Emergency Communication: OTFS-Based Semantic Transmission with Diffusion Noise Suppression

2025-04-10

AI Summary

To address the low reliability and inefficiency caused by dynamic channels in post-disaster emergency communications with high-mobility UAVs, this paper proposes a novel integrated architecture combining orthogonal time-frequency space (OTFS) modulation, end-to-end semantic communication, and diffusion-based denoising. We pioneer the co-design of OTFS, semantic encoding/decoding, and a diffusion probabilistic model (DDPM), establishing a channel noise modeling framework and a progressive semantic denoising mechanism grounded in diffusion processes. By jointly optimizing the semantic and physical layers, the approach significantly enhances semantic reconstruction accuracy and link robustness under high-mobility channel conditions. Experimental results demonstrate an SNR gain of ≥3 dB and a 42% reduction in semantic frame error rate compared to state-of-the-art methods, achieving efficient, accurate, and disturbance-resilient semantic information transmission.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search
• Computer Vision: Diffusion Models for Vision

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

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.

Problem

• Enhance UAV emergency communication reliability in dynamic channels
• Improve transmission efficiency via semantic data compression
• Suppress noise in high-mobility scenarios using diffusion models

Innovation

• OTFS modulation for robust dynamic communication
• Semantic communication to reduce data redundancy
• Diffusion-based denoising for accurate information recovery