A Novel Semi-blind Channel Estimation Scheme for Rayleigh Flat Fading MIMO channels (joint LS estimation and ML detection)

Abstract

In this article, the training-based channel estimation (TBCE) and semi-blind channel estimation (SBCE) schemes in Rayleigh flat fading multiple-input multiple-output (MIMO) channels are investigated. First, least squares (LS), linear minimum mean square error (LMMSE), maximum likelihood (ML), and maximum a posteriori (MAP) channel estimators are presented and simulated. Owing to faster processing and lower bit error rate (BER), the LS estimator is the proper choice for both TBCE and SBCE-ML. It is illustrated that when the number of transmitter and/or receiver antennas increases, the performance of both TBCE and SBCE-ML schemes significantly improves. In addition, Alamouti coding has more effect on the performance of SBCE-ML rather than TBCE. Comparing LS-based TBCE and LS-based SBCE-ML, the simulation results introduce the most appropriate channel estimation method that uses an iterative algorithm. This new proposed method is based on LS estimator and ML detector. Simulation results of this investigation show that LS-based SBCE-ML method compared with LS-based TBCE method in different signal-to-noise ratios (SNRs) offers lower BER, 25% higher processing time, and 100 times lower training bits.