Semi-Supervised End-to-End Learning for Integrated Sensing and Communications
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866916137184264192 |
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| author | Mateos-Ramos, José Miguel Chatelier, Baptiste Häger, Christian Keskin, Musa Furkan Magoarou, Luc Le Wymeersch, Henk |
| author_facet | Mateos-Ramos, José Miguel Chatelier, Baptiste Häger, Christian Keskin, Musa Furkan Magoarou, Luc Le Wymeersch, Henk |
| contents | Integrated sensing and communications (ISAC) is envisioned as one of the key enablers of next-generation wireless systems, offering improved hardware, spectral, and energy efficiencies. In this paper, we consider an ISAC transceiver with an impaired uniform linear array that performs single-target detection and position estimation, and multiple-input single-output communications. A differentiable model-based learning approach is considered, which optimizes both the transmitter and the sensing receiver in an end-to-end manner. An unsupervised loss function that enables impairment compensation without the need for labeled data is proposed. Semi-supervised learning strategies are also proposed, which use a combination of small amounts of labeled data and unlabeled data. Our results show that semi-supervised learning can achieve similar performance to supervised learning with 98.8% less required labeled data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_09940 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Semi-Supervised End-to-End Learning for Integrated Sensing and Communications Mateos-Ramos, José Miguel Chatelier, Baptiste Häger, Christian Keskin, Musa Furkan Magoarou, Luc Le Wymeersch, Henk Signal Processing Integrated sensing and communications (ISAC) is envisioned as one of the key enablers of next-generation wireless systems, offering improved hardware, spectral, and energy efficiencies. In this paper, we consider an ISAC transceiver with an impaired uniform linear array that performs single-target detection and position estimation, and multiple-input single-output communications. A differentiable model-based learning approach is considered, which optimizes both the transmitter and the sensing receiver in an end-to-end manner. An unsupervised loss function that enables impairment compensation without the need for labeled data is proposed. Semi-supervised learning strategies are also proposed, which use a combination of small amounts of labeled data and unlabeled data. Our results show that semi-supervised learning can achieve similar performance to supervised learning with 98.8% less required labeled data. |
| title | Semi-Supervised End-to-End Learning for Integrated Sensing and Communications |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2310.09940 |