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Millimeter Wave Localization with Imperfect Training Data using Shallow Neural Networks. (arXiv:2112.05008v2 [cs.NI] UPDATED)
May 23, 2022, 1:11 a.m. | Anish Shastri, Joan Palacios, Paolo Casari
cs.LG updates on arXiv.org arxiv.org
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 …
arxiv data localization networks neural networks training training data
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