Leveraging Channel Charting for Localization With Weakly Supervised Learning
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.