# Unsupervised Radio Map Construction in Mixed LoS/NLoS Indoor Environments

arXiv: https://arxiv.org/abs/2510.08015 · HTML: https://mlsanity.com/paper/2510.08015/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 9 Oct 2025.

## Builds on (most cited first, 9 of 9)

- [Triplet-Based Wireless Channel Charting: Architecture and Experiments](https://mlsanity.com/paper/2005.12242/) (2020)
- [Environment-Aware Hybrid Beamforming by Leveraging Channel Knowledge Map](https://mlsanity.com/paper/2206.08707/) (2022)
- [Spectrum Surveying: Active Radio Map Estimation with Autonomous UAVs](https://mlsanity.com/paper/2201.04125/) (2022)
- [Sparse Bayesian Learning-Based Hierarchical Construction for 3D Radio Environment Maps Incorporating Channel Shadowing](https://mlsanity.com/paper/2403.08323/) (2024)
- [UAV-aided Radio Map Construction Exploiting Environment Semantics](https://mlsanity.com/paper/2107.10574/) (2021)
- [Channel Charting in Real-World Coordinates with Distributed MIMO](https://mlsanity.com/paper/2406.13722/) (2024)
- [Constructing Indoor Region-based Radio Map without Location Labels](https://mlsanity.com/paper/2308.16759/) (2023)
- [Memoryless Techniques and Wireless Technologies for Indoor Localization with the Internet of Things](https://mlsanity.com/paper/2005.01877/) (2020)
- [Blind Construction of Angular Power Maps in Massive MIMO Networks](https://mlsanity.com/paper/2510.07071/) (2025)

## Built on by (most cited first, 1 of 1)

- [Temporal Visual Semantics-Induced Human Motion Understanding with Large Language Models](https://mlsanity.com/paper/2512.22249/) (2025)

## Abstract (arXiv)

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).