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

arXiv: https://arxiv.org/abs/1911.11174 · HTML: https://mlsanity.com/paper/1911.11174/

## Key facts

- Cited by 96 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2019.

## Built on by (most cited first, 30 of 96)

- [Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications](https://mlsanity.com/paper/2207.09353/) (2022)
- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [Semantic Communications: Principles and Challenges](https://mlsanity.com/paper/2201.01389/) (2021)
- [Nonlinear Transform Source-Channel Coding for Semantic Communications](https://mlsanity.com/paper/2112.10961/) (2021)
- [Generative Joint Source-Channel Coding for Semantic Image Transmission](https://mlsanity.com/paper/2211.13772/) (2022)
- [Robust Semantic Communications with Masked VQ-VAE Enabled Codebook](https://mlsanity.com/paper/2206.04011/) (2022)
- [Wireless Deep Video Semantic Transmission](https://mlsanity.com/paper/2205.13129/) (2022)
- [DeepWiVe: Deep-Learning-Aided Wireless Video Transmission](https://mlsanity.com/paper/2111.13034/) (2021)
- [DeepJSCC-Q: Constellation Constrained Deep Joint Source-Channel Coding](https://mlsanity.com/paper/2206.08100/) (2022)
- [WITT: A Wireless Image Transmission Transformer for Semantic Communications](https://mlsanity.com/paper/2211.00937/) (2022)
- [Joint Source-Channel Coding: Fundamentals and Recent Progress in Practical Designs](https://mlsanity.com/paper/2409.17557/) (2024)
- [Task-Oriented Image Transmission for Scene Classification in Unmanned Aerial Systems](https://mlsanity.com/paper/2112.10948/) (2021)
- [Robust Information Bottleneck for Task-Oriented Communication with Digital Modulation](https://mlsanity.com/paper/2209.10382/) (2022)
- [Deep Joint Source-Channel Coding for Wireless Image Transmission with Adaptive Rate Control](https://mlsanity.com/paper/2110.04456/) (2021)
- [High Perceptual Quality Wireless Image Delivery with Denoising Diffusion Models](https://mlsanity.com/paper/2309.15889/) (2023)
- [Deep Joint Source Channel Coding for WirelessImage Transmission with OFDM](https://mlsanity.com/paper/2101.03909/) (2021)
- [Joint Task and Data Oriented Semantic Communications: A Deep Separate Source-channel Coding Scheme](https://mlsanity.com/paper/2302.13580/) (2023)
- [Machine Learning for Large-Scale Optimization in 6G Wireless Networks](https://mlsanity.com/paper/2301.03377/) (2023)
- [Distributed Deep Joint Source-Channel Coding over a Multiple Access Channel](https://mlsanity.com/paper/2211.09920/) (2022)
- [Robust Semantic Communications Against Semantic Noise](https://mlsanity.com/paper/2202.03338/) (2022)
- [Perceptual Learned Source-Channel Coding for High-Fidelity Image Semantic Transmission](https://mlsanity.com/paper/2205.13120/) (2022)
- [Visual Language Model based Cross-modal Semantic Communication Systems](https://mlsanity.com/paper/2407.00020/) (2024)
- [Diffusion-Aided Joint Source Channel Coding For High Realism Wireless Image Transmission](https://mlsanity.com/paper/2404.17736/) (2024)
- [Pragmatic Communication in Multi-Agent Collaborative Perception](https://mlsanity.com/paper/2401.12694/) (2024)
- [DeepJSCC-Q: Channel Input Constrained Deep Joint Source-Channel Coding](https://mlsanity.com/paper/2111.13042/) (2021)
- [GRACE: Loss-Resilient Real-Time Video through Neural Codecs](https://mlsanity.com/paper/2305.12333/) (2023)
- [Semantic Successive Refinement: A Generative AI-aided Semantic Communication Framework](https://mlsanity.com/paper/2408.05112/) (2024)
- [AttentionCode: Ultra-Reliable Feedback Codes for Short-Packet Communications](https://mlsanity.com/paper/2205.14955/) (2022)
- [Toward Adaptive Semantic Communications: Efficient Data Transmission via Online Learned Nonlinear Transform Source-Channel Coding](https://mlsanity.com/paper/2211.04339/) (2022)
- [One-to-Many Semantic Communication Systems: Design, Implementation, Performance Evaluation](https://mlsanity.com/paper/2209.09425/) (2022)

## Abstract (arXiv)

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.