Modulation Pattern Detection Using Complex Convolutions in Deep Learning
This work addresses a domain-specific problem for telecommunications by providing an incremental improvement in deep learning support for complex-valued data.
The paper tackles the challenge of classifying modulation patterns in telecommunications signals affected by noise and channel impairments by implementing complex convolutions in convolutional neural networks, resulting in statistically significant performance improvements when trained on low SNR signals and tested on high SNR signals.
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.