# Transformer-aided Wireless Image Transmission with Channel Feedback

arXiv: https://arxiv.org/abs/2306.09101 · HTML: https://mlsanity.com/paper/2306.09101/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2023.

## Built on by (most cited first, 19 of 19)

- [Joint Source-Channel Coding: Fundamentals and Recent Progress in Practical Designs](https://mlsanity.com/paper/2409.17557/) (2024)
- [Semantics-Guided Diffusion for Deep Joint Source-Channel Coding in Wireless Image Transmission](https://mlsanity.com/paper/2501.01138/) (2025)
- [Diffusion-Aided Joint Source Channel Coding For High Realism Wireless Image Transmission](https://mlsanity.com/paper/2404.17736/) (2024)
- [Deep Learning in Physical Layer: Review on Data Driven End-to-End Communication Systems and their Enabling Semantic Applications](https://mlsanity.com/paper/2401.12800/) (2024)
- [SING: Semantic Image Communications using Null-Space and INN-Guided Diffusion Models](https://mlsanity.com/paper/2503.12484/) (2025)
- [Transformer-Aided Semantic Communications](https://mlsanity.com/paper/2405.01521/) (2024)
- [Rate-Adaptive Generative Semantic Communication Using Conditional Diffusion Models](https://mlsanity.com/paper/2409.02597/) (2024)
- [Efficient Semantic Communication Through Transformer-Aided Compression](https://mlsanity.com/paper/2412.01817/) (2024)
- [Learned Image Transmission with Hierarchical Variational Autoencoder](https://mlsanity.com/paper/2408.16340/) (2024)
- [Generative Semantic Communication for Text-to-Speech Synthesis](https://mlsanity.com/paper/2410.03459/) (2024)
- [Deep Learning-Based Image Compression for Wireless Communications: Impacts on Reliability,Throughput, and Latency](https://mlsanity.com/paper/2411.10650/) (2024)
- [SNR-EQ-JSCC: Joint Source-Channel Coding with SNR-Based Embedding and Query](https://mlsanity.com/paper/2501.04732/) (2025)
- [SecDiff: Diffusion-Aided Secure Deep Joint Source-Channel Coding Against Adversarial Attacks](https://mlsanity.com/paper/2511.01466/) (2025)
- [Interplay of Semantic Communication and Knowledge Learning](https://mlsanity.com/paper/2402.03339/) (2024)
- [Variational Source-Channel Coding for Semantic Communication](https://mlsanity.com/paper/2410.08222/) (2024)
- [A Channel-Triggered Backdoor Attack on Wireless Semantic Image Reconstruction](https://mlsanity.com/paper/2503.23866/) (2025)
- [Optimization of Private Semantic Communication Performance: An Uncooperative Covert Communication Method](https://mlsanity.com/paper/2508.07586/) (2025)
- [Adaptive Semantic Communication for Wireless Image Transmission Leveraging Mixture-of-Experts Mechanism](https://mlsanity.com/paper/2604.02691/) (2026)
- [TISC: A Text-Driven Image Semantic Communication System for Faithful Reconstruction](https://mlsanity.com/paper/2608.16100/) (2026)

## Abstract (arXiv)

This paper presents a novel wireless image transmission paradigm that can exploit feedback from the receiver, called DeepJSCC-ViT-f. We consider a block feedback channel model, where the transmitter receives noiseless/noisy channel output feedback after each block. The proposed scheme employs a single encoder to facilitate transmission over multiple blocks, refining the receiver's estimation at each block. Specifically, the unified encoder of DeepJSCC-ViT-f can leverage the semantic information from the source image, and acquire channel state information and the decoder's current belief about the source image from the feedback signal to generate coded symbols at each block. Numerical experiments show that our DeepJSCC-ViT-f scheme achieves state-of-the-art transmission performance with robustness to noise in the feedback link. Additionally, DeepJSCC-ViT-f can adapt to the channel condition directly through feedback without the need for separate channel estimation. We further extend the scope of the DeepJSCC-ViT-f approach to include the broadcast channel, which enables the transmitter to generate broadcast codes in accordance with signal semantics and channel feedback from individual receivers.