Real-Time Automatic Modulation Classification
Stephen Tridgell, David Boland, Philip H. W. Leong, Siddhartha Siddhartha · 2019
Deep learning based techniques have shown promising results over traditional hand-crafted methods for automatic modulation classification for radio signals. However, implementation of these deep learning models on specialized hardware can be challenging, as both latency and throughput performance are critical to achieving real-time response to over-the-air radio signals. In this work, we meet our targets by designing an optimized ternarized convolutional neural network that leverages the RF capabilities offered by the Xilinx ZCU111 RFSoC platform. The implemented networks achieve high-speed real-time performance with a classification latency of ≈8μs, and an operational throughput of 488k classifications per second. On the challenging open-source RadioML dataset, we achieve up to 81.1% accuracy, which is competitive to existing state-of-the-art software-only implementations.