# RA-LWLM: Retrieval-Augmented In-Context Localization with Wireless Foundation Models

arXiv: https://arxiv.org/abs/2606.01899 · HTML: https://mlsanity.com/paper/2606.01899/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 41 references that are in the map.
- First posted on arXiv 1 Jun 2026.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [In-Context Retrieval-Augmented Language Models](https://mlsanity.com/paper/2302.00083/) (2023)
- [RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs](https://mlsanity.com/paper/2407.02485/) (2024)
- [Channel Charting: Locating Users within the Radio Environment using Channel State Information](https://mlsanity.com/paper/1807.05247/) (2018)
- [In-Context Learning with Representations: Contextual Generalization of Trained Transformers](https://mlsanity.com/paper/2408.10147/) (2024)
- [MetaLoc: Learning to Learn Wireless Localization](https://mlsanity.com/paper/2211.04258/) (2022)
- [Triplet-Based Wireless Channel Charting: Architecture and Experiments](https://mlsanity.com/paper/2005.12242/) (2020)
- [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)
- [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)
- [A Tutorial on Terahertz-Band Localization for 6G Communication Systems](https://mlsanity.com/paper/2110.08581/) (2021)
- [Positioning Using Wireless Networks: Applications, Recent Progress and Future Challenges](https://mlsanity.com/paper/2403.11417/) (2024)
- [Towards channel foundation models (CFMs): Motivations, methodologies and opportunities](https://mlsanity.com/paper/2507.13637/) (2025)
- [CSI2Vec: Towards a Universal CSI Feature Representation for Positioning and Channel Charting](https://mlsanity.com/paper/2506.05237/) (2025)
- [Large AI Models for Wireless Physical Layer](https://mlsanity.com/paper/2508.02314/) (2025)
- [Attentional Graph Neural Network Is All You Need for Robust Massive Network Localization](https://mlsanity.com/paper/2311.16856/) (2023)
- [Retrieval-augmented Generation for GenAI-enabled Semantic Communications](https://mlsanity.com/paper/2412.19494/) (2024)
- [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)
- [Attentional Graph Meta-Learning for Indoor Localization Using Extremely Sparse Fingerprints](https://mlsanity.com/paper/2504.04829/) (2025)
- [Transfer Learning for CSI-based Positioning with Multi-environment Meta-learning](https://mlsanity.com/paper/2405.11816/) (2024)
- [Multi-Sources Fusion Learning for Multi-Points NLOS Localization in OFDM System](https://mlsanity.com/paper/2409.02454/) (2024)
- [UNILocPro: Unified Localization Integrating Model-Based Geometry and Channel Charting](https://mlsanity.com/paper/2510.27394/) (2025)
- [Positioning via Digital-Twin-Aided Channel Charting with Large-Scale CSI Features](https://mlsanity.com/paper/2511.09227/) (2025)
- [A Retrieval-Assisted Framework for Wireless Localization](https://mlsanity.com/paper/2603.06158/) (2026)

## Abstract (arXiv)

Wireless localization is a fundamental capability of sixth-generation (6G) networks. Conventional model-based methods require accurate modeling of the propagation environment and degrade in complex multipath and non-line-of-sight scenarios, while learning-based methods couple model parameters tightly to the training scene, requiring costly retraining whenever the base station (BS) configuration or propagation environment changes. In this paper, we propose RA-LWLM, a retrieval-augmented in-context localization framework that achieves training-free cross-scene adaptation by externalizing scene-specific information into a per-scene fingerprint database rather than encoding it in model weights. The framework consists of three components: a frozen wireless foundation model (FM) encoder that maps raw channel state information into a scene-agnostic representation; a retrieval module that selects the most informative references from the per-scene database via similarity search in the representation space; and a transformer-based in-context learning (ICL) module that fuses the query with the retrieved references to predict the user equipment (UE) position. To accommodate varying retrieval quality and propagation complexity across queries, the ICL module adopts a mixture-of-experts design in which experts specialize in different context sizes and are softly combined by a learnable selector. Extensive ray-tracing-based experiments across heterogeneous scenes with diverse BS configurations show that RA-LWLM achieves nearly identical accuracy on seen and unseen scenes without any per-scene retraining, substantially outperforming end-to-end and FM-based baselines. These results validate the proposed retrieval-augmented in-context paradigm as a scalable solution for cross-scene localization in 6G networks.