Semantic-Driven Efficient Service Network Towards Smart Healthcare System in Intelligent Fabric

Abstract

The advantages of intelligent fabrics enable health service systems more efficient and safe to perceive and transmit, where massive sensitive data related to health and behavior are collected through the multi-source flexible sensor network. However, due to its complex network structure, health service systems face the challenge of low-power data transmission and processing in intelligent fabric space. Recently, Deep learning-powered semantic communication (Deep-SC) has emerged as a promising communication paradigm. To tackle the above problem, we introduce the concept of semantic cognition into the cyber-physical system of health service and present the Deep-SC based network service framework for the healthcare system, where physical layer blocks are merged into the traditional communication system to jointly optimize the transceiver in communication. Since semantic encoding and decoding require additional knowledge, consider the introduction of the semantic knowledge base in the system to formulate the joint optimization problem of service offloading and bandwidth allocation in the proposed network service framework, with the goals of reliable semantic communication and efficient network transmission. Furthermore, an online algorithm that optimally adapts service offloading and subcarrier allocation decisions to the time-varying channel conditions is required. Finally, we use a reinforcement learning-based decision algorithm to solve combinatorial optimization problems, which further reduces the computing complexity. Experimental results show that the proposed framework achieves significant performance improvements in system energy consumption and reliable transmission, compared with traditional communication strategies.