An impulsive noise mitigation and channel estimation algorithm based on unitary approximate message passing

Impulsive noise and fading channels can severely damage communication system performance. In this paper, we propose an impulsive noise mitigation and channel estimation algorithm based on unitary approximate message passing-sparse Bayesian learning. The algorithm integrates variational inference and unitary transformation to enhance convergence and robustness, allowing for more accurate impulsive noise and channel estimation The simulation results demonstrate that the proposed algorithm owns superior performance than gaussian generalized approximate message passing-sparse Bayesian learning algorithm in terms of mean square error, normalized mean square error, and computational complexity of impulsive noise and channel estimation as well as bit error rate.