Quantification and Validation for Degree of Understanding in M2M Semantic Communications

TL;DR

This paper introduces a hierarchical model to quantify and validate the degree of understanding in machine-to-machine semantic communications, enhancing communication effectiveness in AI and IoT applications.

Contribution

It proposes a novel two-stage hierarchical qualification and validation model for measuring and ensuring understanding in natural language-based M2M SemCom.

Findings

The model effectively quantifies the degree of understanding at word and sentence levels.

Validation at each level improves overall communication accuracy.

Experimental results show significant enhancement in the degree of understanding.

Abstract

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.