Low-Complexity Tensor-Based Monostatic Sensing for IRS-Assisted Communication Systems
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866917542553976832 |
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| author | Benício, Kenneth B. A. Sokal, Bruno de Almeida, André L. F. Fazal-E-Asim Makki, Behrooz Fodor, Gábor |
| author_facet | Benício, Kenneth B. A. Sokal, Bruno de Almeida, André L. F. Fazal-E-Asim Makki, Behrooz Fodor, Gábor |
| contents | This paper proposes a tensor-based parameter estimation algorithm for sensing in an intelligent reflecting surface-assisted system. We present a higher-order singular value decomposition-based solution that exploits the tensor structure of the received echo signal to jointly estimate the target's delay, Doppler, and angular information. Our tensor-based solution can estimate the parameters individually at low complexity, benefiting from parallel computation. Complexity analysis is carried out in comparison with a baseline scheme that does not exploit the intrinsic multilinear structure of the sensed signal. Simulation results show that our proposed tensor-based method can achieve the same performance as the reference method while drastically reducing the computational complexity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_29164 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Low-Complexity Tensor-Based Monostatic Sensing for IRS-Assisted Communication Systems Benício, Kenneth B. A. Sokal, Bruno de Almeida, André L. F. Fazal-E-Asim Makki, Behrooz Fodor, Gábor Signal Processing This paper proposes a tensor-based parameter estimation algorithm for sensing in an intelligent reflecting surface-assisted system. We present a higher-order singular value decomposition-based solution that exploits the tensor structure of the received echo signal to jointly estimate the target's delay, Doppler, and angular information. Our tensor-based solution can estimate the parameters individually at low complexity, benefiting from parallel computation. Complexity analysis is carried out in comparison with a baseline scheme that does not exploit the intrinsic multilinear structure of the sensed signal. Simulation results show that our proposed tensor-based method can achieve the same performance as the reference method while drastically reducing the computational complexity. |
| title | Low-Complexity Tensor-Based Monostatic Sensing for IRS-Assisted Communication Systems |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2605.29164 |