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We consider the localization of a mobile millimeter-wave client in a largeindoor environment using multilayer perceptron neural networks (NNs). Insteadof training and deploying a single deep model, we proceed by choosing amongmultiple tiny NNs trained in a self-supervised manner. The main challenge thenbecomes to determine and switch to the best NN among the available ones, as anincorrect NN will fail to localize the client. In order to upkeep thelocalization accuracy, we propose two switching schemes: one based on a Kalmanfilter, and one based on the statistical distribution of the training data. Weanalyze the proposed schemes via simulations, showing that our approachoutperforms both geometric localization schemes and the use of a single NN.

Indoor Millimeter Wave Localization using Multiple Self-Supervised Tiny Neural Networks