Deep Joint Source-Channel Coding for Wireless Image Transmission

Autor:	Bourtsoulatze, Eirina, Kurka, David Burth, Gunduz, Deniz
Rok vydání:	2018
Předmět:	Computer Science - Information Theory Computer Science - Machine Learning Electrical Engineering and Systems Science - Signal Processing Statistics - Machine Learning
Druh dokumentu:	Working Paper
DOI:	10.1109/TCCN.2019.2919300
Popis:	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 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 non-trainable 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-to-noise ratio (SNR) and channel bandwidth values in the presence of additive white Gaussian noise (AWGN). 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 SNR value assumed during training. In the case of a slow Rayleigh fading channel, deep JSCC learns noise resilient coded representations and significantly outperforms separation-based digital communication at all SNR and channel bandwidth values. Comment: To appear in IEEE Transactions on Cognitive Communications and Networking
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1809.01733 Zobrazit plný text záznamu View this record from Arxiv