Adaptive Channel Estimation for MIMO-OFDM Systems in Impulsive Noise Environments

Multiple-input multiple-output (MIMO) systems with orthogonal frequency-division multiplexing (OFDM) techniques provide a credible high-speed wireless communication system. To realize the efficiency of a MIMO-OFDM system, exact channel state information (CSI) is essential. To obtain CSI, several channel estimation algorithms were implemented in Gaussian noise environments. However, real wireless communication environments can be described by non-Gaussian impulsive noise. A robust adaptive channel estimation algorithm based on the maximum correntropy criterion (MCC) learning algorithm is proposed in this paper for a MIMO-OFDM communication system that operates in non-Gaussian impulsive noise environments. The proposed learning algorithm enables considerable improvement in the precision of mean square deviation (MSD) learning performance and in symbol error rate (SER) performance compared with the traditional least mean square (LMS) learning algorithm.