# Millimeter Wave Localization with Imperfect Training Data using Shallow Neural Networks

arXiv: https://arxiv.org/abs/2112.05008 · HTML: https://mlsanity.com/paper/2112.05008/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 9 Dec 2021.

## Builds on (most cited first, 4 of 4)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Millimeter Wave Cellular Wireless Networks: Potentials and Challenges](https://mlsanity.com/paper/1401.2560/) (2014)
- [Where, When, and How mmWave is Used in 5G and Beyon](https://mlsanity.com/paper/1704.08131/) (2017)
- [Map-Assisted Millimeter Wave Localization for Accurate Position Location](https://mlsanity.com/paper/1908.09773/) (2019)

## Built on by (most cited first, 2 of 2)

- [A Review of Indoor Millimeter Wave Device-based Localization and Device-free Sensing Technologies and Applications](https://mlsanity.com/paper/2112.05593/) (2021)
- [Indoor Millimeter Wave Localization using Multiple Self-Supervised Tiny Neural Networks](https://mlsanity.com/paper/2311.18732/) (2023)

## Abstract (arXiv)

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.