{"ID":2875362,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.02137","arxiv_id":"2509.02137","title":"High-Resolution Sensing in Communication-Centric ISAC: Deep Learning and Parametric Methods","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2509.02137","url_pdf":"https://arxiv.org/pdf/2509.02137v2","authors":"[\"Salmane Naoumi\",\"Ahmad Bazzi\",\"Roberto Bomfin\",\"Marwa Chafii\"]","published":"2025-09-02T09:41:01Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[]","has_code":false}