Unsupervised Learning for Gain-Phase Impairment Calibration in ISAC Systems

José Miguel Mateos-Ramos; Christian Häger; Musa Furkan Keskin; Luc Le Magoarou; Henk Wymeersch

Unsupervised Learning for Gain-Phase Impairment Calibration in ISAC Systems

Signal Processing 2024-10-08 v1

Authors: 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.

Keywords

integrated sensing and communication gaussian processes global navigation satellite system positioning

Cite

@article{arxiv.2410.04176,
  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},
  journal= {arXiv preprint arXiv:2410.04176},
  year   = {2024}
}

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5 pages, 3 figures, submitted to ICASSP

Unsupervised Learning for Gain-Phase Impairment Calibration in ISAC Systems

Abstract

Keywords

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