Deep Joint Source-channel Coding for Wireless Image Transmission

We propose a novel joint source and channel coding (JSCC) scheme for wireless image transmission that departs from the conventional use of explicit source and channel codes for compression and error correction, and directly maps the image pixel values to the complex-valued channel input signal. Our...

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Veröffentlicht in:Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) S. 4774 - 4778
Hauptverfasser: Bourtsoulatze, Eirina, Kurka, David Burth, Gunduz, Deniz
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.05.2019
Schlagworte:
Bandwidth
Channel estimation
Deep neural networks
Fading channels
Image coding
Image communication
Image reconstruction
joint source-channel coding
Signal to noise ratio
Speech processing
Training
Wireless communication
wireless image transmission
ISSN:2379-190X
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Zusammenfassung:We propose a novel joint source and channel coding (JSCC) scheme for wireless image transmission that departs from the conventional use of explicit source and channel codes for compression and error correction, and directly maps the image pixel values to the complex-valued channel input signal. Our encoder-decoder pair form an autoencoder with a non-trainable layer in the middle, which represents the noisy communication channel. Our results show that the proposed deep JSCC scheme outperforms separation-based digital transmission at low signal-to-noise ratio (SNR) and low channel bandwidth regimes in the presence of additive white Gaussian noise (AWGN). More strikingly, deep JSCC does not suffer from the "cliff effect" 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 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.
ISSN:2379-190X
DOI:10.1109/ICASSP.2019.8683463