Artificial Intelligence (AI) and Internet of Things (IoT) technologies have revolutionized network communications, but they have also exposed the limitations of traditional Shannon-Nyquist theorem-based approaches. These traditional approaches neglect the semantic information within the transmitted content, making it difficult for receivers to extract the true meaning of the information.
To address this issue, the concept of Semantic Communication (SemCom) has emerged. SemCom focuses on extracting the underlying meaning from the transmitted content, allowing for more accurate and meaningful communication. The key to SemCom is the use of a shared knowledge base (KB) that helps receivers interpret the semantic information correctly.
This paper proposes a two-stage hierarchical qualification and validation model for natural language-based machine-to-machine (M2M) SemCom. This model can be applied in various applications, including autonomous driving and edge computing, where accurate and reliable communication is crucial.
In this model, the degree of understanding (DoU) between two communication parties is measured quantitatively at both the word and sentence levels. This quantification allows for a more precise assessment of the level of understanding between the parties. Furthermore, the DoU is validated and ensured at each level before moving on to the next step, ensuring a high level of accuracy in the communication process.
The effectiveness of this model has been tested and verified through a series of experiments. The results demonstrate that the proposed quantification and validation method significantly improves the DoU of inter-machine SemCom. This improvement is a crucial step towards achieving more accurate and meaningful communication in M2M scenarios.
This research has significant implications for the development of AI and IoT technologies. By addressing the limitations of traditional communication methods and focusing on semantic information, SemCom opens up new possibilities for more intelligent and context-aware communication between machines. This advancement can enhance applications such as autonomous driving, where machines need to understand and respond to complex situations in real-time.
In conclusion, the two-stage hierarchical qualification and validation model proposed in this paper represents an important step forward in improving machine-to-machine SemCom. By addressing the limitations of traditional communication approaches and emphasizing semantic information, this model brings us closer to achieving more accurate and meaningful communication in AI and IoT-enabled scenarios.