Localization as a key enabler of 6G wireless systems: A comprehensive survey and an outlook
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Abstract (arXiv)
When fully implemented, sixth generation (6G) wireless systems will constitute intelligent wireless networks that enable not only ubiquitous communication but also high-accuracy localization services. They will be the driving force behind this transformation by introducing a new set of characteristics and service capabilities in which location will coexist with communication while sharing available resources. To that purpose, this survey investigates the envisioned applications and use cases of localization in future 6G wireless systems, while analyzing the impact of the major technology enablers. Afterwards, system models for millimeter wave, terahertz and visible light positioning that take into account both line-of-sight (LOS) and non-LOS channels are presented, while localization key performance indicators are revisited alongside mathematical definitions. Moreover, a detailed review of the state of the art conventional and learning-based localization techniques is conducted. Furthermore, the localization problem is formulated, the wireless system design is considered and the optimization of both is investigated. Finally, insights that arise from the presented analysis are summarized and used to highlight the most important future directions for localization in 6G wireless systems.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Feb 2023.
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Citing papers per year: 2024: 1; 2025: 4; 2026: 2.
7 papers.
- 2025AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challengescited by 5
- 2024Distributed AI Platform for the 6G RANcited by 2
- 2026A Multi-Modal Foundational Model for Wireless Communication and Sensingcited by 2
- 2025Reliable Wireless Indoor Localization via Cross-Validated Prediction-Powered Calibrationcited by 1
- 2025A Representation Learning Approach to Feature Drift Detection in Wireless Networkscited by 0
- 2025A Configuration-First Framework for Reproducible, Low-Code Machine Learning: a Localization Use Casecited by 0
- 2026IIns-VAE+: A Robust Transfer Learning Framework for Environmental Identification in Wireless Sensingcited by 0
- 2026IIns-VAE+: A Robust Transfer Learning Framework for Environmental Identification in Wireless Sensing5 Sep 2026
- 2026A Multi-Modal Foundational Model for Wireless Communication and Sensing3 Feb 2026
- 2025A Configuration-First Framework for Reproducible, Low-Code Machine Learning: a Localization Use Case29 Oct 2025
- 2025Reliable Wireless Indoor Localization via Cross-Validated Prediction-Powered Calibration27 Jul 2025
- 2025A Representation Learning Approach to Feature Drift Detection in Wireless Networks15 May 2025
- 2025AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challenges24 Jan 2025
- 2024Distributed AI Platform for the 6G RAN1 Oct 2024