使用变分自编码器的 MIMO-OFDM ISAC 系统干扰检测
Jamming Detection in MIMO-OFDM ISAC Systems Using Variational Autoencoders
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中文总结 AI 辅助
提出一种面向单基地 MIMO-OFDM 雷达的无监督 VAE 干扰检测框架，通过学习无干扰回波的潜在表示识别异常，并在 ISAC 5G 场景中与传统自编码器比较。
中文摘要 AI 辅助
本文介绍了一种新颖的无监督干扰检测框架，该框架专为单基地多输入多输出（MIMO）正交频分复用（OFDM）雷达系统设计。该框架利用在基站（BS）捕获的回波信号，并采用变分自编码器（VAE）的潜在数据表示学习能力。基于 VAE 的检测器在无干扰条件下利用从真实目标接收的回波信号进行训练，使其能够学习正常网络运行的最优潜在表示。在测试阶段，当存在干扰机时，检测器通过异常信号无法符合所学潜在空间来识别异常信号。我们在典型的支持集成感知与通信（ISAC）的 5G 无线网络中评估了所提方法的性能，甚至将其与传统自编码器进行了比较。
英文摘要
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.
