Imperial College London
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ProfessorDenizGunduz

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor in Information Processing
 
 
 
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Contact

 

+44 (0)20 7594 6218d.gunduz Website

 
 
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Assistant

 

Ms Joan O'Brien +44 (0)20 7594 6316

 
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Location

 

1016Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Kurka:2020:10.1109/jsait.2020.2987203,
author = {Kurka, DB and Gunduz, D},
doi = {10.1109/jsait.2020.2987203},
journal = {IEEE Journal on Selected Areas in Information Theory},
pages = {178--193},
title = {DeepJSCC-f: deep joint source-channel coding of images with feedback},
url = {http://dx.doi.org/10.1109/jsait.2020.2987203},
volume = {1},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - We consider wireless transmission of images in the presence of channel output feedback. From a Shannon theoretic perspective feedback does not improve the asymptotic end-to-end performance, and separate source coding followed by capacity-achieving channel coding, which ignores the feedback signal, achieves the optimal performance. It is well known that separation is not optimal in the practical finite blocklength regime; however, there are no known practical joint source-channel coding (JSCC) schemes that can exploit the feedback signal and surpass the performance of separation-based schemes. Inspired by the recent success of deep learning methods for JSCC, we investigate how noiseless or noisy channel output feedback can be incorporated into the transmission system to improve the reconstruction quality at the receiver. We introduce an autoencoder-based JSCC scheme, which we call DeepJSCC-f, that exploits the channel output feedback, and provides considerable improvements in terms of the end-to-end reconstruction quality for fixed-length transmission, or in terms of the average delay for variable-length transmission. To the best of our knowledge, this is the first practical JSCC scheme that can fully exploit channel output feedback, demonstrating yet another setting in which modern machine learning techniques can enable the design of new and efficient communication methods that surpass the performance of traditional structured coding-based designs.
AU  - Kurka,DB
AU  - Gunduz,D
DO  - 10.1109/jsait.2020.2987203
EP  - 193
PY  - 2020///
SN  - 2641-8770
SP  - 178
TI  - DeepJSCC-f: deep joint source-channel coding of images with feedback
T2  - IEEE Journal on Selected Areas in Information Theory
UR  - http://dx.doi.org/10.1109/jsait.2020.2987203
UR  - https://ieeexplore.ieee.org/document/9066966
UR  - http://hdl.handle.net/10044/1/78937
VL  - 1
ER  -
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