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 paper, a real-time modulation recognition system based on deep learning and softwaredefined 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 multiscale 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.

A Fault Diagnosis Method for Ultrasonic Flow Meters Based on KPCA-CLSSA-SVM

“…SVM is a classification algorithm that utilizes supervised learning to categorize data into two classes, with the aim of minimizing the structural risk. Its decision boundary is the maximum margin hyperplane obtained by solving the learning sample [19].…”

“…of the eight channels[12][13][14][15][16][17][18][19] Acoustic velocity in each of the eight channels 20Average acoustic velocity in all eight channels 21-36 Amplification at either ends of each of the eight channels at both ends of the four channels 36-43 Amplification at both ends of the four channels 44-51Time for transit at both ends of the four channels Velocity along each of the four channels 8-11Acoustic velocity in the four channels[12][13][14][15][16][17][18][19] Signal intensity at both ends of the four channels 20-27Quality of signal at both ends of the four channels 28-35 Amplification at both ends of the four channels 36-43Time for transit at both ends of the four channelsDue to the minor variation between flow meters C and D, with C having only one additional sample…”

“…Velocity along each of the four channels 9Mean flow velocity across all four channels 10-13 Acoustic velocity in four channels 14Average acoustic velocity in all four channels[15][16][17][18][19][20][21][22] Signal intensity at each end of the four channels[23][24][25][26] Flow disturbance in each of the four channels 27Meter performance[28][29][30][31][32][33][34][35] Quality of signal at both ends of the four channels 36-43 Amplification at both ends of the four channels 44-51Time for transit at both ends of the four channels…”

IRLNet: A Short-Time and Robust Architecture for Automatic Modulation Recognition

“…As an example, in iteration t, the updating process for weight w l to be optimized is as follows [37]:…”

Undergraduate Curriculum to Teach and Provide Research Skills on Hardware Design for SDR Applications in FPGA Technology

“…This is in correspondence with the LOs 1 and 2. TA 2 Project development labs (5)(6)(7)(8)(9)(10)(11) to implement each specific task organized as workshops along 7 sessions, representing 43.75 % of the course. This is in correspondence with the LO 2.…”