Automatic recognition of communication signal modulation based on neural network
Xiaolei Zhu, Yun Lin, Zheng Dou · 2016
In order to solve the problem of low modulation recognition rate of digital communication signals and the difficulty of selecting the appropriate decision threshold, the paper features a recognition method for communication signal modulation. The paper constructs characteristic parameters for recognizing signals in the cyclic frequency domain, and uses a 3-layer neural network as a classifier to identify the modulation mode. The experiment indicates that it can recognize 2FSK, 4FSK, 8FSK, BPSK, QPSK, MSK and 2ASK When signal to noise ratio (SNR) is higher than 0 dB, the recognition rate achieves 95%. The results suggest that recognition of communication signal modulation based on neural network is accurate and feasible.