TY - GEN
T1 - Emergency Communication
T2 - 101st IEEE Vehicular Technology Conference, VTC 2025-Spring 2025
AU - Zhang, Kexin
AU - Zhang, Xin
AU - Li, Lixin
AU - Lin, Wensheng
AU - Cheng, Wenchi
AU - Du, Qinghe
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - diffusion model
KW - OTFS
KW - Semantic communication
UR - https://www.scopus.com/pages/publications/105019045736
U2 - 10.1109/VTC2025-Spring65109.2025.11174824
DO - 10.1109/VTC2025-Spring65109.2025.11174824
M3 - 会议稿件
AN - SCOPUS:105019045736
T3 - IEEE Vehicular Technology Conference
BT - 2025 IEEE 101st Vehicular Technology Conference, VTC 2025-Spring 2025 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 17 June 2025 through 20 June 2025
ER -