Jamming Detection in MIMO-OFDM ISAC Systems Using Variational Autoencoders
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916420768497664 |
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| author | Arcangeloni, Luca Testi, Enrico Giorgetti, Andrea |
| author_facet | Arcangeloni, Luca Testi, Enrico Giorgetti, Andrea |
| contents | This paper introduces a novel unsupervised jamming detection framework designed specifically for monostatic multiple-input multiple-output (MIMO)-orthogonal frequency-division multiplexing (OFDM) radar systems. The framework leverages echo signals captured at the base station (BS) and employs the latent data representation learning capability of variational autoencoders (VAEs). The VAE-based detector is trained on echo signals received from a real target in the absence of jamming, enabling it to learn an optimal latent representation of normal network operation. During testing, in the presence of a jammer, the detector identifies anomalous signals by their inability to conform to the learned latent space. We assess the performance of the proposed method in a typical integrated sensing and communication (ISAC)-enabled 5G wireless network, even comparing it with a conventional autoencoder. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_01632 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Jamming Detection in MIMO-OFDM ISAC Systems Using Variational Autoencoders Arcangeloni, Luca Testi, Enrico Giorgetti, Andrea Signal Processing This paper introduces a novel unsupervised jamming detection framework designed specifically for monostatic multiple-input multiple-output (MIMO)-orthogonal frequency-division multiplexing (OFDM) radar systems. The framework leverages echo signals captured at the base station (BS) and employs the latent data representation learning capability of variational autoencoders (VAEs). The VAE-based detector is trained on echo signals received from a real target in the absence of jamming, enabling it to learn an optimal latent representation of normal network operation. During testing, in the presence of a jammer, the detector identifies anomalous signals by their inability to conform to the learned latent space. We assess the performance of the proposed method in a typical integrated sensing and communication (ISAC)-enabled 5G wireless network, even comparing it with a conventional autoencoder. |
| title | Jamming Detection in MIMO-OFDM ISAC Systems Using Variational Autoencoders |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2410.01632 |