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.
@article{arxiv.2410.01632,
title = {Jamming Detection in MIMO-OFDM ISAC Systems Using Variational Autoencoders},
author = {Luca Arcangeloni and Enrico Testi and Andrea Giorgetti},
journal= {arXiv preprint arXiv:2410.01632},
year = {2024}
}