Channel charting (CC) is a self-supervised learning technique which aims to construct a lower-dimensional representation of the channel measurements, while preserving the neighboring relationship of users. In this letter, we propose a machine learning approach for radio-based localization task with the aid of CC, which fully exploits the dissimilarity extracted solely from the channel measurements. To this end, a hybrid model structure inspired by physical principles is employed, which is realized by a computationally efficient two-layer neural network initialized with a channel chart. The training process employs a weakly-supervised approach that combines: 1) a Siamese network architecture preserving relative user neighborhood relationship through channel dissimilarity metrics, and 2) a limited set of anchor points with ground-truth location annotations to establish absolute positional references. The proposed approach is empirically validated on realistic channel data, achieving encouraging localization accuracy compared to benchmark approaches.
PACC designs a propagation-aware dissimilarity metric that adapts to line-of-sight and non-line-of-sight propagation conditions, thereby preserving both local neighborhood relationships and the intrinsic geometry of the radio environment.
A unified framework for FM-based wireless localization that learns transferable channel representations from large-scale unlabeled channel state information and adapts to new environments with minimal or even no supervision is presented.
Guangjin Pan, Jia-Jia Guo, Zheng Xing et al.· 0 citations
A deep learning-based approach is presented that optimizes environmental input construction for accurate channel path loss prediction and validate that the input feature construction improves channel prediction accuracy and provides guidance for future intelligent channel prediction.
Zhicheng Qiu, Rui-Si He, Bo Ai et al.· npj Wireless Technology· 0 citations
Channel charting is a self-supervised framework that learns low-dimensional spatial representations from high-dimensional channel state information. We revisit channel charting from a graph-theoretic perspective, and show that the position-diffusion objective is equivalent to a graph Laplacian smoothness functional. We...
Yinri Jin, Yu-Xin Zhao, Dan-Dan Hao et al.· 0 citations
Channel Charting is a self-supervised learning technique that maps high-dimensional Channel State Information into a low-dimensional latent space preserving the spatial topology of user equipment trajectories, without relying on ground-truth position labels. While recent work has extended Channel Charting with neural t...
A. Piroddi, T. Kanakis, M. O. Agyeman· IEEE Access· 0 citations
Non-line-of-sight (NLOS) identification is essential for accurate range-only localization (ROL) but remains challenging in high geometric dilution of precision (high-GDOP) environments. This letter proposes a geometric consensus convolutional neural network (GC-CNN) framework for simultaneous multi-anchor NLOS identifi...
