# A Robust Deep Learning Enabled Semantic Communication System for Text

arXiv: https://arxiv.org/abs/2206.02596 · HTML: https://mlsanity.com/paper/2206.02596/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2022.

## Built on by (most cited first, 13 of 13)

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- [VideoQA-SC: Adaptive Semantic Communication for Video Question Answering](https://mlsanity.com/paper/2406.18538/) (2024)
- [Asymmetric Diffusion Based Channel-Adaptive Secure Wireless Semantic Communications](https://mlsanity.com/paper/2310.19439/) (2023)
- [Generative Semantic Communication for Text-to-Speech Synthesis](https://mlsanity.com/paper/2410.03459/) (2024)
- [A Semantic Communication System for Real-time 3D Reconstruction Tasks](https://mlsanity.com/paper/2412.01191/) (2024)
- [Contrastive Language-Image Pre-Training Model based Semantic Communication Performance Optimization](https://mlsanity.com/paper/2507.08873/) (2025)
- [Modeling and Performance Analysis for Semantic Communications Based on Empirical Results](https://mlsanity.com/paper/2504.21055/) (2025)
- [Semantic Information Extraction for Text Data with Probability Graph](https://mlsanity.com/paper/2309.08879/) (2023)
- [Communication-Efficient Multi-Modal Edge Inference via Uncertainty-Aware Distributed Learning](https://mlsanity.com/paper/2601.14942/) (2026)

## Abstract (arXiv)

With the advent of the 6G era, the concept of semantic communication has attracted increasing attention. Compared with conventional communication systems, semantic communication systems are not only affected by physical noise existing in the wireless communication environment, e.g., additional white Gaussian noise, but also by semantic noise due to the source and the nature of deep learning-based systems. In this paper, we elaborate on the mechanism of semantic noise. In particular, we categorize semantic noise into two categories: literal semantic noise and adversarial semantic noise. The former is caused by written errors or expression ambiguity, while the latter is caused by perturbations or attacks added to the embedding layer via the semantic channel. To prevent semantic noise from influencing semantic communication systems, we present a robust deep learning enabled semantic communication system (R-DeepSC) that leverages a calibrated self-attention mechanism and adversarial training to tackle semantic noise. Compared with baseline models that only consider physical noise for text transmission, the proposed R-DeepSC achieves remarkable performance in dealing with semantic noise under different signal-to-noise ratios.