Abstract
The study of wireless channel propagation characteristics is important to channel availability analysis and wireless network optimization. The existing deterministic channel models are greatly restricted by the high complexity. This paper proposes a deep learning (DL) method for path loss (PL) prediction in mobile communication systems, which uses satellite images and neural network (NN) to achieve efficient and reliable prediction. A Deep Neural Network (DNN) framework is established based on a Convolutional Neural Network (CNN) and a Back Propagation Neural Network (BPNN), where CNN extracts environmental features of propagation paths from satellite images and BPNN predicts PL according to the distance features and environmental features. The results show that DNN proposed in this paper can reduce the time cost with satisfying accuracy compared to the traditional Ray-tracing method with the lack of a detailed model of the scene and material parameters. The DL method for making a quick prediction of the PL in this paper can be used to optimize the mobile communication systems.
Published Version
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