ResNet-WGAN-Based End-to-End Learning for IoV Communication With Unknown Channels

An end-to-end learning framework is proposed to optimize each module jointly in the communication system. Recently, convolutional neural network (CNN) and conditional Generative Adversarial Network (cGAN) are used for end-to-end learning. However, deeper network layers will degrade the effect of CNN. cGAN suffers from unstable training and lacks generative diversity. In this article, we propose the end-to-end learning based on deep residual network (ResNet) and Wasserstein GAN (WGAN) for communication with unknown channels (ResNet-WGAN). First, ResNet is applied to solve the problem of network degradation to extract deeper data features. Second, for unknown channels, WGAN with conditional information is used to fit the channel effect to improve training stability and generative diversity. Finally, we present the simulation results of the ResNet-WGAN under additive white Gaussian noise (AWGN) channel, Rayleigh fading channel, and frequency selective channel. The results demonstrate that the ResNet-WGAN reduces the communication bit error rate (BER) and block error rate (BLER). In particular, this article applies ResNet-WGAN to the Internet of Vehicles (IoV) communication, and the results demonstrate that ResNet-WGAN is more effective.