Millimeter Wave Localization with Imperfect Training Data using Shallow Neural Networks
This addresses the challenge of data collection for mmWave localization in resource-constrained environments, though it is incremental as it builds on existing learning-based approaches.
The paper tackles the problem of indoor millimeter wave localization with limited training data by proposing a shallow neural network that requires fewer weights and training samples, and shows it performs as well as or better than state-of-the-art algorithms even when using imperfect training data from geometry-based methods.
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.