Unsupervised Learning for Gain-Phase Impairment Calibration in ISAC Systems
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916424623063040 |
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| author | Mateos-Ramos, José Miguel Häger, Christian Keskin, Musa Furkan Magoarou, Luc Le Wymeersch, Henk |
| author_facet | Mateos-Ramos, José Miguel Häger, Christian Keskin, Musa Furkan Magoarou, Luc Le Wymeersch, Henk |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_04176 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unsupervised Learning for Gain-Phase Impairment Calibration in ISAC Systems Mateos-Ramos, José Miguel Häger, Christian Keskin, Musa Furkan Magoarou, Luc Le Wymeersch, Henk Signal Processing 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. |
| title | Unsupervised Learning for Gain-Phase Impairment Calibration in ISAC Systems |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2410.04176 |