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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2306.12309 |
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| _version_ | 1866910267565146112 |
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| author | Benício, Kenneth B. A. de Almeida, André L. F. Sokal, Bruno Fazal-E-Asim Makki, Behrooz Fodor, Gabor |
| author_facet | Benício, Kenneth B. A. de Almeida, André L. F. Sokal, Bruno Fazal-E-Asim Makki, Behrooz Fodor, Gabor |
| contents | This paper proposes a tensor-based parametric modeling and estimation framework in multiple-input multiple-output (MIMO) systems assisted by intelligent reflecting surfaces (IRSs). We present two algorithms that exploit the tensor structure of the received pilot signal to estimate the concatenated channel. The first one is an iterative solution based on the alternating least squares algorithm. In contrast, the second method provides closed-form estimates of the involved parameters using the high order single value decomposition. Our numerical results show that our proposed tensor-based methods provide improved performance compared to competing state-of-the-art channel estimation schemes, thanks to the exploitation of the algebraic tensor structure of the combined channel without additional computational complexity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_12309 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Tensor-based modeling/estimation of static channels in IRS-assisted MIMO systems Benício, Kenneth B. A. de Almeida, André L. F. Sokal, Bruno Fazal-E-Asim Makki, Behrooz Fodor, Gabor Signal Processing This paper proposes a tensor-based parametric modeling and estimation framework in multiple-input multiple-output (MIMO) systems assisted by intelligent reflecting surfaces (IRSs). We present two algorithms that exploit the tensor structure of the received pilot signal to estimate the concatenated channel. The first one is an iterative solution based on the alternating least squares algorithm. In contrast, the second method provides closed-form estimates of the involved parameters using the high order single value decomposition. Our numerical results show that our proposed tensor-based methods provide improved performance compared to competing state-of-the-art channel estimation schemes, thanks to the exploitation of the algebraic tensor structure of the combined channel without additional computational complexity. |
| title | Tensor-based modeling/estimation of static channels in IRS-assisted MIMO systems |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2306.12309 |