Deep Learning-Based Channel Estimation for Wireless Power Transfer Systems

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High-speed wireless communication systems operate under highly dynamic channel conditions characterized by rapid fading, mobility, and complex propagation environments. Accurate channel modeling is essential for reliable transmission, efficient resource allocation, and improved spectral efficiency in such networks. This paper presents a deep learning–based channel modeling approach for high-speed wireless networks, where neural networks learn complex channel characteristics directly from data. The proposed method captures nonlinear relationships between channel parameters and environmental factors that are difficult to model using traditional analytical techniques. By leveraging large-scale channel measurement datasets, the model adapts to varying mobility and propagation scenarios. Simulation results demonstrate that the deep learning approach achieves higher prediction accuracy and robustness compared to conventional channel models. The findings highlight the potential of deep learning to enhance channel estimation and system performance in next-generation high-speed wireless networks.