  Efficient removal of impulsive noise (IN) from received signal is essential
in many communication applications. In this paper, we propose a two stage IN
mitigation approach for orthogonal frequency-division multiplexing (OFDM)-based
communication systems. In the first stage, a deep neural network (DNN) is used
to detect the instances of impulsivity. Then, the detected IN is blanked in the
suppression stage to alleviate the harmful effects of outliers. Simulation
results demonstrate the superior bit error rate (BER) performance of this
approach relative to classic approaches such as blanking and clipping that use
threshold to detect the IN. We demonstrate the robustness of the DNN-based
approach under (i) mismatch between IN models considered for training and
testing, and (ii) bursty impulsive environment when the receiver is empowered
with interleaving techniques.
