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A Study on Path Loss Modeling using ResNet and Pre-Training with Free Space Path Loss

Wireless emulation technologies have recently been the subject of research and development to evaluate and verify wireless communication systems, including Beyond 5G, in a virtual space. To accurately emulate wireless system behavior, it is essential to model the radio propagation characteristics according to the environment. In recent years, several methods for site-specific path loss modeling have been proposed using machine learning, particularly convolutional neural networks (CNNs). Most of these methods simply stack convolution and pooling layers, but may not learn sufficiently due to vanishing gradients. Additionally, the most fundamental parameter characterizing propagation loss is the distance between the transmitter and receiver. However, some learning methods may fall into the local optimum solution by giving importance to other parameters. This paper proposes a method that uses Residual Network (ResNet) as the architecture and pre-training with Free Space Path Loss (FSPL) as the target variable. The proposed method improves learning efficiency and is expected to improve prediction accuracy. Finally, the effectiveness of the proposed method will be demonstrated by evaluating measurement data acquired in an urban area.