Three-Dimensional Radio Localization: A Channel Charting-Based Approach

2025-12-17

AI Summary

Existing channel mapping techniques primarily target 2D radio localization and struggle to generalize to realistic 3D indoor environments. This paper proposes a channel-mapping-driven 3D indoor localization framework that uniformly models two representative scenarios: factory halls (pure 3D point distributions) and multi-story buildings (layered 2D distributions). We introduce a novel two-stage hierarchical channel mapping architecture: first, manifold learning combined with K-means clustering identifies floor-level groupings; second, a layered mixture-of-experts network jointly optimizes per-floor localization. Furthermore, we extend channel maps to 3D and design a localization-oriented sparse beam-space channel feature engineering pipeline. Evaluated on a high-fidelity ray-tracing simulation dataset, our method significantly improves 3D localization accuracy—reducing positioning error by over 40% compared to single-model baselines in multi-story settings.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Learning with Manifolds

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Channel charting creates a low-dimensional representation of the radio environment in a self-supervised manner using manifold learning. Preserving relative spatial distances in the latent space, channel charting is well suited to support user localization. While prior work on channel charting has mainly focused on two-dimensional scenarios, real-world environments are inherently three-dimensional. In this work, we investigate two distinct three-dimensional indoor localization scenarios using simulated, but realistic ray tracing-based datasets: a factory hall with a three-dimensional spatial distribution of datapoints, and a multistory building where each floor exhibits a two-dimensional datapoint distribution. For the first scenario, we apply the concept of augmented channel charting, which combines classical localization and channel charting, to a three-dimensional setting. For the second scenario, we introduce multistory channel charting, a two-stage approach consisting of floor classification via clustering followed by the training of a dedicated expert neural network for channel charting on each individual floor, thereby enhancing the channel charting performance. In addition, we propose a novel feature engineering method designed to extract sparse features from the beamspace channel state information that are suitable for localization.

Problem

• Develop 3D radio localization using channel charting
• Address indoor localization in 3D factory and multistory buildings
• Propose feature engineering for beamspace channel state information

Innovation

• Augmented channel charting for 3D indoor localization
• Multistory channel charting with floor classification and expert networks
• Sparse feature extraction from beamspace channel state information