Unsupervised Learning for Gain-Phase Impairment Calibration in ISAC Systems
Автор: Henk Wymeersch
Загружено: 2025-03-24
Просмотров: 280
Video presentation of the following paper:
J. M. Mateos-Ramos, C. Häger, M. F. Keskin, L. Le Magoarou and H. Wymeersch, "Unsupervised Learning for Gain-Phase Impairment Calibration in ISAC Systems," ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Hyderabad, India, 2025.
Paper link: https://ieeexplore.ieee.org/document/...
Paper code: https://github.com/josemateosramos/UL...
Paper 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.
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