标题： 基于数字-模拟传输的紧急语义通信 显示英文标题

标题： Digital-Analog Transmission based Emergency Semantic Communications

摘要： 应急无线通信（EWC）网络采用用户数据报协议（UDP）实时传输场景图像，以快速评估损坏程度。然而，在较差的信道条件下，现有的基于UDP的EWC表现出次优性能，因为UDP缺乏自动重传请求（ARQ）机制。此外，未来的EWC系统不仅要增强应急响应操作中的人员决策，还要支持人工智能（AI）驱动的方法以提高救援效率。基于深度学习的语义通信（DL-based SemCom）作为一种强大、高效且任务导向的传输方案，适用于部署在基于UDP的EWC中。由于硬件能力和传输资源的限制，EWC发射机无法集成足够强大的神经网络模型，从而在EWC场景下无法达到理想的性能。针对EWC场景，我们提出了一种性能受限的语义编码模型，该模型考虑了语义噪声和信道噪声的影响。然后，我们推导了所提出的语义编码模型的Cramer-Rao下界，作为设计语义编解码器以增强其对语义噪声和信道噪声适应性的指导。为了进一步提高系统性能，我们提出了基于数字-模拟传输的应急语义通信（DAESemCom）框架，该框架结合了模拟的基于深度学习的语义编码和数字的分布式源编码（DSC）方案，以利用各自的优势。仿真结果表明，所提出的DA-ESemCom框架在保真度和检测性能方面优于经典的分离信源信道编码（SSCC）和其他基于深度学习的联合信源信道编码（DL-based JSCC）方案。 显示英文摘要

摘要： Emergency Wireless Communication (EWC) networks adopt the User Datagram Protocol (UDP) to transmit scene images in real time for quickly assessing the extent of the damage. However, existing UDP-based EWC exhibits suboptimal performance under poor channel conditions since UDP lacks an Automatic Repeat reQuest (ARQ) mechanism. In addition, future EWC systems must not only enhance human decisionmaking during emergency response operations but also support Artificial Intelligence (AI)-driven approaches to improve rescue efficiency. The Deep Learning-based Semantic Communication (DL-based SemCom) emerges as a robust, efficient, and taskoriented transmission scheme, suitable for deployment in UDP based EWC. Due to the constraints in hardware capabilities and transmission resources, the EWC transmitter is unable to integrate sufficiently powerful NN model, thereby failing to achieve ideal performance under EWC scene. For EWC scene, we propose a performance-constrained semantic coding model, which considers the effects of the semantic noise and the channel noise. Then, we derive Cramer-Rao lower bound of the proposed semantic coding model, as guidance for the design of semantic codec to enhance its adaptability to semantic noise as well as channel noise. To further improve the system performance, we propose Digital-Analog transmission based Emergency Semantic Communication (DAESemCom) framework, which integrates the analog DL-based semantic coding and the digital Distributed Source Coding (DSC) schemes to leverage their respective advantages. The simulation results show that the proposed DA-ESemCom framework outperforms the classical Separated Source-Channel Coding (SSCC) and other DL-based Joint Source-Channel Coding (DL-based JSCC) schemes in terms of fidelity and detection performances.