# DeepJSCC-Q: Channel Input Constrained Deep Joint Source-Channel Coding

arXiv: https://arxiv.org/abs/2111.13042 · HTML: https://mlsanity.com/paper/2111.13042/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 24 references that are in the map.
- First posted on arXiv 25 Nov 2021.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules](https://mlsanity.com/paper/2001.01568/) (2020)
- [Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations](https://mlsanity.com/paper/1704.00648/) (2017)
- [Density Modeling of Images using a Generalized Normalization Transformation](https://mlsanity.com/paper/1511.06281/) (2015)
- [DeepJSCC-f: Deep Joint Source-Channel Coding of Images with Feedback](https://mlsanity.com/paper/1911.11174/) (2019)
- [Bandwidth-Agile Image Transmission with Deep Joint Source-Channel Coding](https://mlsanity.com/paper/2009.12480/) (2020)
- [Deep Joint Source Channel Coding for WirelessImage Transmission with OFDM](https://mlsanity.com/paper/2101.03909/) (2021)
- [Joint Learning of Geometric and Probabilistic Constellation Shaping](https://mlsanity.com/paper/1906.07748/) (2019)
- [SNR-adaptive deep joint source-channel coding for wireless image transmission](https://mlsanity.com/paper/2102.00202/) (2021)
- [Neural Joint Source-Channel Coding](https://mlsanity.com/paper/1811.07557/) (2018)

## Built on by (most cited first, 9 of 9)

- [High Perceptual Quality Wireless Image Delivery with Denoising Diffusion Models](https://mlsanity.com/paper/2309.15889/) (2023)
- [Distributed Deep Joint Source-Channel Coding over a Multiple Access Channel](https://mlsanity.com/paper/2211.09920/) (2022)
- [Learning Based Joint Coding-Modulation for Digital Semantic Communication Systems](https://mlsanity.com/paper/2208.05704/) (2022)
- [Non-Orthogonal Multiple Access Enhanced Multi-User Semantic Communication](https://mlsanity.com/paper/2303.06597/) (2023)
- [Rate-Adaptive Coding Mechanism for Semantic Communications With Multi-Modal Data](https://mlsanity.com/paper/2305.10773/) (2023)
- [Aligning Task- and Reconstruction-Oriented Communications for Edge Intelligence](https://mlsanity.com/paper/2502.15472/) (2025)
- [sDAC -- Semantic Digital Analog Converter for Semantic Communications](https://mlsanity.com/paper/2405.02335/) (2024)
- [Variational Source-Channel Coding for Semantic Communication](https://mlsanity.com/paper/2410.08222/) (2024)
- [Large Speech Model Enabled Semantic Communication](https://mlsanity.com/paper/2512.04711/) (2025)

## Abstract (arXiv)

Recent works have shown that the task of wireless transmission of images can be learned with the use of machine learning techniques. Very promising results in end-to-end image quality, superior to popular digital schemes that utilize source and channel coding separation, have been demonstrated through the training of an autoencoder, with a non-trainable channel layer in the middle. However, these methods assume that any complex value can be transmitted over the channel, which can prevent the application of the algorithm in scenarios where the hardware or protocol can only admit certain sets of channel inputs, such as the use of a digital constellation. Herein, we propose DeepJSCC-Q, an end-to-end optimized joint source-channel coding scheme for wireless image transmission, which is able to operate with a fixed channel input alphabet. We show that DeepJSCC-Q can achieve similar performance to models that use continuous-valued channel input. Importantly, it preserves the graceful degradation of image quality observed in prior work when channel conditions worsen, making DeepJSCC-Q much more attractive for deployment in practical systems.