论文标题
I/Q调制分类的高容量复杂卷积神经网络
High-Capacity Complex Convolutional Neural Networks For I/Q Modulation Classification
论文作者
论文摘要
I/Q调制分类是一个唯一的模式识别问题，因为每个类的数据在质量上有所不同，通过信号与噪声比（SNR）量化，并且在复杂平面中具有结构。先前的工作表明，将这些样品视为复杂值的信号和深度学习框架内的计算复杂值的卷积，可显着提高比可比浅CNN体系结构的性能。在这项工作中，我们通过实现包含残差和/或密集连接的高容量架构来计算复杂值的卷积，而峰值分类精度在基准分类问题（RadiOML 2016.10A数据集）上，峰分类精度为92.4％。我们在所有网络中显示出具有复杂卷积的I/Q调制分类的统计显着改善。复杂性和推理速度分析表明，在每种情况下，具有相当数量的参数和可比速度的模型基本上优于架构，并且在每种情况下都超过10％。
I/Q modulation classification is a unique pattern recognition problem as the data for each class varies in quality, quantified by signal to noise ratio (SNR), and has structure in the complex-plane. Previous work shows treating these samples as complex-valued signals and computing complex-valued convolutions within deep learning frameworks significantly increases the performance over comparable shallow CNN architectures. In this work, we claim state of the art performance by enabling high-capacity architectures containing residual and/or dense connections to compute complex-valued convolutions, with peak classification accuracy of 92.4% on a benchmark classification problem, the RadioML 2016.10a dataset. We show statistically significant improvements in all networks with complex convolutions for I/Q modulation classification. Complexity and inference speed analyses show models with complex convolutions substantially outperform architectures with a comparable number of parameters and comparable speed by over 10% in each case.
