论文标题
在深度学习中使用复杂卷积的调制模式检测
Modulation Pattern Detection Using Complex Convolutions in Deep Learning
论文作者
论文摘要
用于电信的收发器传输并接收特定的调制模式，这些模式表示为复数序列。对调制模式进行分类是具有挑战性的，因为噪声和通道障碍以复杂的方式影响信号，因此接收的信号与传输信号几乎没有相似之处。尽管深度学习方法对此问题空间中的统计方法表现出了巨大的希望，但深度学习框架继续落后，以支持复杂值的数据。为了解决这一差距，我们研究了一系列卷积神经网络体系结构中复杂卷积的实施和使用。在建筑中，通过其复杂的概括替换数据结构和卷积操作可以提高性能，具有统计学意义，在接受低SNR信号培训后，识别具有高SNR的复杂值信号的调制模式。这表明复杂价值的卷积使网络能够学习更有意义的表示。我们通过比较每个实验中学到的特征，通过可视化输入，从而对每个网络进行单热调制模式分类来研究这一假设。
Transceivers used for telecommunications transmit and receive specific modulation patterns that are represented as sequences of complex numbers. Classifying modulation patterns is challenging because noise and channel impairments affect the signals in complicated ways such that the received signal bears little resemblance to the transmitted signal. Although deep learning approaches have shown great promise over statistical methods in this problem space, deep learning frameworks continue to lag in support for complex-valued data. To address this gap, we study the implementation and use of complex convolutions in a series of convolutional neural network architectures. Replacement of data structure and convolution operations by their complex generalization in an architecture improves performance, with statistical significance, at recognizing modulation patterns in complex-valued signals with high SNR after being trained on low SNR signals. This suggests complex-valued convolutions enables networks to learn more meaningful representations. We investigate this hypothesis by comparing the features learned in each experiment by visualizing the inputs that results in one-hot modulation pattern classification for each network.
