Abstract
Ensemble learning is a useful frame algorithm which could improve the performance of weak learners by combining them. It is well known AdaBoost algorithm is one of these successful boosting algorithms. In this paper, we choose it to complete ensemble frame. we compare the performance of three machine learning algorithms including SVM, AdaBoost and decision tree stump based on communication signal modulation scheme to prove the effect of AdaBoost. The AdaBoost algorithm combines decision tree stump and iterates 500 rounds in the training phase. And the result reveals the performance of AdaBoost is proximal to that of SVM. At last, experiment to examine the features’ working principle on signals is done. The features can identify 4ASK correctly in all SNRs.
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Acknowledgments
This paper is funded by the National Natural Science Foundation of China (61301095), Nature Science Foundation of Heilongjiang Province of China (F201408).
This paper is also funded by the International Exchange Program of Harbin Engineering University for Innovation-oriented Talents Cultivation.
Meantime, all the authors declare that there is no conflict of interests regarding the publication of this article.
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Zhang, Z., Li, Y., Lin, Y. (2019). A Method for Modulation Recognition Based on Entropy Features and Ensemble Algorithm. In: Liang, Q., Mu, J., Jia, M., Wang, W., Feng, X., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2017. Lecture Notes in Electrical Engineering, vol 463. Springer, Singapore. https://doi.org/10.1007/978-981-10-6571-2_254
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DOI: https://doi.org/10.1007/978-981-10-6571-2_254
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