Robust and Efficient Modulation Recognition Based on Local Sequential IQ Features
Robust and Efficient Modulation Recognition Based on Local Sequential IQ Features
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DOI：
10.1109/infocom.2019.8737397
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发表时间：
2019-04
期刊：
影响因子：
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通讯作者：
Wei Xiong;Petko Bogdanov;M. Zheleva
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文献类型：
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作者：
Wei Xiong;Petko Bogdanov;M. Zheleva
调制识别在频谱强制、资源分配、隐私和安全等新兴频谱应用中起着关键作用。虽然对频谱共享的实际进展至关重要，但迄今为止，调制识别的研究是在不切实际的假设下进行的：(i)必须单独扫描发射机的带宽，（ii）必须有技术的先验知识，（iii）发射机必须值得信赖。在现实中，这些假设并不容易满足，因为发射机的带宽可能只是间歇性地、部分地或与其他发射机一起被扫描，并且短时间扫描或恶意活动可能会引入调制混淆。本文弥合了现实世界频谱感知与在简化假设下设计的调制识别方法之间的差距。除了从原始IQ数据中提取的全局统计数据外，我们建议使用局部特征，这些特征共同使调制识别的鲁棒框架优于最先进的基线。具体来说，我们利用基于捕获底层数据非线性的Fisher核框架提取的连续IQ样本的局部模式的判别能力。利用这些领域信息特征，我们采用轻量级线性支持向量机分类进行调制检测。我们的框架对噪声、部分发射机扫描和数据偏差具有鲁棒性，而无需利用底层发射机技术的先验知识。我们的方法的识别精度始终优于基线在模拟和现实世界的痕迹。在USRP测试平台上，我们展示了高达98%的准确率，比文献中部分扫描的几个同行提高了30%。
Modulation recognition plays a key role in emerging spectrum applications including spectrum enforcement, resource allocation, privacy and security. While critical for the practical progress of spectrum sharing, modulation recognition has so far been investigated under unrealistic assumptions: (i)a transmitter’s bandwidth must be scanned alone and in full, (ii) prior knowledge of the technology must be available and (iii) a transmitter must be trustworthy. In reality these assumptions cannot be readily met, as a transmitter’s bandwidth may only be scanned intermittently, partially, or alongside other transmitters, and modulation obfuscation may be introduced by short-lived scans or malicious activity.This paper bridges the gap between real-world spectrum sensing and the growing body of methods for modulation recognition designed under simplifying assumptions. We propose to use local features, besides global statistics, extracted from raw IQ data, which collectively enable a robust framework for modulation recognition that outperforms baselines from the state-of-the-art. Specifically, we exploit the discriminative power of local patterns from consecutive IQ samples extracted based on a Fisher Kernel framework that captures non-linearity in the underlying data. With these domain-informed features, we employ lightweight linear support vector machine classification for modulation detection. Our framework is robust to noise, partial transmitter scans and data biases without utilizing prior knowledge of the underlying transmitter technology. The recognition accuracy of our approach consistently outperforms baselines in both simulated and real-world traces. We demonstrate up to a 98% accuracy and a 30% improvement over several counterparts from the literature with partial scans in a USRP testbed.