# Impulsive Noise Detection in OFDM-based Systems: A Deep Learning Perspective

## TL;DR

This paper introduces a deep learning-based two-stage method for impulsive noise detection and mitigation in OFDM systems, significantly improving bit error rate performance over traditional threshold-based techniques.

## Contribution

It presents a novel deep neural network approach for impulsive noise detection in OFDM systems, demonstrating robustness and superior performance compared to classic methods.

## Key findings

- DNN-based approach outperforms traditional blanking and clipping methods.
- The method is robust to model mismatch and bursty impulsive environments.
- Simulation results confirm improved bit error rate performance.

## Abstract

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.