Blind Radio Mapping via Spatially Regularized Bayesian Trajectory Inference

2025-12-04

AI Summary

Conventional blind radio map construction relies heavily on costly and impractical position labels. Method: This paper proposes a label-free, indoor MIMO-OFDM channel-driven approach. We first theoretically establish the spatial continuity of non-line-of-sight (NLOS) channel state information (CSI) and devise a CSI-distance metric accordingly; rigorously derive the asymptotic Cramér–Rao lower bound (CRLB) of localization error under rectangular trajectories—proving it converges to zero; and formulate a spatially regularized Bayesian joint inference framework to simultaneously estimate user trajectory, LOS/NLOS states, and spatial channel features. Contribution/Results: Leveraging quasi-specular environment modeling and CSI continuity analysis, our method achieves an average localization error of 0.68 m and beam-pattern reconstruction error of only 3.3% on ray-tracing datasets—significantly enhancing both accuracy and generalizability of radio maps in label-free scenarios.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Security and Privacy: Large-scale security measurements

Abstract

Radio maps enable intelligent wireless applications by capturing the spatial distribution of channel characteristics. However, conventional construction methods demand extensive location-labeled data, which are costly and impractical in many real-world scenarios. This paper presents a blind radio map construction framework that infers user trajectories from indoor multiple-input multiple-output (MIMO)-Orthogonal Frequency-Division Multiplexing (OFDM) channel measurements without relying on location labels. It first proves that channel state information (CSI) under non-line-of-sight (NLOS) exhibits spatial continuity under a quasi-specular environmental model, allowing the derivation of a CSI-distance metric that is proportional to the corresponding physical distance. For rectilinear trajectories in Poisson-distributed access point (AP) deployments, it is shown that the Cramer-Rao Lower Bound (CRLB) of localization error vanishes asymptotically, even under poor angular resolution. Building on these theoretical results, a spatially regularized Bayesian inference framework is developed that jointly estimates channel features, distinguishes line-of-sight (LOS)/NLOS conditions and recovers user trajectories. Experiments on a ray-tracing dataset demonstrate an average localization error of 0.68 m and a beam map reconstruction error of 3.3%, validating the effectiveness of the proposed blind mapping method.

Problem

• Blind radio map construction without location labels
• Inferring user trajectories from indoor MIMO-OFDM measurements
• Jointly estimating channel features and LOS/NLOS conditions

Innovation

• Blind trajectory inference from MIMO-OFDM channel measurements
• Spatially regularized Bayesian framework for joint feature estimation
• Derives CSI-distance metric enabling localization without location labels