Research on modulation recognition with ensemble learning

EURASIP Journal on Wireless Communications and Networking

Yanan Guan¹  Tong Liu¹  Yun Lin¹ 

[1] College of Information and Communication Engineering, Harbin Engineering University;

关键词: Modulation scheme recognition;    Ensemble learning;    Boosting;    Information entropy;    Decision tree;   

DOI  :  10.1186/s13638-017-0949-5

来源: DOAJ

【 摘 要 】

Abstract Modulation scheme recognition occupies a crucial position in the civil and military application. In this paper, we present boosting algorithm as an ensemble frame to achieve a higher accuracy than a single classifier. To evaluate the effect of boosting algorithm, eight common communication signals are yet to be identified. And five kinds of entropy are extracted as the training vector. And then, AdaBoost algorithm based on decision tree is utilized to confirm the idea of boosting algorithm. The results illustrate AdaBoost is always a superior classifier, while, as a weak estimator, decision tree is barely satisfactory. In addition, the performance of three diverse boosting members is compared by experiments. Gradient boosting has better behavior than AdaBoost, and xgboost creates optimal cost performance especially.

【 授权许可】

Unknown