Unsupervised Learning for Gain-Phase Impairment Calibration in ISAC Systems
José Miguel Mateos-Ramos, Christian Häger, Musa Furkan Keskin, Luc Le Magoarou, Henk Wymeersch
Abstract
Gain-phase impairments (GPIs) affect both communication and sensing in 6G integrated sensing and communication (ISAC). We study the effect of GPIs in a single-input, multiple-output orthogonal frequency-division multiplexing ISAC system and develop a model-based unsupervised learning approach to simultaneously (i) estimate the gain-phase errors and (ii) localize sensing targets. The proposed method is based on the optimal maximum a-posteriori ratio test for a single target. Results show that the proposed approach can effectively estimate the gain-phase errors and yield similar position estimation performance as the case when the impairments are fully known.
BibTeX
@inproceedings{icassp2025_unsupervisedlear,
title = {Unsupervised Learning for Gain-Phase Impairment Calibration in ISAC Systems},
author = {José Miguel Mateos-Ramos and Christian Häger and Musa Furkan Keskin and Luc Le Magoarou and Henk Wymeersch},
booktitle = {ICASSP 2025},
year = {2025}
}