# User Localization using RF Sensing: A Performance comparison between LIS and mmWave Radars

arXiv: https://arxiv.org/abs/2205.10321 · HTML: https://mlsanity.com/paper/2205.10321/

## Key facts

- No citing papers in the map yet (data through 7 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 17 May 2022.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Tesla-Rapture: A Lightweight Gesture Recognition System from mmWave Radar Point Clouds](https://mlsanity.com/paper/2109.06448/) (2021)
- [Assessing Wireless Sensing Potential with Large Intelligent Surfaces](https://mlsanity.com/paper/2011.08465/) (2020)
- [Radio Sensing with Large Intelligent Surface for 6G](https://mlsanity.com/paper/2111.02783/) (2021)
- [Integrating Sensing and Communication in Cellular Networks via NR Sidelink](https://mlsanity.com/paper/2109.07253/) (2021)

## Abstract (arXiv)

Since electromagnetic signals are omnipresent, Radio Frequency (RF)-sensing has the potential to become a universal sensing mechanism with applications in localization, smart-home, retail, gesture recognition, intrusion detection, etc. Two emerging technologies in RF-sensing, namely sensing through Large Intelligent Surfaces (LISs) and mmWave Frequency-Modulated Continuous-Wave (FMCW) radars, have been successfully applied to a wide range of applications. In this work, we compare LIS and mmWave radars for localization in real-world and simulated environments. In our experiments, the mmWave radar achieves 0.71 Intersection Over Union (IOU) and 3cm error for bounding boxes, while LIS has 0.56 IOU and 10cm distance error. Although the radar outperforms the LIS in terms of accuracy, LIS features additional applications in communication in addition to sensing scenarios.