Millimeter Wave Localization with Imperfect Training Data using Shallow Neural Networks

• URL: http://arxiv.org/abs/2112.05008v1
• Date: Thu, 9 Dec 2021 16:03:30 GMT
• Title: Millimeter Wave Localization with Imperfect Training Data using Shallow Neural Networks
• Authors: Anish Shastri, Joan Palacios, and Paolo Casari
• Abstract summary: We propose a shallow neural network model to localize mmWave devices indoors. This model requires significantly fewer weights than those proposed in the literature. We also propose to relieve training data collection efforts by retrieving location estimates from geometry-based mmWave localization algorithms.
• Score: 13.454939391912095
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Millimeter wave (mmWave) localization algorithms exploit the quasi-optical propagation of mmWave signals, which yields sparse angular spectra at the receiver. Geometric approaches to angle-based localization typically require to know the map of the environment and the location of the access points. Thus, several works have resorted to automated learning in order to infer a device's location from the properties of the received mmWave signals. However, collecting training data for such models is a significant burden. In this work, we propose a shallow neural network model to localize mmWave devices indoors. This model requires significantly fewer weights than those proposed in the literature. Therefore, it is amenable for implementation in resource-constrained hardware, and needs fewer training samples to converge. We also propose to relieve training data collection efforts by retrieving (inherently imperfect) location estimates from geometry-based mmWave localization algorithms. Even in this case, our results show that the proposed neural networks perform as good as or better than state-of-the-art algorithms.

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