Computer Science > Networking and Internet Architecture
[Submitted on 9 Dec 2021 (v1), last revised 20 May 2022 (this version, v2)]
Title:Millimeter Wave Localization with Imperfect Training Data using Shallow Neural Networks
View PDFAbstract: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.
Submission history
From: Anish Shastri [view email][v1] Thu, 9 Dec 2021 16:03:30 UTC (881 KB)
[v2] Fri, 20 May 2022 15:53:45 UTC (879 KB)
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