基于极化MKAN的射频指纹可解释特征学习
Interpretable Feature Learning for RF Fingerprinting via Polar MKANs
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中文总结 AI 辅助
针对现有射频指纹深度学习模型不透明的问题，提出Polar MKAN模型，在合成基准上实现更高的DCI解缠率，同时评估了真实数据检测精度权衡及盲CFO补偿敏感性。
中文摘要 AI 辅助
射频（RF）指纹通过硬件引起的同相/正交（I/Q）损伤对无线设备进行身份验证，现有深度学习特征提取器虽准确但不透明，限制了其在安全关键场景的应用。本文提出极化单调柯尔莫哥洛夫-阿诺尔德网络（Polar Monotonic Kolmogorov-Arnold Networks，Polar MKAN），这是一种针对极化输入的块划分单调编码器，每个潜在维度仅依赖幅度或相位，通过构造实现信道分离和单调响应。在合成增益与载波频率偏移（CFO）基准上，Polar MKAN的DCI解缠率达57.2%，而未划分基线的解缠率最高仅为12.9%。本文还评估了其在真实数据上的检测精度权衡以及对盲CFO补偿的敏感性。
英文摘要
Radio frequency (RF) fingerprinting authenticates wireless devices from hardware-induced I/Q impairments, typically with deep learning feature extractors that are accurate but opaque, limiting their use in security critical settings. We propose Polar Monotonic Kolmogorov-Arnold Networks (Polar MKAN), a block partitioned monotonic encoder on polar inputs in which each latent dimension depends exclusively on magnitude or phase, yielding channel separation and monotone responses by construction. On a synthetic gain and carrier frequency offset (CFO) benchmark, Polar MKAN reaches 57.2 percent DCI Disentanglement versus at most 12.9 percent for unpartitioned baselines. We further evaluate the detection accuracy trade off on real data and the sensitivity to blind CFO compensation.
发表机构
- Jožef Stefan Institute（约热夫·斯泰凡研究所）
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