DeepJSCC-f: Deep Joint Source-Channel Coding of Images With Feedback

By David Burth Kurka, Deniz Gündüz. IEEE Journal on Selected Areas in Information Theory, 2019

488 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computational Mechanics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/3bdb100ddc701f20c428c66a257dbf791bd9fddf

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Citations in the corpus

Semantic Communication Systems for Speech Transmission (2021, methodology): “Based on JSCC, an image transmission system, integrating noiseless or noisy channel output feedback, has been investigated to improve image reconstruction [28], where the channel output backpropagates to the transmitter based on a unit delay and additive white Gaussian noise (AWGN) is added to gener”
Wireless Image Transmission Using Deep Source Channel Coding With Attention Modules (2020, methodology): “Akin to [29]–[31], we call the image size n, the channel input size k and R = k/n as the source bandwidth, the channel bandwidth and bandwidth ratio, respectively.”
Semantic Communications: Principles and Challenges (2021, background): “Kurka et al. proposed DeepJSCC [64], [65] for adaptive-bandwidth wireless transmission of images.”
Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications (2022, unknown): “In [228], the DeepJSCC scheme is extended to channels with channel output feedback, and it is shown that feedback can significantly improve the”
Nonlinear Transform Source-Channel Coding for Semantic Communications (2021, methodology): “The deep JSCC scheme employed in this paper is consistent with [10] and [13].”
Task-Oriented Multi-User Semantic Communications (2021, background): “Kurka et al. [13] extended Bourtsoulatze’s work with the channel feedback to improve the quality of image reconstruction.”
Distributed Learning in Wireless Networks: Recent Progress and Future Challenges (2021, methodology): “Recently, an alternative fully data-driven DNNbased scheme, called DeepJSCC, has been introduced [169].”