A Real-Time Modulation Recognition System Based on Software-Defined Radio and Multi-Skip Residual Neural Network

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.

Keywords

• Cognitive radio
• modulation recognition
• software-defined radio
• multi-skip residual neural network
• deep learning
• non-collaborative communication