High-Resolution Sensing in Communication-Centric ISAC: Deep Learning and Parametric Methods
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866914174322343936 |
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| author | Naoumi, Salmane Bazzi, Ahmad Bomfin, Roberto Chafii, Marwa |
| author_facet | Naoumi, Salmane Bazzi, Ahmad Bomfin, Roberto Chafii, Marwa |
| contents | This paper introduces two novel algorithms designed to address the challenge of super-resolution sensing parameter estimation in bistatic configurations within communication-centric integrated sensing and communication (ISAC) systems. Our approach leverages the estimated channel state information derived from reference symbols originally intended for communication to achieve super-resolution sensing parameter estimation. The first algorithm, IFFT-C2VNN, employs complex-valued convolutional neural networks to estimate the parameters of different targets, achieving significant reductions in computational complexity compared to traditional methods. The second algorithm, PARAMING, utilizes a parametric method that capitalizes on the knowledge of the system model, including the transmit and receive array geometries, to extract the sensing parameters accurately. Through a comprehensive performance analysis, we demonstrate the effectiveness and robustness of both algorithms across a range of signal-to-noise ratios, underscoring their applicability in realistic ISAC scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_02137 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | High-Resolution Sensing in Communication-Centric ISAC: Deep Learning and Parametric Methods Naoumi, Salmane Bazzi, Ahmad Bomfin, Roberto Chafii, Marwa Signal Processing This paper introduces two novel algorithms designed to address the challenge of super-resolution sensing parameter estimation in bistatic configurations within communication-centric integrated sensing and communication (ISAC) systems. Our approach leverages the estimated channel state information derived from reference symbols originally intended for communication to achieve super-resolution sensing parameter estimation. The first algorithm, IFFT-C2VNN, employs complex-valued convolutional neural networks to estimate the parameters of different targets, achieving significant reductions in computational complexity compared to traditional methods. The second algorithm, PARAMING, utilizes a parametric method that capitalizes on the knowledge of the system model, including the transmit and receive array geometries, to extract the sensing parameters accurately. Through a comprehensive performance analysis, we demonstrate the effectiveness and robustness of both algorithms across a range of signal-to-noise ratios, underscoring their applicability in realistic ISAC scenarios. |
| title | High-Resolution Sensing in Communication-Centric ISAC: Deep Learning and Parametric Methods |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2509.02137 |