CNN-Based Path Loss Prediction With Enhanced Satellite Images

Precise path loss models play a crucial role in design and optimization of wireless communication systems, requiring a careful balance between accuracy and efficiency. Deep learning presents a promising approach for improving both aspects. This letter proposes a novel approach that uses satellite images to construct a comprehensive dataset with rich environmental information. By incorporating environmental features into a convolutional-neural-network-based model, the accuracy of path loss prediction is significantly enhanced. Comparisons and validation demonstrate the approach in improving the accuracy of path loss prediction, particularly with the assistance of road information.