Deep Joint Source-Channel Coding for Wireless Image Transmission

Abstract--We propose a joint source and channel coding (JSCC) technique for wireless image transmission that does not rely on explicit codes for either compression or error correction; instead, it directly maps the image pixel values to the real/complex - valued channel input symbols. We parameterize the encoder and decoder functions by two convolutional neural networks (CNNs), which are trained jointly, and can be considered as an autoencoder with a nontrainable layer in the middle that represents the noisy communication channel. Our results show that the proposed deep JSCC scheme outperforms digital transmission concatenating JPEG or JPEG2000 compression with a capacity achieving channel code at low signal-tonoise ratio (SNR) and channel bandwidth values in the presence of additive white Gaussian noise. More strikingly, deep JSCC does not suffer from the "cliff effect", and it provides a graceful performance degradation as the channel SNR varies with respect to the training SNR. In the case of a slow Rayleigh fading channel, deep JSCC can learn to communicate without explicit pilot signals or channel estimation, and significantly outperforms separation-based digital communication at all SNR and channel bandwidth values. I. Introduction Modern communication systems employ a two step encoding process for the transmission of image/video data (see Figure 1a for an illustration): (i) the image/video data is first compressed with a source coding algorithm in order to get rid of the inherent redundancy, and to reduce the amount of transferred information; and (ii) the compressed bitstream is first encoded with an error correcting code, which enables resilient transmission against errors, and then modulated. Shannon's separation theorem proves that this two-step source and channel coding approach is optimal theoretically in the asymptotic limit of infinitely E. Bourtsoulatze is with the Communications and Information Systems Group, Department of Electronic and Electrical Engineering, University College London, London, UK.