# Quantification and Validation for Degree of Understanding in M2M Semantic Communications

arXiv: https://arxiv.org/abs/2408.00767 · HTML: https://mlsanity.com/paper/2408.00767/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 18 references that are in the map.
- First posted on arXiv 15 Jul 2024.

## Builds on (most cited first, 9 of 9)

- [Semantic Communication Systems for Speech Transmission](https://mlsanity.com/paper/2102.12605/) (2021)
- [Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges](https://mlsanity.com/paper/2207.00427/) (2022)
- [A Lite Distributed Semantic Communication System for Internet of Things](https://mlsanity.com/paper/2007.11095/) (2020)
- [What is Semantic Communication? A View on Conveying Meaning in the Era of Machine Intelligence](https://mlsanity.com/paper/2110.00196/) (2021)
- [Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels](https://mlsanity.com/paper/1911.03038/) (2019)
- [Cognitive Semantic Communication Systems Driven by Knowledge Graph](https://mlsanity.com/paper/2202.11958/) (2022)
- [Towards Goal-Oriented Semantic Signal Processing: Applications and Future Challenges](https://mlsanity.com/paper/2109.11885/) (2021)
- [Knowledge Distillation Based Semantic Communications For Multiple Users](https://mlsanity.com/paper/2311.13789/) (2023)
- [Enhancing Word-Level Semantic Representation via Dependency Structure for Expressive Text-to-Speech Synthesis](https://mlsanity.com/paper/2104.06835/) (2021)

## Abstract (arXiv)

With the development of Artificial Intelligence (AI) and Internet of Things (IoT) technologies, network communications based on the Shannon-Nyquist theorem gradually reveal their limitations due to the neglect of semantic information in the transmitted content. Semantic communication (SemCom) provides a solution for extracting information meanings from the transmitted content. The semantic information can be successfully interpreted by a receiver with the help of a shared knowledge base (KB). This paper proposes a two-stage hierarchical qualification and validation model for natural language-based machine-to-machine (M2M) SemCom. The approach can be applied in various applications, such as autonomous driving and edge computing. In the proposed model, we quantitatively measure the degree of understanding (DoU) between two communication parties at the word and sentence levels. The DoU is validated and ensured at each level before moving to the next step. The model's effectiveness is verified through a series of experiments, and the results show that the quantification and validation method proposed in this paper can significantly improve the DoU of inter-machine SemCom.