Channel Path Loss Prediction Using Satellite Images: A Deep Learning Approach
Chenlong Wang, Bo Ai, Ruisi He, Mi Yang, Shun Zhou, Long Yu, Yuxin Zhang, Zhicheng Qiu, Zhangdui Zhong, Jianhua Fan
Abstract
With the advancement of communication technology, there is a higher demand for high-precision and high-generalization channel path loss models as it is fundamental to communication systems. For traditional stochastic and deterministic models, it is difficult to strike a balance between prediction accuracy and generalizability. This paper proposes a novel deep learning-based path loss prediction model using satellite images. In order to efficiently extract environment features from satellite images, residual structure, attention mechanism, and spatial pyramid pooling layer are developed in the network based on expert knowledge. Using a convolutional network activation visualization method, the interpretability of the proposed model is improved. Finally, the proposed model achieves a prediction accuracy with a root mean square error of 5.05 dB, demonstrating an improvement of 3.07 dB over a reference empirical propagation model.