Site-specific deep learning path loss models based on the method of moments
Brennan, ConorORCID: 0000-0002-0405-3869 and McGuinness, KevinORCID: 0000-0003-1336-6477
(2023)
Site-specific deep learning path loss models based on the method of moments.
In: 17th European Conference on Antennas and Propagation (EuCAP23), 26 - 31 Mar 2023, Florence, Italy.
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.
Metadata
Item Type:
Conference or Workshop Item (Paper)
Event Type:
Conference
Refereed:
Yes
Uncontrolled Keywords:
Propagation, rural; method of moments; surface integral equation; FAFFA; machine learning; convolutional neural network