Sensing With Random Signals
Fuente:
arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866913195364450304 |
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| author | Lu, Shihang Liu, Fan Dong, Fuwang Xiong, Yifeng Xu, Jie Liu, Ya-Feng |
| author_facet | Lu, Shihang Liu, Fan Dong, Fuwang Xiong, Yifeng Xu, Jie Liu, Ya-Feng |
| contents | Radar systems typically employ well-designed deterministic signals for target sensing. In contrast to that, integrated sensing and communications (ISAC) systems have to use random signals to convey useful information, potentially causing sensing performance degradation. In this paper, we define a new sensing performance metric, namely, ergodic linear minimum mean square error (ELMMSE), accounting for the randomness of ISAC signals. Then, we investigate a data-dependent precoding scheme to minimize the ELMMSE, which attains the optimized sensing performance at the price of high computational complexity. To reduce the complexity, we present an alternative data-independent precoding scheme and propose a stochastic gradient projection (SGP) algorithm for ELMMSE minimization, which can be trained offline by locally generated signal samples. Finally, we demonstrate the superiority of the proposed methods by simulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_02375 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Sensing With Random Signals Lu, Shihang Liu, Fan Dong, Fuwang Xiong, Yifeng Xu, Jie Liu, Ya-Feng Signal Processing Radar systems typically employ well-designed deterministic signals for target sensing. In contrast to that, integrated sensing and communications (ISAC) systems have to use random signals to convey useful information, potentially causing sensing performance degradation. In this paper, we define a new sensing performance metric, namely, ergodic linear minimum mean square error (ELMMSE), accounting for the randomness of ISAC signals. Then, we investigate a data-dependent precoding scheme to minimize the ELMMSE, which attains the optimized sensing performance at the price of high computational complexity. To reduce the complexity, we present an alternative data-independent precoding scheme and propose a stochastic gradient projection (SGP) algorithm for ELMMSE minimization, which can be trained offline by locally generated signal samples. Finally, we demonstrate the superiority of the proposed methods by simulations. |
| title | Sensing With Random Signals |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2309.02375 |