Intelligent Fabric Enabled 6G Semantic Communication System for In-Cabin Scenarios
Yuan Tang, Ning Zhou, Yu Qiao, Di Wu, Chong Hou, Guangming Tao, Min Chen
Abstract
With the large-scale commercialization of 5G, the global industry has started the exploration of the next generation mobile communication technology (6G). From mobile Internet, to IoT, and then to the smart connection of everything, 6G will transform from 5G’s service objects of people and things to the intelligent networking of agent that supports human–machine–object. 6G networks should have the characteristics of ubiquitous intelligence and ubiquitous perception, which poses challenges for 6G network construction. Therefore, we propose a 6G Semantic Communication Scheme based on Intelligent Fabrics for transportation in-cabin scenarios (6GSCS-IF), which can provide senseless intelligent interaction in transportation in-cabin environment through widely and flexibly deployed intelligent fabrics, demonstrating the superiority of intelligent fabrics in realizing human–machine–object intelligent sensory interaction. Then, we propose a Deep Learning-based Semantic Communication Model for Time-series data (DL-SCMT), and use deep learning for semantic sensing and information extraction to build an end-to-end semantic communication system. The experimental results show that the semantic communication services provided by this model can achieve better signal reconstruction and higher-order intelligent services compared with traditional communication methods.