# Large Wireless Localization Model (LWLM): A Foundation Model for Positioning in 6G Networks

arXiv: https://arxiv.org/abs/2505.10134 · HTML: https://mlsanity.com/paper/2505.10134/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 50 references that are in the map.
- First posted on arXiv 15 May 2025.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet](https://mlsanity.com/paper/2101.11986/) (2021)
- [SpectralGPT: Spectral Remote Sensing Foundation Model](https://mlsanity.com/paper/2311.07113/) (2023)
- [A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends](https://mlsanity.com/paper/2301.05712/) (2023)
- [DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications](https://mlsanity.com/paper/1902.06435/) (2019)
- [Towards artificial general intelligence via a multimodal foundation model](https://mlsanity.com/paper/2110.14378/) (2021)
- [To Compress or Not to Compress- Self-Supervised Learning and Information Theory: A Review](https://mlsanity.com/paper/2304.09355/) (2023)
- [LLM4CP: Adapting Large Language Models for Channel Prediction](https://mlsanity.com/paper/2406.14440/) (2024)
- [Large Wireless Model (LWM): A Foundation Model for Wireless Channels](https://mlsanity.com/paper/2411.08872/) (2024)
- [WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication](https://mlsanity.com/paper/2502.06877/) (2025)
- [WiFo: Wireless Foundation Model for Channel Prediction](https://mlsanity.com/paper/2412.08908/) (2024)
- [MetaLoc: Learning to Learn Wireless Localization](https://mlsanity.com/paper/2211.04258/) (2022)
- [LLM4WM: Adapting LLM for Wireless Multi-Tasking](https://mlsanity.com/paper/2501.12983/) (2025)
- [Self-Supervised and Invariant Representations for Wireless Localization](https://mlsanity.com/paper/2302.07000/) (2023)
- [Angle-Delay Profile-Based and Timestamp-Aided Dissimilarity Metrics for Channel Charting](https://mlsanity.com/paper/2308.09539/) (2023)
- [Learning to Localize: A 3D CNN Approach to User Positioning in Massive MIMO-OFDM Systems](https://mlsanity.com/paper/1910.12378/) (2019)
- [Radio Foundation Models: Pre-training Transformers for 5G-based Indoor Localization](https://mlsanity.com/paper/2410.00617/) (2024)
- [A Tutorial on Terahertz-Band Localization for 6G Communication Systems](https://mlsanity.com/paper/2110.08581/) (2021)
- [A Tutorial on 5G Positioning](https://mlsanity.com/paper/2311.10551/) (2023)
- [AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challenges](https://mlsanity.com/paper/2501.14970/) (2025)
- [High Accurate Time-of-Arrival Estimation with Fine-Grained Feature Generation for Internet-of-Things Applications](https://mlsanity.com/paper/2008.07932/) (2020)
- [MIMO-OFDM Joint Radar-Communications: Is ICI Friend or Foe?](https://mlsanity.com/paper/2103.15694/) (2021)
- [Robust NLoS Localization in 5G mmWave Networks: Data-based Methods and Performance](https://mlsanity.com/paper/2406.16519/) (2024)
- [6G Positioning and Sensing Through the Lens of Sustainability, Inclusiveness, and Trustworthiness](https://mlsanity.com/paper/2309.13602/) (2023)

## Built on by (most cited first, 11 of 11)

- [HeterCSI: Channel-Adaptive Heterogeneous CSI Pretraining Framework for Generalized Wireless Foundation Models](https://mlsanity.com/paper/2601.18200/) (2026)
- [A Multi-Modal Foundational Model for Wireless Communication and Sensing](https://mlsanity.com/paper/2602.04016/) (2026)
- [AirFM-DDA: Air-Interface Foundation Model in the Delay-Doppler-Angle Domain for AI-Native 6G](https://mlsanity.com/paper/2605.00020/) (2026)
- [Simplicity is Key: An Unsupervised Pretraining Approach for Sparse Radio Channels](https://mlsanity.com/paper/2505.13055/) (2025)
- [Map as a Prompt: Learning Multi-Modal Spatial-Signal Foundation Models for Cross-scenario Wireless Localization](https://mlsanity.com/paper/2607.15713/) (2026)
- [PilotWiMAE: Pilot-Native Representation Learning for Wireless Channels](https://mlsanity.com/paper/2605.22856/) (2026)
- [RA-LWLM: Retrieval-Augmented In-Context Localization with Wireless Foundation Models](https://mlsanity.com/paper/2606.01899/) (2026)
- [OmniLoc: A Geometry-Aware Foundation Model for Anchor-Free UE Localization Across Diverse Indoor Environments](https://mlsanity.com/paper/2606.11490/) (2026)
- [6G Native AI and Channel Foundation Models](https://mlsanity.com/paper/2608.14591/) (2026)
- [A Comprehensive Survey of Wireless Foundation Models for AI-Native 6G Networks](https://mlsanity.com/paper/2608.14694/) (2026)
- [Wireless Foundation Models: State-of-the-Art and Open Challenges](https://mlsanity.com/paper/2609.04707/) (2026)

## Abstract (arXiv)

Accurate and robust localization is a critical enabler for emerging 5G and 6G applications, including autonomous driving, extended reality (XR), and smart manufacturing. While data-driven approaches have shown promise, most existing models require large amounts of labeled data and struggle to generalize across deployment scenarios and wireless configurations. To address these limitations, we propose a foundation-model-based solution tailored for wireless localization. We first analyze how different self-supervised learning (SSL) tasks acquire general-purpose and task-specific semantic features based on information bottleneck (IB) theory. Building on this foundation, we design a pretraining methodology for the proposed Large Wireless Localization Model (LWLM). Specifically, we propose an SSL framework that jointly optimizes three complementary objectives: (i) spatial-frequency masked channel modeling (SF-MCM), (ii) domain-transformation invariance (DTI), and (iii) position-invariant contrastive learning (PICL). These objectives jointly capture the underlying semantics of wireless channel from multiple perspectives. We further design lightweight decoders for key downstream tasks, including time-of-arrival (ToA) estimation, angle-of-arrival (AoA) estimation, single base station (BS) localization, and multiple BS localization. Comprehensive experimental results confirm that LWLM consistently surpasses both model-based and supervised learning baselines across all localization tasks. In particular, LWLM achieves 26.0%--87.5% improvement over transformer models without pretraining, and exhibits strong generalization under label-limited fine-tuning and unseen BS configurations, confirming its potential as a foundation model for wireless localization.