MIMO Detector for Uncoded Symbols based on Complex Sphere Decoder and Manhattan Metric

Abstract

For communication standards with high transfer rates (WiMAX, WiFi, LTE), which use MIMO (Multiple Input - Mul- tiple Output) systems, detectors with reduced computational complexity that achieve a good Bit Error Rate (BER) performance are of great interest. In the literature, it has been recognized the maximum likelihood (ML) detector as the optimum, but this algorithm experiences an exponential complexity making it an impractical alternative for implemen- tation. However, there are also alternatives such as the K-best and sphere decoder (SD) which can reach a quasi-ML performance with lower computational complexity. This paper presents a variation of the SD algorithm based on the Complex Sphere Decoder and K-best algorithm, named as KLSD, which limits the number of searching points during the predetection and has a performance similar to that given by the algorithm Fixed Complexity Sphere Decoder (FSD) without channel ordering. Furthermore, for the calculation of the weights of each candidate node is proposed to replace the use of the Euclidean distance by the Manhattan Metric to reduce the number calculation performed. When compar- ing performance and complexity against others algorithms, it can be seen that a similar performance without increasing its complexity. Additionally, the results show that the change of metric, does not affect the performance of the proposed algorithm, so it is considered a feasible complexity reduction scheme.