Abstract
Path loss modeling of millimeter waves is regarded as one of the most challenging problems in the design of the fifth-generation (5G) mobile communication networks due to the high susceptibility to various environmental influences. We present a novel convolutional neural network (CNN)-based path loss modeling method based on a set of 28 GHz mmWave field measurements in suburban scenarios. Enhanced local area multi-scanning (E-LAMS) algorithm that provides a CNN with path loss environmental information is proposed. A new CNN structure with four subnetworks and feature-sharing layers added between convolutional layers is also proposed. The proposed methods demonstrate their superior performance over empirical models and deterministic models in terms of accuracy and complexity. The root-mean-square error of 8.59 dB has been achieved in path loss prediction in the test scenarios.
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