Indoor Localization using Compact, Telemetry-Agnostic, Transfer-Learning Enabled Decoder-Only Transformer

2025-10-13

AI Summary

Indoor Wi-Fi localization faces significant challenges—including high calibration overhead and poor generalizability—due to dynamic environmental conditions, time-varying channel characteristics, and hardware heterogeneity. To address these, we propose Locaris, the first localization system leveraging a decoder-only large language model (LLM) for wireless signal regression. Locaris models raw Wi-Fi access point (AP) measurements as discrete tokens, enabling end-to-end, calibration-free positioning. Its core innovation lies in redefining LLM applicability to continuous-value regression tasks by integrating transfer learning with few-shot fine-tuning, thereby supporting cross-device and cross-environment generalization. Extensive experiments demonstrate that Locaris achieves sub-meter accuracy across diverse deployment scenarios, maintains robust performance with only hundreds of training samples, and gracefully handles missing APs and heterogeneous hardware configurations.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Intelligent Robots: Localization, Mapping, and Navigation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

Indoor Wi-Fi positioning remains a challenging problem due to the high sensitivity of radio signals to environmental dynamics, channel propagation characteristics, and hardware heterogeneity. Conventional fingerprinting and model-based approaches typically require labor-intensive calibration and suffer rapid performance degradation when devices, channel or deployment conditions change. In this paper, we introduce Locaris, a decoder-only large language model (LLM) for indoor localization. Locaris treats each access point (AP) measurement as a token, enabling the ingestion of raw Wi-Fi telemetry without pre-processing. By fine-tuning its LLM on different Wi-Fi datasets, Locaris learns a lightweight and generalizable mapping from raw signals directly to device location. Our experimental study comparing Locaris with state-of-the-art methods consistently shows that Locaris matches or surpasses existing techniques for various types of telemetry. Our results demonstrate that compact LLMs can serve as calibration-free regression models for indoor localization, offering scalable and robust cross-environment performance in heterogeneous Wi-Fi deployments. Few-shot adaptation experiments, using only a handful of calibration points per device, further show that Locaris maintains high accuracy when applied to previously unseen devices and deployment scenarios. This yields sub-meter accuracy with just a few hundred samples, robust performance under missing APs and supports any and all available telemetry. Our findings highlight the practical viability of Locaris for indoor positioning in the real-world scenarios, particularly in large-scale deployments where extensive calibration is infeasible.

Problem

• Indoor Wi-Fi positioning struggles with environmental sensitivity and hardware heterogeneity
• Conventional methods require labor-intensive calibration and degrade rapidly
• Locaris enables calibration-free localization using compact transformer models

Innovation

• Decoder-only transformer model for indoor localization
• Uses raw Wi-Fi telemetry without preprocessing
• Enables few-shot adaptation with minimal calibration