| Citation: | WANG P，LIU C H，ZHANG D N. Automatic modulation recognition method based on improved weight AdaBoost.M2 algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（8）：2089-2098 （in Chinese） doi: 10.13700/j.bh.1001-5965.2021.0577
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A signal modulation recognition method is proposed based on AdaBoost.M2 algorithm to address difficult identification of signals from the same family of modulation types and the poor generalization of the deep learning model. An improved method of sample weight is proposed to solve the problem that the learning rate of the weak learning algorithm is difficult to adapt to the weighted sample data in the case of large samples. The improved sample weight ensures that the order of magnitude of the training sample data remains unchanged after weighting, so that the algorithm pays more attention to the difficult classification samples, improving the comprehensive performance of the weak classifier. In addition, in view of the difficult classification problem caused by the statistical characteristics of some samples easily submerged in noise, a random feature clipping method is proposed to avoid much attention given to abnormal features. This method reduces the negative impact of extremely difficult classification samples on the performance of AdaBoost.M2 algorithm, improving the generalization ability of the algorithm. Experimental verification with low signal-to-noise ratio data is conducted. Finally, given the fact that the signals of the same family of modulation types are difficult to classify, the signals of the same family are selected for model training and testing. Results show that the improved AdaBoost.M2 algorithm increases the test set accuracy of PSK family and QAM family by 8.5 and 11.25% respectively compared with the single CLDNN algorithm, and by 8.25 and 6.5% respectively compared with the classical AdaBoost.M2 algorithm.
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