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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.09393 |
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| _version_ | 1866915195992932352 |
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| author | Wu, Qi Sui, Zeping Ngo, Hien Quoc Wan, Qun Matthaiou, Michail |
| author_facet | Wu, Qi Sui, Zeping Ngo, Hien Quoc Wan, Qun Matthaiou, Michail |
| contents | In this paper, we investigate the joint generalized channel estimation and device identification problem in Internet of Things (IoT) networks {under multipath propagation}. To fully utilize the received signal, we decompose the generalized channel into three components: transmitter hardware characteristics, path gains, and angles of arrival. By modelling the received signals as parallel factor (PARAFAC) tensors, we develop alternating least squares (ALS)-based algorithms to simultaneously estimate the generalized channels and identify the transmitters. Simulation results show that the proposed scheme outperforms {both Khatri-Rao Factorization (KRF) and the conventional least squares (LS) method} in terms of channel estimation accuracy and achieves performance close to the derived Cramer-Rao lower bound. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_09393 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generalized Tensor-Aided Channel Estimation for Hardware Impaired Device Identification Wu, Qi Sui, Zeping Ngo, Hien Quoc Wan, Qun Matthaiou, Michail Signal Processing In this paper, we investigate the joint generalized channel estimation and device identification problem in Internet of Things (IoT) networks {under multipath propagation}. To fully utilize the received signal, we decompose the generalized channel into three components: transmitter hardware characteristics, path gains, and angles of arrival. By modelling the received signals as parallel factor (PARAFAC) tensors, we develop alternating least squares (ALS)-based algorithms to simultaneously estimate the generalized channels and identify the transmitters. Simulation results show that the proposed scheme outperforms {both Khatri-Rao Factorization (KRF) and the conventional least squares (LS) method} in terms of channel estimation accuracy and achieves performance close to the derived Cramer-Rao lower bound. |
| title | Generalized Tensor-Aided Channel Estimation for Hardware Impaired Device Identification |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2503.09393 |