  Leveraging powerful deep learning techniques, the end-to-end (E2E) learning
of communication system is able to outperform the classical communication
system. Unfortunately, this communication system cannot be trained by deep
learning without known channel. To deal with this problem, a generative
adversarial network (GAN) based training scheme has been recently proposed to
imitate the real channel. However, the gradient vanishing and overfitting
problems of GAN will result in the serious performance degradation of E2E
learning of communication system. To mitigate these two problems, we propose a
residual aided GAN (RA-GAN) based training scheme in this paper. Particularly,
inspired by the idea of residual learning, we propose a residual generator to
mitigate the gradient vanishing problem by realizing a more robust gradient
backpropagation. Moreover, to cope with the overfitting problem, we reconstruct
the loss function for training by adding a regularizer, which limits the
representation ability of RA-GAN. Simulation results show that the trained
residual generator has better generation performance than the conventional
generator, and the proposed RA-GAN based training scheme can achieve the
near-optimal block error rate (BLER) performance with a negligible
computational complexity increase in both the theoretical channel model and the
ray-tracing based channel dataset.
