A New ML Detector for SIMO Systems with Imperfect Channel and Carrier Frequency Offset Estimation

The objective of this paper is to develop a new detection algorithm for single input multiple output (SIMO) systems, using maximum likelihood (ML) scheme. The proposed method takes into account both channel and carrier frequency offset (CFO) estimation errors for detection of the transmitted data. Simulation results show that the new algorithm improve performance in the presence of multiple estimation error variances as compared to the conventional method for different modulation schemes.