# OmniLoc: A Geometry-Aware Foundation Model for Anchor-Free UE Localization Across Diverse Indoor Environments

arXiv: https://arxiv.org/abs/2606.11490 · HTML: https://mlsanity.com/paper/2606.11490/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 32 of its 78 references that are in the map.
- First posted on arXiv 9 Jun 2026.

## Builds on (most cited first, 30 of 32)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [Self-Attention Graph Pooling](https://mlsanity.com/paper/1904.08082/) (2019)
- [A Structured Self-attentive Sentence Embedding](https://mlsanity.com/paper/1703.03130/) (2017)
- [DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications](https://mlsanity.com/paper/1902.06435/) (2019)
- [A Survey of Indoor Localization Systems and Technologies](https://mlsanity.com/paper/1709.01015/) (2017)
- [Pooling Methods in Deep Neural Networks, a Review](https://mlsanity.com/paper/2009.07485/) (2020)
- [Channel Charting: Locating Users within the Radio Environment using Channel State Information](https://mlsanity.com/paper/1807.05247/) (2018)
- [CSI-based Fingerprinting for Indoor Localization: A Deep Learning Approach](https://mlsanity.com/paper/1603.07080/) (2016)
- [WiFo: Wireless Foundation Model for Channel Prediction](https://mlsanity.com/paper/2412.08908/) (2024)
- [Large Multi-Modal Models (LMMs) as Universal Foundation Models for AI-Native Wireless Systems](https://mlsanity.com/paper/2402.01748/) (2024)
- [CSI-based Positioning in Massive MIMO systems using Convolutional Neural Networks](https://mlsanity.com/paper/1911.11523/) (2019)
- [Finding the Missing Data: A BERT-inspired Approach Against Package Loss in Wireless Sensing](https://mlsanity.com/paper/2403.12400/) (2024)
- [A Comprehensive Survey of Machine Learning Based Localization with Wireless Signals](https://mlsanity.com/paper/2012.11171/) (2020)
- [6G WavesFM: A Foundation Model for Sensing, Communication, and Localization](https://mlsanity.com/paper/2504.14100/) (2025)
- [Self-Supervised and Invariant Representations for Wireless Localization](https://mlsanity.com/paper/2302.07000/) (2023)
- [Large Wireless Localization Model (LWLM): A Foundation Model for Positioning in 6G Networks](https://mlsanity.com/paper/2505.10134/) (2025)
- [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 State-of-the-Art Survey on Multidimensional Scaling Based Localization Techniques](https://mlsanity.com/paper/1906.03585/) (2019)
- [Channel Charting in Real-World Coordinates with Distributed MIMO](https://mlsanity.com/paper/2406.13722/) (2024)
- [DyLoc: Dynamic Localization for Massive MIMO Using Predictive Recurrent Neural Networks](https://mlsanity.com/paper/2101.07848/) (2021)
- [Exploiting Semantic Localization in Highly Dynamic Wireless Networks Using Deep Homoscedastic Domain Adaptation](https://mlsanity.com/paper/2310.07792/) (2023)
- [UBiGTLoc: A Unified BiLSTM-Graph Transformer Localization Framework for IoT Sensor Networks](https://mlsanity.com/paper/2601.10743/) (2026)
- [Context-aware Telco Outdoor Localization](https://mlsanity.com/paper/2108.10651/) (2021)
- [Transforming Indoor Localization: Advanced Transformer Architecture for NLOS Dominated Wireless Environments with Distributed Sensors](https://mlsanity.com/paper/2501.07774/) (2025)
- [Wireless AI Evolution: From Statistical Learners to Electromagnetic-Guided Foundation Models](https://mlsanity.com/paper/2507.00366/) (2025)

## Abstract (arXiv)

Indoor localization from wireless measurements remains challenging in large-scale deployments due to substantial variation in building geometry, the set of detectable access points (APs), and the heterogeneity of received signals. Existing learning-based methods often perform well only in limited settings and degrade under environmental shifts, making robust anchor-free localization across diverse indoor environments notoriously difficult. In this paper, we present OmniLoc, an environment-interactive foundation model for anchor-free user equipment localization across diverse indoor environments. To the best of our knowledge, OmniLoc is the first foundation-model-based approach built directly on wireless measurements for this task. OmniLoc is built on three key designs. First, a unified input tokenization module converts heterogeneous wireless measurements into a common representation that is more amenable to learning. Second, a geometry-aware Transformer performs AP-aware feature extraction by emphasizing dominant APs while aggregating complementary evidence from supporting APs. Third, a geometry-aware location estimation module conditions regression on geometric embeddings to produce geometrically consistent location predictions. We evaluate OmniLoc on both a large-scale in-house dataset and a public benchmark dataset. Results show that OmniLoc significantly outperforms existing methods, consistently improves existing backbones when its design components are integrated, and demonstrates strong generalization in cross-environment evaluations.