Adaptive Geman-McClure Estimator for Robust Distributed Channel Estimation

Communication systems are affected by channel distortions. Impulsive noise is one of the significant factors for impairments. The standard additive white Gaussian (AWGN) model and conventional estimation algorithms like least mean square (LMS) its variants tend to be ineffective under such conditions. This paper presents a robust adaptive algorithm using Geman-McClure estimator in diffusion-based distributed network. analytical study on stability analysis carried out two separate statistics: Symmetric α-stable ( SαS) Bernoulli-Gaussian (BG) distribution. computer simulations confirm proposed algorithm's competitive robustness compared Maximum Correntropy Criterion Minimum Kernel Risk Sensitive Loss at high impulsive environment without exponential cost function. Further, efficiency also verified simulating bit error rate designing minimum (MMSE) equalizer with estimated coefficients.