Unsupervised Learning for Gain-Phase Impairment Calibration in ISAC Systems
Paper in proceeding, 2025

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.

Unsupervised learning

Model-based learning

GPIs

Orthogonal frequency-division multiplexing

Author

José Miguel Mateos Ramos

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Christian Häger

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Musa Furkan Keskin

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Luc Le Magoarou

Université de Rennes

Henk Wymeersch

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

15206149 (ISSN)

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025
Hyderabad, India,

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European Commission (EC) (101095759-Hexa-X-II), 2022-12-01 -- 2025-06-30.

Subject Categories (SSIF 2025)

Signal Processing

DOI

10.1109/ICASSP49660.2025.10890700

More information

Latest update

7/15/2025