Quantification and Validation for Degree of Understanding in M2M Semantic Communications
It addresses the challenge of reliable semantic communication for applications like autonomous driving and edge computing, but appears incremental as it builds on existing semantic communication frameworks.
This paper tackles the problem of measuring and ensuring understanding in machine-to-machine semantic communications by proposing a two-stage hierarchical model that quantifies the degree of understanding at word and sentence levels, with experiments showing it significantly improves understanding.
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.