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.

中文翻译：

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基于深度学习和软件定义无线电的实时 OFDM 信号调制分类


这封信介绍了我们基于深度学习和软件定义无线电的实时正交频分复用 (OFDM) 信号调制分类的初步结果。我们生成了动态衰落信道下的调制分类数据集，包括 6 个不同的 OFDM 调制信号，并提出了一种以三跳残差堆栈 (TRS) 作为基本单元的新型神经网络。每个TRS都有多个残差单元，并且卷积层逐渐增加。最后，基于所提出的网络和 GNU Radio 设计了一个近实时分类系统。检测和调制分类产生的处理延迟约为4ms。值得一提的是，在-10 dB时分类精度可以达到64%，比ResNet和VGG高约7%。