Real-Time OFDM Signal Modulation Classification Based on Deep Learning and Software-Defined Radio
Limin Zhang, Lin Chong, Wenjun Yan, Qing Ling, Yu Wang
Abstract
This letter presents our initial results for real-time orthogonal frequency division multiplexing (OFDM) signal modulation classification based on deep learning and software-defined radio. We generate a modulation classification dataset under a dynamic fading channel, including 6 different OFDM modulation signals, and propose a novel neural network with triple-skip residual stack (TRS) as the basic unit. Each TRS has multiple residual units with gradually increasing convolutional layers. Finally, a near real-time classification system is designed based on the proposed network and GNU Radio. The processing delay incurred by the detection and modulation classification is about 4 ms. It is worth mentioning that the classification accuracy can reach 64% at -10 dB, which is about 7% higher than ResNet and VGG.