Abstract
A novel approach (HGO-EAC) for hybrid genetic optimization (GO) with elite ant colony (EAC) is proposed for the automatic modulation recognition of communication signals, through which we improve the basic ant colony algorithms by referencing elite strategy and present a new fusion strategy for genetic optimization and elite ant colony. This approach is used to train the neural networks as the classifier for modulation. Simulation results indicate good performance on an additive white Gaussian noise (AWGN) channel, with recognition rate reaching to 70% especially for CW even at signal-to-noise ratios as low as 5 dB. This approach can achieve a high recognition rate for the typical modulations such as CW, 4ASK, 4FSK, BPSK, and QAM16. Test result shows that it has better performance than BP algorithm and basic ant colony algorithms by achieving faster training and stronger robustness.
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