Robust and Distributed Estimation of Wireless Channel Using Maximum Correntropy Criteria

Channel estimation is inevitable in wireless communication to decode the correct symbol at the receiver end. Several adaptive algorithms are proposed in the literature to estimate the channel state information. The gradient-based adaptive algorithms like least mean square (LMS) works well in additive white Gaussian noise (AWGN) environment, but these algorithms are not robust against the impulsive noise. Researchers have used robust techniques based on logarithmic, sigmoidal cost functions, and switched norm algorithms like Hubers. In this paper, a modified cost function based on maximum correntropy criteria (MCC) is used in the adaptive algorithm to improve the steady-state performance. To make the algorithm energy-efficient distributed adaptive technique based on the incremental way of cooperation is incorporated. It is observed that the proposed robust and distributed algorithm has better steady-state and transient performance as compared to the above-stated algorithms.