# Joint Coding-Modulation for Digital Semantic Communications via Variational Autoencoder

arXiv: https://arxiv.org/abs/2310.06690 · HTML: https://mlsanity.com/paper/2310.06690/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2023.

## Built on by (most cited first, 18 of 18)

- [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)
- [Joint Semantic-Channel Coding and Modulation for Token Communications](https://mlsanity.com/paper/2511.15699/) (2025)
- [Semantic Edge Computing and Semantic Communications in 6G Networks: A Unifying Survey and Research Challenges](https://mlsanity.com/paper/2411.18199/) (2024)
- [MambaJSCC: Adaptive Deep Joint Source-Channel Coding with Generalized State Space Model](https://mlsanity.com/paper/2409.16592/) (2024)
- [SCDM: Score-Based Channel Denoising Model for Digital Semantic Communications](https://mlsanity.com/paper/2501.17876/) (2025)
- [Learned Image Transmission with Hierarchical Variational Autoencoder](https://mlsanity.com/paper/2408.16340/) (2024)
- [From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications](https://mlsanity.com/paper/2505.22311/) (2025)
- [Importance-Aware Semantic Communication in MIMO-OFDM Systems Using Vision Transformer](https://mlsanity.com/paper/2508.07696/) (2025)
- [Generative Feature Imputing -- A Technique for Error-resilient Semantic Communication](https://mlsanity.com/paper/2508.17957/) (2025)
- [sDAC -- Semantic Digital Analog Converter for Semantic Communications](https://mlsanity.com/paper/2405.02335/) (2024)
- [Knowledge Abstraction for Knowledge-based Semantic Communication: A Generative Causality Invariant Approach](https://mlsanity.com/paper/2507.17784/) (2025)
- [Adaptive Source-Channel Coding for Semantic Communications](https://mlsanity.com/paper/2508.07958/) (2025)
- [Way to Build Native AI-driven 6G Air Interface: Principles, Roadmap, and Outlook](https://mlsanity.com/paper/2508.15277/) (2025)
- [Towards 6G Intelligence: The Role of Generative AI in Future Wireless Networks](https://mlsanity.com/paper/2508.19495/) (2025)
- [Synesthesia of Machines (SoM)-Based Task-Driven MIMO System for Image Transmission](https://mlsanity.com/paper/2509.02031/) (2025)
- [Large Speech Model Enabled Semantic Communication](https://mlsanity.com/paper/2512.04711/) (2025)
- [Beyond Token Accuracy: Prioritizing What Matters for Visual Reconstruction](https://mlsanity.com/paper/2610.03822/) (2026)

## Abstract (arXiv)

Semantic communications have emerged as a new paradigm for improving communication efficiency by transmitting the semantic information of a source message that is most relevant to a desired task at the receiver. Most existing approaches typically utilize neural networks (NNs) to design end-to-end semantic communication systems, where NN-based semantic encoders output continuously distributed signals to be sent directly to the channel in an analog fashion. In this work, we propose a joint coding-modulation (JCM) framework for digital semantic communications by using variational autoencoder (VAE). Our approach learns the transition probability from source data to discrete constellation symbols, thereby avoiding the non-differentiability problem of digital modulation. Meanwhile, by jointly designing the coding and modulation process together, we can match the obtained modulation strategy with the operating channel condition. We also derive a matching loss function with information-theoretic meaning for end-to-end training. Experiments on image semantic communication validate the superiority of our proposed JCM framework over the state-of-the-art quantization-based digital semantic coding-modulation methods across a wide range of channel conditions, transmission rates, and modulation orders. Furthermore, its performance gap to analog semantic communication reduces as the modulation order increases while enjoying the hardware implementation convenience.