Modulation Recognition Scheme Using Mixed Model

On automatic modulation there are two approaches, decision-theoretic and statistical pattern. An automatic modulation recognition system to recognize four digital signal classes as: MASK, MFSK, MPSK, MQAM is proposed in this paper, which using decision-theoretic based feature set addition to statistical pattern based feature set with VLBP(variable learning rate back-propagation)BP neural network and Bayesian normalized BP neural network. Performance is generally good when Signal to Noise Ratios (SNR) in 0-10dB, simulations show the results even larger than 95%, that confirm the robustness and practicality of this recognition method.