Communication under Mixed Gaussian-Impulsive Channel: An End-to-End Framework

TL;DR

This paper introduces an end-to-end data-driven framework for communication over mixed Gaussian-impulsive noise channels, utilizing GAN-based noise simulation and wavelet CNN preprocessing to improve bit-error rate performance.

Contribution

It proposes a novel end-to-end system combining GAN-based noise modeling and wavelet CNN preprocessing for robust communication under complex noise conditions.

Findings

Outperforms traditional methods in BER under MGIN.

GAN-based noise simulation effectively models complex noise.

Wavelet CNN preprocessing mitigates impulsive noise effects.

Abstract

In many communication scenarios, the communication signals simultaneously suffer from white Gaussian noise (WGN) and non-Gaussian impulsive noise (IN), i.e., mixed Gaussian-impulsive noise (MGIN). Under MGIN channel, classical communication signal schemes and corresponding detection methods usually can not achieve desirable performance as they are optimized with respect to WGN. Moreover, as the widely adopted IN model has no analytical and general closed-form expression of probability density function (PDF), it is extremely hard to obtain optimal communication signal and corresponding detection schemes based on classical stochastic signal processing theory. To circumvent these difficulties, we propose a data-driven end-to-end framework to address the communication signal design and detection under MGIN channel in this paper. In this proposed framework, a channel noise simulator (CNS) is elaborately designed based on an improved generative adversarial net (GAN) to simulate the MGIN without requirement of any analytical PDF. Meanwhile, a multi-level wavelet convolutional neural network (MWCNN) based preprocessing network is used to mitigate the negative effect of outliers due to the IN. Compared with conventional approaches and existing end-to-end systems, extensive simulation results verify that our proposed novel end-to-end communication system can achieve better performance in terms of bit-error rate (BER) under MGIN environments.