RA-LWLM: Retrieval-Augmented In-Context Localization with Wireless Foundation Models
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 weight...
| Authors | Guangjin Pan, Hui Chen, Hei Victor Cheng et al. |
|---|---|
| Primary category | eess.SP |
| Published | 1 June 2026 |
| Last updated at source | 1 June 2026 |
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