A Tutorial on Terahertz-Band Localization for 6G Communication Systems
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Abstract (arXiv)
Terahertz (THz) communications are celebrated as key enablers for converged localization and sensing in future sixth-generation (6G) wireless communication systems and beyond. Instead of being a byproduct of the communication system, localization in 6G is indispensable for location-aware communications. Towards this end, we aim to identify the prospects, challenges, and requirements of THz localization techniques. We first review the history and trends of localization methods and discuss their objectives, constraints, and applications in contemporary communication systems. We then detail the latest advances in THz communications and introduce the THz-specific channel and system models. Afterward, we formulate THz-band localization as a 3D position/orientation estimation problem, detailing geometry-based localization techniques and describing potential THz localization and sensing extensions. We further formulate the offline design and online optimization of THz localization systems, provide numerical simulation results, and conclude by providing lessons learned and future research directions. Preliminary results illustrate that under the same transmission power and array footprint, THz-based localization outperforms millimeter wave-based localization. In other words, the same level of localization performance can be achieved at THz-band with less transmission power or a smaller footprint.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Oct 2021.
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Citing papers per year: 2024: 2; 2025: 4; 2026: 2.
8 papers.
- 2025Large Wireless Localization Model (LWLM): A Foundation Model for Positioning in 6G Networkscited by 10
- 2025AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challengescited by 5
- 2024Metarobotics for Industry and Society: Vision, Technologies, and Opportunitiescited by 2
- 2024Transfer Learning for CSI-based Positioning with Multi-environment Meta-learningcited by 1
- 20255G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localizationcited by 1
- 2026Map as a Prompt: Learning Multi-Modal Spatial-Signal Foundation Models for Cross-scenario Wireless Localizationcited by 1
- 2025RadioDiff-Loc: Diffusion Model Enhanced Scattering Congnition for NLoS Localization with Sparse Radio Map Estimationcited by 0
- 2026RA-LWLM: Retrieval-Augmented In-Context Localization with Wireless Foundation Modelscited by 0
- 2026Map as a Prompt: Learning Multi-Modal Spatial-Signal Foundation Models for Cross-scenario Wireless Localization17 Jul 2026
- 2026RA-LWLM: Retrieval-Augmented In-Context Localization with Wireless Foundation Models1 Jun 2026
- 2025RadioDiff-Loc: Diffusion Model Enhanced Scattering Congnition for NLoS Localization with Sparse Radio Map Estimation2 Sep 2025
- 20255G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization23 May 2025
- 2025Large Wireless Localization Model (LWLM): A Foundation Model for Positioning in 6G Networks15 May 2025
- 2025AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challenges24 Jan 2025
- 2024Transfer Learning for CSI-based Positioning with Multi-environment Meta-learning20 May 2024
- 2024Metarobotics for Industry and Society: Vision, Technologies, and Opportunities31 Mar 2024