Research and implementation of modulation recognition based on cascaded feature fusion

Abstract

Aiming at the problems of weak robustness of single feature and limited recognition range in modulation recognition, this paper proposes a modulation recognition algorithm based on cascaded feature fusion and multi-classifier combination, in which time-frequency map and instantaneous amplitude spectral density features are extracted from low intermediate frequency (IF) signal, constellation map and high-order cumulant features are extracted from zero IF signal, and a three-level recognition algorithm is designed through the decision fusion of decision tree, convolutional neural network, and support vector machine. In order to verify the performance of the algorithm, a modulation recognition system is designed and built based on USRP2974. The low IF and zero IF modulation signals are received through two channels, and the RF signals are received and processed in real time with the help of the built-in CPU of the receiver. The recognition of 13 kinds of analog modulation and digital modulation signals is realized. Under the condition of wireless reception, the recognition rate of this system is more than 93% at 5 to 10 dB.

Research on Communication Signal Modulation Recognition Based on a CCLDNN

“…Many scholars have conducted a lot of research on this technology. After decades of development, radio signal modulation technologies are mainly divided into two categories: those that use the decision theory modulation method based on maximum likelihood (LB) [8][9][10] and those that use the pattern recognition method based on feature extraction (FB) [11][12][13].…”