Bridging the Gap via Data-Aided Sensing: Can Bistatic ISAC Converge to Genie Performance?
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909599017205760 |
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| author | Keskin, Musa Furkan Mura, Silvia Mizmizi, Marouan Tagliaferri, Dario Wymeersch, Henk |
| author_facet | Keskin, Musa Furkan Mura, Silvia Mizmizi, Marouan Tagliaferri, Dario Wymeersch, Henk |
| contents | We investigate data-aided iterative sensing in bistatic OFDM ISAC systems, focusing on scenarios with co-located sensing and communication receivers. To enhance target detection beyond pilot-only sensing methods, we propose a multi-stage bistatic OFDM receiver, performing iterative sensing and data demodulation to progressively refine ISAC channel and data estimates. Simulation results demonstrate that the proposed data-aided scheme significantly outperforms pilot-only benchmarks, particularly in multi-target scenarios, substantially narrowing the performance gap compared to a genie-aided system with perfect data knowledge. Moreover, the proposed approach considerably expands the bistatic ISAC trade-off region, closely approaching the probability of detection-achievable rate boundary established by its genie-aided counterpart. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_01280 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bridging the Gap via Data-Aided Sensing: Can Bistatic ISAC Converge to Genie Performance? Keskin, Musa Furkan Mura, Silvia Mizmizi, Marouan Tagliaferri, Dario Wymeersch, Henk Signal Processing We investigate data-aided iterative sensing in bistatic OFDM ISAC systems, focusing on scenarios with co-located sensing and communication receivers. To enhance target detection beyond pilot-only sensing methods, we propose a multi-stage bistatic OFDM receiver, performing iterative sensing and data demodulation to progressively refine ISAC channel and data estimates. Simulation results demonstrate that the proposed data-aided scheme significantly outperforms pilot-only benchmarks, particularly in multi-target scenarios, substantially narrowing the performance gap compared to a genie-aided system with perfect data knowledge. Moreover, the proposed approach considerably expands the bistatic ISAC trade-off region, closely approaching the probability of detection-achievable rate boundary established by its genie-aided counterpart. |
| title | Bridging the Gap via Data-Aided Sensing: Can Bistatic ISAC Converge to Genie Performance? |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2505.01280 |