Abstract
Communication signal modulation recognition has important research value in the fields of cognitive electronic warfare, communication countermeasures and non-collaborative communication. However, traditional signal recognition methods usually suffer some drawbacks, such as low accuracy, poor scalability, dependence on expert characteristics, and poor applicability to real-world environments. Therefore, in this article, a real-time modulation recognition system based on deep learning and software-defined radio (SDR) technology is designed. In the first step, an improved residual neural network is designed. A multi-skip residual stack (MRS) is designed to preserve more initial residuals information on the multi-scale feature map, which can simultaneously learn the deep and shallow characteristics of the signal. Then, a multi-skip residual network is designed with the MRS as the basic unit, and the network is trained using an adaptive moment estimation optimization algorithm. Finally, the network is tested on public datasets. In the second step, the network is embedded in a SDR platform composed of a GNU Radio and an universal software radio peripheral to realize real-time recognition of the input signal. Experiments show that this system has strong real-time capabilities, high recognition accuracy and considerable robustness.
Highlights
In order to achieve communication reconnaissance and electromagnetic dominance, dynamic spectrum monitoring and communication countermeasures as well as interference and anti-jamming technologies are needed
2) A real-time modulation recognition system based on software-defined radio (SDR) and MRNN is proposed
Because multi-skip residual stack (MRS) learns both deep and shallow features of signals on multi-scale feature maps at the same time, and one MRS can be fitted as a function with 1 ~ 4 convolutional layers, MRNN is more capable of extracting features than residual neural network (ResNet): the recognition accuracy reaches 50% when signal to noise ratio (SNR) = 0 dB, and the performance increases from 74% to 96% when SNR = 14 dB
Summary
In order to achieve communication reconnaissance and electromagnetic dominance, dynamic spectrum monitoring and communication countermeasures as well as interference and anti-jamming technologies are needed. In [28], a blind demodulation system (BDS) based on AlexNet and SDR platform is designed, which can identify 3 modulations, and the real-time performance of the system is tested in a realworld environment To solve these problems, this work combines the improved deep ResNet with SDR, and proposes a real-time modulation recognition system: 1) A multi-skip residual neural network (MRNN) is proposed. A real-time recognition module is designed based on GNU Radio's custom block This module can load a trained MRNN model to recognize the signals received by USRP in real-time.
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