# A Review of Indoor Millimeter Wave Device-based Localization and Device-free Sensing Technologies and Applications

arXiv: https://arxiv.org/abs/2112.05593 · HTML: https://mlsanity.com/paper/2112.05593/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 257 references that are in the map.
- First posted on arXiv 10 Dec 2021.

## Builds on (most cited first, 27 of 27)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [PointConv: Deep Convolutional Networks on 3D Point Clouds](https://mlsanity.com/paper/1811.07246/) (2018)
- [A Survey of Indoor Localization Systems and Technologies](https://mlsanity.com/paper/1709.01015/) (2017)
- [mm-Pose: Real-Time Human Skeletal Posture Estimation using mmWave Radars and CNNs](https://mlsanity.com/paper/1911.09592/) (2019)
- [See Through Smoke: Robust Indoor Mapping with Low-cost mmWave Radar](https://mlsanity.com/paper/1911.00398/) (2019)
- [mD-Track: Leveraging Multi-Dimensionality in Passive Indoor Wi-Fi Tracking](https://mlsanity.com/paper/1812.03103/) (2018)
- [6G White Paper on Localization and Sensing](https://mlsanity.com/paper/2006.01779/) (2020)
- [Initial Access in 5G mm-Wave Cellular Networks](https://mlsanity.com/paper/1602.07731/) (2016)
- [Multi-Person Continuous Tracking and Identification from mm-Wave micro-Doppler Signatures](https://mlsanity.com/paper/2003.03571/) (2020)
- [Real-time People Tracking and Identification from Sparse mm-Wave Radar Point-clouds](https://mlsanity.com/paper/2105.11368/) (2021)
- [Multiple Patients Behavior Detection in Real-time using mmWave Radar and Deep CNNs](https://mlsanity.com/paper/1911.06363/) (2019)
- [RAPID: Retrofitting IEEE 802.11ay Access Points for Indoor Human Detection and Sensing](https://mlsanity.com/paper/2109.04819/) (2021)
- [Map-Assisted Millimeter Wave Localization for Accurate Position Location](https://mlsanity.com/paper/1908.09773/) (2019)
- [An Overview on Integrated Localization and Communication Towards 6G](https://mlsanity.com/paper/2006.01535/) (2020)
- [Millimeter Wave Sensing: A Review of Application Pipelines and Building Blocks](https://mlsanity.com/paper/2012.13664/) (2020)
- [IEEE 802.11ay based mmWave WLANs: Design Challenges and Solutions](https://mlsanity.com/paper/1803.07808/) (2018)
- [Indoor Wireless Channel Properties at Millimeter Wave and Sub-Terahertz Frequencies](https://mlsanity.com/paper/1908.09765/) (2019)
- [Multi-Frequency Multi-Scenario Millimeter Wave MIMO Channel Measurements and Modeling for B5G Wireless Communication Systems](https://mlsanity.com/paper/2007.13973/) (2020)
- [3-D Statistical Indoor Channel Model for Millimeter-Wave and Sub-Terahertz Bands](https://mlsanity.com/paper/2009.12971/) (2020)
- [Millimeter Wave Localization with Imperfect Training Data using Shallow Neural Networks](https://mlsanity.com/paper/2112.05008/) (2021)
- [A Channel Measurement Campaign for mmWave Communication in Industrial Settings](https://mlsanity.com/paper/1903.10502/) (2019)
- [28 GHz mmWave Channel Measurements and Modeling in a Library Environment](https://mlsanity.com/paper/1910.08632/) (2019)
- [Localization Error Bounds For 5G mmWave Systems Under I/Q Imbalance: An Extended Version](https://mlsanity.com/paper/1911.06124/) (2019)
- [Impact of Spatially Consistent Channels on Digital Beamforming for Millimeter-Wave Systems](https://mlsanity.com/paper/2003.01653/) (2020)

## Built on by (most cited first, 7 of 7)

- [AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challenges](https://mlsanity.com/paper/2501.14970/) (2025)
- [Channel Charting in Smart Radio Environments](https://mlsanity.com/paper/2508.07305/) (2025)
- [Indoor Millimeter Wave Localization using Multiple Self-Supervised Tiny Neural Networks](https://mlsanity.com/paper/2311.18732/) (2023)
- [Towards Channel Charting Enhancement with Non-Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2511.00919/) (2025)
- [Exploring Spatial-Temporal Representation via Star Graph for mmWave Radar-based Human Activity Recognition](https://mlsanity.com/paper/2512.12013/) (2025)
- [Comprehensive Evaluation of Rule-Based, Machine Learning, and Deep Learning in Human Estimation Using Radio Wave Sensing: Accuracy, Spatial Generalization, and Output Granularity Trade-offs](https://mlsanity.com/paper/2512.13031/) (2025)
- [When Simultaneous Localization and Mapping Meets Wireless Communications: A Survey](https://mlsanity.com/paper/2602.06995/) (2026)

## Abstract (arXiv)

The commercial availability of low-cost millimeter wave (mmWave) communication and radar devices is starting to improve the penetration of such technologies in consumer markets, paving the way for large-scale and dense deployments in fifth-generation (5G)-and-beyond as well as 6G networks. At the same time, pervasive mmWave access will enable device localization and device-free sensing with unprecedented accuracy, especially with respect to sub-6 GHz commercial-grade devices. This paper surveys the state of the art in device-based localization and device-free sensing using mmWave communication and radar devices, with a focus on indoor deployments. We first overview key concepts about mmWave signal propagation and system design. Then, we provide a detailed account of approaches and algorithms for localization and sensing enabled by mmWaves. We consider several dimensions in our analysis, including the main objectives, techniques, and performance of each work, whether each research reached some degree of implementation, and which hardware platforms were used for this purpose. We conclude by discussing that better algorithms for consumer-grade devices, data fusion methods for dense deployments, as well as an educated application of machine learning methods are promising, relevant and timely research directions.