Deep-JSCC-Driven Technology for Enhancing Differentiated Emergency Service Quality Assurance

With the increasing frequency of natural disasters and public safety events, traditional communication systems suffer from low transmission efficiency, highlighting the need for more robust, timely, and reliable solutions. Semantic communication offers a promising solution, yet research on its application in emergency response remains limited, particularly in terms of quality of service (QoS) assurance mechanisms in disaster scenarios. This paper addresses this gap by developing a dynamic resource allocation and QoS optimization method based on the Deep Joint Source Channel Coding (Deep-JSCC) semantic model. The model is trained to improve the efficiency of accurate message delivery in emergency scenarios. To ensure QoS for high-priority users in emergency communication, a priority queuing algorithm is proposed, which optimally the communication quality of emergency tasks and important users in the process of emergency communication, and improve the speed of sending and receiving their datagrams. Through theoretical analyses and simulation experiments, the effectiveness of the proposed approach is verified, providing novel insights and methods for improving information transmission in emergency response.