Training Sequence-Aided QRM-MLD Block Signal Detection for Single-Carrier MIMO Spatial Multiplexing

Abstract

QR decomposition with M-algorithm maximum likelihood detection (QRM-MLD) block signal detection can significantly improve the packet error rate (PER) performance of cyclic prefix-inserted single-carrier (CP-SC) multi-input multi-output (MIMO) spatial multiplexing when compared to the frequency-domain spatial filtering based on the minimum mean square error (MMSE) criterion. However, in order to achieve the sufficiently improved performance, the use of a fairly large number M of surviving paths in the M-algorithm is required because if smaller M is used, the probability of removing the correct path at early stages increases. In this paper, to reduce this probability, we proposed a training sequence-aided QRM-MLD block signal detection for SC MIMO spatial multiplexing. We show by computer simulation that training sequence-aided SC (TA-SC) MIMO spatial multiplexing using QRM-MLD block signal detection with M=16 can achieve the PER performance similar to CP-SC MIMO spatial multiplexing with M=1024 in the case of 16QAM and 4×4 MIMO spatial multiplexing.