# Learning Based Joint Coding-Modulation for Digital Semantic Communication Systems

arXiv: https://arxiv.org/abs/2208.05704 · HTML: https://mlsanity.com/paper/2208.05704/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 21 references that are in the map.
- First posted on arXiv 11 Aug 2022.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Deep Variational Information Bottleneck](https://mlsanity.com/paper/1612.00410/) (2016)
- [Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach](https://mlsanity.com/paper/2102.04170/) (2021)
- [Semantic Communication Systems for Speech Transmission](https://mlsanity.com/paper/2102.12605/) (2021)
- [Semantic Communications: Principles and Challenges](https://mlsanity.com/paper/2201.01389/) (2021)
- [Task-Oriented Multi-User Semantic Communications for VQA Task](https://mlsanity.com/paper/2108.07357/) (2021)
- [SpinalNet: Deep Neural Network with Gradual Input](https://mlsanity.com/paper/2007.03347/) (2020)
- [Deep Joint Source-Channel Coding for Wireless Image Transmission](https://mlsanity.com/paper/1809.01733/) (2018)
- [Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Data](https://mlsanity.com/paper/2205.00271/) (2022)
- [Deep Source-Channel Coding for Sentence Semantic Transmission with HARQ](https://mlsanity.com/paper/2106.03009/) (2021)
- [Channel-Adaptive Wireless Image Transmission with OFDM](https://mlsanity.com/paper/2205.02417/) (2022)
- [Semantic Communications for Speech Signals](https://mlsanity.com/paper/2012.05369/) (2020)
- [DeepJSCC-Q: Channel Input Constrained Deep Joint Source-Channel Coding](https://mlsanity.com/paper/2111.13042/) (2021)
- [Neural Joint Source-Channel Coding](https://mlsanity.com/paper/1811.07557/) (2018)

## Built on by (most cited first, 6 of 6)

- [CDDM: Channel Denoising Diffusion Models for Wireless Semantic Communications](https://mlsanity.com/paper/2309.08895/) (2023)
- [OFDM-Based Digital Semantic Communication with Importance Awareness](https://mlsanity.com/paper/2401.02178/) (2024)
- [Non-Orthogonal Multiple Access Enhanced Multi-User Semantic Communication](https://mlsanity.com/paper/2303.06597/) (2023)
- [Task-Oriented Communication for Graph Data: A Graph Information Bottleneck Approach](https://mlsanity.com/paper/2409.02728/) (2024)
- [Deep Joint Source-Channel Coding for Efficient and Reliable Cross-Technology Communication](https://mlsanity.com/paper/2402.10072/) (2024)
- [TOAST: Task-Oriented Adaptive Semantic Transmission over Dynamic Wireless Environments](https://mlsanity.com/paper/2506.21900/) (2025)

## Abstract (arXiv)

In learning-based semantic communications, neural networks have replaced different building blocks in traditional communication systems. However, the digital modulation still remains a challenge for neural networks. The intrinsic mechanism of neural network based digital modulation is mapping continuous output of the neural network encoder into discrete constellation symbols, which is a non-differentiable function that cannot be trained with existing gradient descend algorithms. To overcome this challenge, in this paper we develop a joint coding-modulation scheme for digital semantic communications with BPSK modulation. In our method, the neural network outputs the likelihood of each constellation point, instead of having a concrete mapping. A random code rather than a deterministic code is hence used, which preserves more information for the symbols with a close likelihood on each constellation point. The joint coding-modulation design can match the modulation process with channel states, and hence improve the performance of digital semantic communications. Experiment results show that our method outperforms existing digital modulation methods in semantic communications over a wide range of SNR, and outperforms neural network based analog modulation method in low SNR regime.