Emergency Communication: OTFS-Based Semantic Transmission with Diffusion Noise Suppression
Kexin Zhang, Xin Zhang, Lixin Li, Wensheng Lin, Wenchi Cheng, Qinghe Du
TL;DR
This work tackles emergency UAV communications in disaster zones where high mobility and degraded infrastructure hinder reliable data transfer. It proposes an integrated framework that combines OTFS modulation, semantic transmission via a Swin Transformer-based encoder/decoder, and a diffusion-based channel denoising module, trained in three stages. The approach reduces data redundancy while maintaining resilience to Doppler and multipath effects, and delivers a demonstrated $3\ \mathrm{dB}$ SNR gain over baseline methods. The combination of delay-Doppler domain processing and learned noise modeling significantly improves the transmission of task-relevant information, enabling faster, more reliable emergency responses in highly dynamic environments.
Abstract
Due to their flexibility and dynamic coverage capabilities, Unmanned Aerial Vehicles (UAVs) have emerged as vital platforms for emergency communication in disaster-stricken areas. However, the complex channel conditions in high-speed mobile scenarios significantly impact the reliability and efficiency of traditional communication systems. This paper presents an intelligent emergency communication framework that integrates Orthogonal Time Frequency Space (OTFS) modulation, semantic communication, and a diffusion-based denoising module to address these challenges. OTFS ensures robust communication under dynamic channel conditions due to its superior anti-fading characteristics and adaptability to rapidly changing environments. Semantic communication further enhances transmission efficiency by focusing on key information extraction and reducing data redundancy. Moreover, a diffusion-based channel denoising module is proposed to leverage the gradual noise reduction process and statistical noise modeling, optimizing the accuracy of semantic information recovery. Experimental results demonstrate that the proposed solution significantly improves link stability and transmission performance in high-mobility UAV scenarios, achieving at least a 3dB SNR gain over existing methods.