# Task-oriented Explainable Semantic Communications

arXiv: https://arxiv.org/abs/2302.13560 · HTML: https://mlsanity.com/paper/2302.13560/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2023.

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

- [Semantic Communication based on Large Language Model for Underwater Image Transmission](https://mlsanity.com/paper/2408.12616/) (2024)
- [Revisiting Outage for Edge Inference Systems](https://mlsanity.com/paper/2504.03686/) (2025)
- [Asymmetric Diffusion Based Channel-Adaptive Secure Wireless Semantic Communications](https://mlsanity.com/paper/2310.19439/) (2023)
- [Modeling and Performance Analysis for Semantic Communications Based on Empirical Results](https://mlsanity.com/paper/2504.21055/) (2025)
- [TONIC: Token-Centric Semantic Communication for Task-Oriented Wireless Systems](https://mlsanity.com/paper/2605.21553/) (2026)
- [Autoencoder-Based Domain Learning for Semantic Communication with Conceptual Spaces](https://mlsanity.com/paper/2401.16569/) (2024)
- [SCU: An Efficient Machine Unlearning Scheme for Deep Learning Enabled Semantic Communications](https://mlsanity.com/paper/2502.19785/) (2025)
- [TOAST: Task-Oriented Adaptive Semantic Transmission over Dynamic Wireless Environments](https://mlsanity.com/paper/2506.21900/) (2025)
- [Context-Aware Information Transfer via Digital Semantic Communication in UAV-Based Networks](https://mlsanity.com/paper/2601.01430/) (2026)
- [A Survey on Semantic Communication for Vision: Categories, Frameworks, Enabling Techniques, and Applications](https://mlsanity.com/paper/2601.22202/) (2026)
- [Task-Semantic Graph-Driven Distributed Agent Networking for Underwater Target Tracking](https://mlsanity.com/paper/2605.15528/) (2026)
- [TWIST: Closed-Loop token Synchronization for Application-Aware Wireless Digital Twins](https://mlsanity.com/paper/2605.27205/) (2026)
- [Joint Movement and Compression Ratio Design for Mobile Embodied AI Networks (MEAN)](https://mlsanity.com/paper/2610.02334/) (2026)

## Abstract (arXiv)

Semantic communications utilize the transceiver computing resources to alleviate scarce transmission resources, such as bandwidth and energy. Although the conventional deep learning (DL) based designs may achieve certain transmission efficiency, the uninterpretability issue of extracted features is the major challenge in the development of semantic communications. In this paper, we propose an explainable and robust semantic communication framework by incorporating the well-established bit-level communication system, which not only extracts and disentangles features into independent and semantically interpretable features, but also only selects task-relevant features for transmission, instead of all extracted features. Based on this framework, we derive the optimal input for rate-distortion-perception theory, and derive both lower and upper bounds on the semantic channel capacity. Furthermore, based on the β -variational autoencoder (β -VAE), we propose a practical explainable semantic communication system design, which simultaneously achieves semantic features selection and is robust against semantic channel noise. We further design a real-time wireless mobile semantic communication proof-of-concept prototype. Our simulations and experiments demonstrate that our proposed explainable semantic communications system can significantly improve transmission efficiency, and also verify the effectiveness of our proposed robust semantic transmission scheme.