Diffusion-Based Semantic-Communication-Assisted Low-Altitude Intelligent Service for IoT

Autonomous aerial vehicles (AAVs), as the key Internet of Things (IoT) devices, play a dominant position in low-altitude environments. Semantic communication (SC), as the next-generation communication technology, serves as a bridge for surpassing the Shannon limit toward the 6G wireless network. Establishing air-ground SC to provide intelligent IoT services is a crucial initiative for building future smart cities. In this article, we propose an AAV-based SC framework, named diffusion joint source-channel coding (D-JSCC). Abandoning traditional convolutional neural networks, we use transformers as the backbone and innovatively incorporate the diffusion model (DM) for image enhancement, achieving an optimal balance between image distortion and human perception. To accurately capture the numerical and perceptual loss induced by wireless channels and seamlessly amalgamate the DM with SC, we integrate channel states as strong prior information to refine the sampling process. Furthermore, we employ the gradient guidance strategy, which counteracts the randomness of sampling ensuring high robustness in harsh communication conditions. Additionally, we strike a balance between performance and sampling steps, ensuring both efficient computation and high-quality image enhancement. Comprehensive experiments demonstrate the advantages of D-JSCC across different communication environments.