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arXiv · 2410.04176

Unsupervised Learning for Gain-Phase Impairment Calibration in ISAC Systems

José Miguel Mateos-Ramos·Christian Häger·Musa Furkan Keskin·Luc Le Magoarou·Henk Wymeersch
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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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José Miguel Mateos-Ramos, Christian Häger, Musa Furkan Keskin, Luc Le Magoarou, Henk Wymeersch. 2024-10-05. Unsupervised Learning for Gain-Phase Impairment Calibration in ISAC Systems. https://arxiv.org/abs/2410.04176

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