# Deep Joint Source-Channel Coding for Wireless Image Transmission

arXiv: https://arxiv.org/abs/1809.01733 · HTML: https://mlsanity.com/paper/1809.01733/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2018.

## Built on by (most cited first, 30 of 38)

- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [Nonlinear Transform Source-Channel Coding for Semantic Communications](https://mlsanity.com/paper/2112.10961/) (2021)
- [CDDM: Channel Denoising Diffusion Models for Wireless Semantic Communications](https://mlsanity.com/paper/2309.08895/) (2023)
- [Towards Semantic Communications: Deep Learning-Based Image Semantic Coding](https://mlsanity.com/paper/2208.04094/) (2022)
- [Transformer-Empowered 6G Intelligent Networks: From Massive MIMO Processing to Semantic Communication](https://mlsanity.com/paper/2205.03770/) (2022)
- [Joint Task and Data Oriented Semantic Communications: A Deep Separate Source-channel Coding Scheme](https://mlsanity.com/paper/2302.13580/) (2023)
- [CDDM: Channel Denoising Diffusion Models for Wireless Communications](https://mlsanity.com/paper/2305.09161/) (2023)
- [Machine Learning for Large-Scale Optimization in 6G Wireless Networks](https://mlsanity.com/paper/2301.03377/) (2023)
- [Neuromorphic Wireless Cognition: Event-Driven Semantic Communications for Remote Inference](https://mlsanity.com/paper/2206.06047/) (2022)
- [Vector Quantized Semantic Communication System](https://mlsanity.com/paper/2209.11519/) (2022)
- [Reinforcement Learning-powered Semantic Communication via Semantic Similarity](https://mlsanity.com/paper/2108.12121/) (2021)
- [Robust Semantic Communications Against Semantic Noise](https://mlsanity.com/paper/2202.03338/) (2022)
- [Wireless End-to-End Image Transmission System using Semantic Communications](https://mlsanity.com/paper/2302.13721/) (2023)
- [Task-Oriented Communication for Edge Video Analytics](https://mlsanity.com/paper/2211.14049/) (2022)
- [Perceptual Learned Source-Channel Coding for High-Fidelity Image Semantic Transmission](https://mlsanity.com/paper/2205.13120/) (2022)
- [Latent Semantic Diffusion-based Channel Adaptive De-Noising SemCom for Future 6G Systems](https://mlsanity.com/paper/2304.09420/) (2023)
- [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)
- [Joint Sensing and Semantic Communications with Multi-Task Deep Learning](https://mlsanity.com/paper/2311.05017/) (2023)
- [Wireless Transmission of Images With The Assistance of Multi-level Semantic Information](https://mlsanity.com/paper/2202.04754/) (2022)
- [Learning Based Joint Coding-Modulation for Digital Semantic Communication Systems](https://mlsanity.com/paper/2208.05704/) (2022)
- [Semantic Communications with Discrete-time Analog Transmission: A PAPR Perspective](https://mlsanity.com/paper/2208.08342/) (2022)
- [DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey](https://mlsanity.com/paper/2503.09956/) (2025)
- [Adaptive Information Bottleneck Guided Joint Source and Channel Coding for Image Transmission](https://mlsanity.com/paper/2203.06492/) (2022)
- [A Specific Task-oriented Semantic Image Communication System for substation patrol inspection](https://mlsanity.com/paper/2301.03331/) (2023)
- [Semantic Edge Computing and Semantic Communications in 6G Networks: A Unifying Survey and Research Challenges](https://mlsanity.com/paper/2411.18199/) (2024)
- [Energy-Efficient Downlink Semantic Generative Communication with Text-to-Image Generators](https://mlsanity.com/paper/2306.05041/) (2023)
- [Trustworthy Image Semantic Communication with GenAI: Explainablity, Controllability, and Efficiency](https://mlsanity.com/paper/2408.03806/) (2024)
- [A Survey on Semantic Communications in Internet of Vehicles](https://mlsanity.com/paper/2503.03767/) (2025)
- [Land-then-transport: A Flow Matching-Based Generative Decoder for Wireless Image Transmission](https://mlsanity.com/paper/2601.07512/) (2026)

## Abstract (arXiv)

We propose a joint source and channel coding (JSCC) technique for wireless image transmission that does not rely on explicit codes for either compression or error correction; instead, it directly maps the image pixel values to the complex-valued channel input symbols. We parameterize the encoder and decoder functions by two convolutional neural networks (CNNs), which are trained jointly, and can be considered as an autoencoder with a non-trainable layer in the middle that represents the noisy communication channel. Our results show that the proposed deep JSCC scheme outperforms digital transmission concatenating JPEG or JPEG2000 compression with a capacity achieving channel code at low signal-to-noise ratio (SNR) and channel bandwidth values in the presence of additive white Gaussian noise (AWGN). More strikingly, deep JSCC does not suffer from the ``cliff effect'', and it provides a graceful performance degradation as the channel SNR varies with respect to the SNR value assumed during training. In the case of a slow Rayleigh fading channel, deep JSCC learns noise resilient coded representations and significantly outperforms separation-based digital communication at all SNR and channel bandwidth values.