this contribution we propose a novel complexity-reduction scheme, referred to as the Apriori-LLR-Threshold (ALT) based technique for the K-best SD, which was based on the exploitation of the a priori LLRs provided by the outer channel decoder in the context of iterative detection aided channel coded systems. For example, given a BER of 10^{-5}, a near-ML performance is achieved in an (8x4)-element rank-deficient 4-QAM system, despite imposing a factor two reduced detection candidate list generation related complexity and a factor eight reduced extrinsic LLR calculation related complexity, when compared to the conventional SD-aided iterative benchmark receiver. The associated memory requirements were also reduced by a factor of eight. |