# SNR-adaptive deep joint source-channel coding for wireless image transmission

arXiv: https://arxiv.org/abs/2102.00202 · HTML: https://mlsanity.com/paper/2102.00202/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2021.

## Built on by (most cited first, 10 of 10)

- [DeepJSCC-Q: Constellation Constrained Deep Joint Source-Channel Coding](https://mlsanity.com/paper/2206.08100/) (2022)
- [DeepJSCC-Q: Channel Input Constrained Deep Joint Source-Channel Coding](https://mlsanity.com/paper/2111.13042/) (2021)
- [Semantic Successive Refinement: A Generative AI-aided Semantic Communication Framework](https://mlsanity.com/paper/2408.05112/) (2024)
- [Deep Joint Source-Channel Coding with Iterative Source Error Correction](https://mlsanity.com/paper/2302.09174/) (2023)
- [Interplay of Semantic Communication and Knowledge Learning](https://mlsanity.com/paper/2402.03339/) (2024)
- [Semantic Communication in Dynamic Channel Scenarios: Collaborative Optimization of Dual-Pipeline Joint Source-Channel Coding and Personalized Federated Learning](https://mlsanity.com/paper/2503.14084/) (2025)
- [Transferable Deployment of Semantic Edge Inference Systems via Unsupervised Domain Adaption](https://mlsanity.com/paper/2504.11873/) (2025)
- [Deep Joint Source-Channel Coding for Wireless Video Transmission with Asymmetric Context](https://mlsanity.com/paper/2601.06170/) (2026)
- [Generative Semantic Communication via Alternating Dual-Domain Posterior Sampling](https://mlsanity.com/paper/2604.16796/) (2026)
- [Restoration Flow Matching-Based Channel Refinement and Equalization Correction for MIMO Semantic Communications](https://mlsanity.com/paper/2607.23615/) (2026)

## Abstract (arXiv)

Considering the problem of joint source-channel coding (JSCC) for multi-user transmission of images over noisy channels, an autoencoder-based novel deep joint source-channel coding scheme is proposed in this paper. In the proposed JSCC scheme, the decoder can estimate the signal-to-noise ratio (SNR) and use it to adaptively decode the transmitted image. Experiments demonstrate that the proposed scheme achieves impressive results in adaptability for different SNRs and is robust to the decoder's estimation error of the SNR. To the best of our knowledge, this is the first deep JSCC scheme that focuses on the adaptability for different SNRs and can be applied to multi-user scenarios.