Unsupervised Radio Map Construction in Mixed LoS/NLoS Indoor Environments
Zheng Xing, Junting Chen
TL;DR
This work tackles the calibration bottleneck in radio map construction by introducing an unsupervised HMM-based framework that jointly recovers user trajectories and radio-map parameters from CSI in MIMO-OFDM indoor networks. By separately modeling LOS and NLOS propagation in terms of power, angle, and delay, and by employing a Gaussian-Markov mobility model, the method performs alternating optimization to infer trajectory, propagation, and LOS/NLOS classifications without location labels. Experimental validation via ray-tracing simulations shows an average localization error of about 0.65 m for trajectory recovery and substantial improvements over traditional baselines, with the radio map enabling near-supervised localization performance while avoiding labeled data. The proposed approach offers a scalable, label-free solution that reduces calibration costs and enhances localization accuracy in mixed LOS/NLOS indoor environments.
Abstract
Radio maps are essential for enhancing wireless communications and localization. However, existing methods for constructing radio maps typically require costly calibration processes to collect location-labeled channel state information (CSI) datasets. This paper aims to recover the data collection trajectory directly from the channel propagation sequence, eliminating the need for location calibration. The key idea is to employ a hidden Markov model (HMM)-based framework to conditionally model the channel propagation matrix, while simultaneously modeling the location correlation in the trajectory. The primary challenges involve modeling the complex relationship between channel propagation in multiple-input multiple-output (MIMO) networks and geographical locations, and addressing both line-of-sight (LOS) and non-line-of-sight (NLOS) indoor conditions. In this paper, we propose an HMM-based framework that jointly characterizes the conditional propagation model and the evolution of the user trajectory. Specifically, the channel propagation in MIMO networks is modeled separately in terms of power, delay, and angle, with distinct models for LOS and NLOS conditions. The user trajectory is modeled using a Gaussian-Markov model. The parameters for channel propagation, the mobility model, and LOS/NLOS classification are optimized simultaneously. Experimental validation using simulated MIMO-Orthogonal Frequency-Division Multiplexing (OFDM) networks with a multi-antenna uniform linear arrays (ULA) configuration demonstrates that the proposed method achieves an average localization accuracy of 0.65 meters in an indoor environment, covering both LOS and NLOS regions. Moreover, the constructed radio map enables localization with a reduced error compared to conventional supervised methods, such as k-nearest neighbors (KNN), support vector machine (SVM), and deep neural network (DNN).