Site-specific Deep Learning Path Loss Models based on the Method of Moments

• URL: http://arxiv.org/abs/2302.01052v1
• Date: Thu, 2 Feb 2023 12:29:38 GMT
• Title: Site-specific Deep Learning Path Loss Models based on the Method of Moments
• Authors: Conor Brennan and Kevin McGuinness
• Abstract summary: This paper describes deep learning models applied to the problem of predicting EM wave propagation over rural terrain. A surface integral equation formulation is used to generate synthetic training data which comprises path loss computed over randomly generated 1D terrain profiles. The models show excellent agreement when applied to test profiles generated using the same statistical process used to create the training data and very good accuracy when applied to real life problems.
• Score: 7.894490919875104
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This paper describes deep learning models based on convolutional neural networks applied to the problem of predicting EM wave propagation over rural terrain. A surface integral equation formulation, solved with the method of moments and accelerated using the Fast Far Field approximation, is used to generate synthetic training data which comprises path loss computed over randomly generated 1D terrain profiles. These are used to train two networks, one based on fractal profiles and one based on profiles generated using a Gaussian process. The models show excellent agreement when applied to test profiles generated using the same statistical process used to create the training data and very good accuracy when applied to real life problems.

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