Indoor Millimeter Wave Localization using Multiple Self-Supervised Tiny
  Neural Networks
Anish Shastri Andres Garcia-Saavedra Paolo Casari
We consider the localization of a mobile millimeter-wave client in a large
indoor environment using multilayer perceptron neural networks (NNs). Instead
of training and deploying a single deep model, we proceed by choosing among
multiple tiny NNs trained in a self-supervised manner. The main challenge then
becomes to determine and switch to the best NN among the available ones, as an
incorrect NN will fail to localize the client. In order to upkeep the
localization accuracy, we propose two switching schemes: one based on a Kalman
filter, and one based on the statistical distribution of the training data. We
analyze the proposed schemes via simulations, showing that our approach
outperforms both geometric localization schemes and the use of a single NN.