  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.
