Unsupervised Learning for Gain-Phase Impairment Calibration in ISAC Systems
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.
Authors
- José Miguel Mateos-Ramos (ORCID: https://orcid.org/0000-0001-7859-9090)
- Luc Le Magoarou (ORCID: https://orcid.org/0000-0002-2871-4406)
- Christian Häger (ORCID: https://orcid.org/0000-0002-5155-8018)
- Henk Wymeersch (ORCID: https://orcid.org/0000-0002-1298-6159)
- Musa Furkan Keskin (ORCID: https://orcid.org/0000-0002-7718-8377)
Institutions
- Centre National de la Recherche Scientifique (FR)
- Institut National des Sciences Appliquées de Rennes (FR)
- Institut d'Électronique et des Technologies du numéRique (FR)
- Université de Rennes (FR)
- Chalmers University of Technology (SE)
Publication Details
- Published
- 2025-03-12
- DOI
- https://doi.org/10.1109/icassp49660.2025.10890700
- Citations
- 1
- Primary Topic
- Wireless Signal Modulation Classification
- Type
- article
- Field-Weighted Citation Impact
- 2.48
Funders
- European Commission
- Agence Nationale de la Recherche
- Stiftelsen för Strategisk Forskning
- Vetenskapsrådet
- HORIZON EUROPE Framework Programme