Towards Physics-Based Generalizable Convolutional Neural Network Models
for Indoor Propagation
Abstract
A fundamental challenge for machine learning models for electromagnetics
is their ability to predict output quantities of interest (such as
fields and scattering parameters) in geometries that the model has not
been trained for. Addressing this challenge is a key to fulfilling one
of the most appealing promises of machine learning for computational
electromagnetics: the rapid solution of problems of interest just by
processing the geometry and the sources involved. The impact of such
models that can “generalize” to new geometries is more profound for
large-scale computations, such as those encountered in wireless
propagation scenarios. We present generalizable models for indoor
propagation that can predict received signal strengths within new
geometries, beyond those of the training set of the model, for
transmitters and receivers of multiple positions, and for new
frequencies. We show that a convolutional neural network can “learn”
the physics of indoor radiowave propagation from ray-tracing solutions
of a small set of training geometries, so that it can eventually deal
with substantially different geometries. We emphasize the role of
exploiting physical insights in the training of the network, by defining
input parameters and cost functions that assist the network to
efficiently learn basic and complex propagation mechanisms.