An impulsive noise mitigation method based on all subcarriers assisted gaussian generalized approximate message passing

In many orthogonal frequency division multiplexing (OFDM) communication scenarios, impulsive noise can severely degrade communication performance. In this paper, we propose an impulsive noise mitigation method based on gaussian generalized approximate message passing-sparse Bayesian learning (GGAMP-SBL) by exploiting all subcarriers. The algorithm achieves accurate estimation of impulsive noise without any prior information and with relatively low complexity. The simulation results show that the algorithm performs well in terms of mean square error and normalized mean square error performance of impulsive noise estimation and bit error rate performance.