# Deep Learning for Joint Source-Channel Coding of Text

arXiv: https://arxiv.org/abs/1802.06832 · HTML: https://mlsanity.com/paper/1802.06832/

## Key facts

- Cited by 69 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2018.

## Built on by (most cited first, 30 of 69)

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- [Robust Information Bottleneck for Task-Oriented Communication with Digital Modulation](https://mlsanity.com/paper/2209.10382/) (2022)
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- [Wireless End-to-End Image Transmission System using Semantic Communications](https://mlsanity.com/paper/2302.13721/) (2023)
- [Latent Diffusion Model-Enabled Low-Latency Semantic Communication in the Presence of Semantic Ambiguities and Wireless Channel Noises](https://mlsanity.com/paper/2406.06644/) (2024)
- [Cognitive Semantic Communication Systems Driven by Knowledge Graph](https://mlsanity.com/paper/2202.11958/) (2022)
- [Semantic Information Recovery in Wireless Networks](https://mlsanity.com/paper/2204.13366/) (2022)
- [Pragmatic Communication in Multi-Agent Collaborative Perception](https://mlsanity.com/paper/2401.12694/) (2024)
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- [Toward Adaptive Semantic Communications: Efficient Data Transmission via Online Learned Nonlinear Transform Source-Channel Coding](https://mlsanity.com/paper/2211.04339/) (2022)
- [Semantics-Empowered Communication: A Tutorial-cum-Survey](https://mlsanity.com/paper/2212.08487/) (2022)
- [Cognitive Semantic Communication Systems Driven by Knowledge Graph: Principle, Implementation, and Performance Evaluation](https://mlsanity.com/paper/2303.08546/) (2023)
- [One-to-Many Semantic Communication Systems: Design, Implementation, Performance Evaluation](https://mlsanity.com/paper/2209.09425/) (2022)
- [Synchronous Multi-modal Semantic Communication System with Packet-level Coding](https://mlsanity.com/paper/2408.04535/) (2024)
- [Wireless Transmission of Images With The Assistance of Multi-level Semantic Information](https://mlsanity.com/paper/2202.04754/) (2022)
- [Rate-Adaptive Coding Mechanism for Semantic Communications With Multi-Modal Data](https://mlsanity.com/paper/2305.10773/) (2023)
- [Deep Learning in Physical Layer: Review on Data Driven End-to-End Communication Systems and their Enabling Semantic Applications](https://mlsanity.com/paper/2401.12800/) (2024)
- [Semantic Communication based on Large Language Model for Underwater Image Transmission](https://mlsanity.com/paper/2408.12616/) (2024)
- [SVD-Embedded Deep Autoencoder for MIMO Communications](https://mlsanity.com/paper/2111.02359/) (2021)

## Abstract (arXiv)

We consider the problem of joint source and channel coding of structured data such as natural language over a noisy channel. The typical approach to this problem in both theory and practice involves performing source coding to first compress the text and then channel coding to add robustness for the transmission across the channel. This approach is optimal in terms of minimizing end-to-end distortion with arbitrarily large block lengths of both the source and channel codes when transmission is over discrete memoryless channels. However, the optimality of this approach is no longer ensured for documents of finite length and limitations on the length of the encoding. We will show in this scenario that we can achieve lower word error rates by developing a deep learning based encoder and decoder. While the approach of separate source and channel coding would minimize bit error rates, our approach preserves semantic information of sentences by first embedding sentences in a semantic space where sentences closer in meaning are located closer together, and then performing joint source and channel coding on these embeddings.