A Review of Indoor Millimeter Wave Device-based Localization and Device-free Sensing Technologies and Applications
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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.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 27 of its 257 references that are in the map. First posted on arXiv 10 Dec 2021.
Builds on
27 of 257 references.
References · 27
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15 more
- 2021Real-time People Tracking and Identification from Sparse mm-Wave Radar Point-cloudscited by 6
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Built on by
Citing papers per year: 2023: 1; 2024: 0; 2025: 5; 2026: 1.
7 papers.
- 2025AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challengescited by 5
- 2025Channel Charting in Smart Radio Environmentscited by 1
- 2023Indoor Millimeter Wave Localization using Multiple Self-Supervised Tiny Neural Networkscited by 0
- 2025Towards Channel Charting Enhancement with Non-Reconfigurable Intelligent Surfacescited by 0
- 2025Exploring Spatial-Temporal Representation via Star Graph for mmWave Radar-based Human Activity Recognitioncited by 0
- 2025Comprehensive Evaluation of Rule-Based, Machine Learning, and Deep Learning in Human Estimation Using Radio Wave Sensing: Accuracy, Spatial Generalization, and Output Granularity Trade-offscited by 0
- 2026When Simultaneous Localization and Mapping Meets Wireless Communications: A Surveycited by 0
- 2026When Simultaneous Localization and Mapping Meets Wireless Communications: A Survey28 Jan 2026
- 2025Comprehensive Evaluation of Rule-Based, Machine Learning, and Deep Learning in Human Estimation Using Radio Wave Sensing: Accuracy, Spatial Generalization, and Output Granularity Trade-offs15 Dec 2025
- 2025Exploring Spatial-Temporal Representation via Star Graph for mmWave Radar-based Human Activity Recognition12 Dec 2025
- 2025Towards Channel Charting Enhancement with Non-Reconfigurable Intelligent Surfaces2 Nov 2025
- 2025Channel Charting in Smart Radio Environments10 Aug 2025
- 2025AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challenges24 Jan 2025
- 2023Indoor Millimeter Wave Localization using Multiple Self-Supervised Tiny Neural Networks30 Nov 2023