Automatic Modulation Classification based on Wiener filter preprocessing and Cumulants

Wiener filter is the best linear filter under the minimum mean square error criterion. It can effectively eliminate the influence of noise, which may deal with the problem of Automatic modulation classification (AMC) performing poorly at low Signal-to-Noise Ratio (SNR). This paper explores how to use Wiener filter to improve the classification performance in the AMC, combining cumulant and Back Propagation (BP) neural network to classify Six kinds of digital modulation signals. The simulation results show that the AMC with Wiener filter preprocessing has a higher classification rate under low SNR (-10dB).