Bourtsoulatze, E;
Kurka, DB;
Gündüz, D;
(2019)
Deep Joint Source-Channel Coding for Wireless Image Transmission.
IEEE Transactions on Cognitive Communications and Networking
, 5
(3)
pp. 567-579.
10.1109/TCCN.2019.2919300.
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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 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.
| Type: | Article |
|---|---|
| Title: | Deep Joint Source-Channel Coding for Wireless Image Transmission |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/TCCN.2019.2919300 |
| Publisher version: | https://doi.org/10.1109/TCCN.2019.2919300 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Joint source-channel coding, deep neural networks, image communications |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10089480 |
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