- author = "Sai Huang and Yizhou Jiang and Xiaoqi Qin and Yue Gao and Zhiyong Feng and Ping Zhang",
- journal = "IEEE Access",
- title = "Automatic Modulation Classification of Overlapped Sources Using Multi-Gene Genetic Programming With Structural Risk Minimization Principle",
- year = "2018",
- volume = "6",
- pages = "48827--48839",
- abstract = "As the spectrum environment becomes increasingly crowded and complicated, primary users may be interfered by secondary users and other illegal users. Automatic modulation classification (AMC) of a single source cannot recognize the overlapped sources. Consequently, the AMC of overlapped sources attracts much attention. In this paper, we propose a genetic programming-based modulation classification method for overlapped sources (GPOS). The proposed GPOS consists of two stages, the training stage, and the classification stage. In the training stage, multi-gene genetic programming (MGP)-based feature engineering transforms sample estimates of cumulants into highly discriminative MGP-features iteratively, until optimal MGP-features (OMGP-features) are obtained, where the structural risk minimization principle (SRMP) is employed to evaluate the classification performance of MGP-features and train the classifier. Moreover, a self-adaptive genetic operation is designed to accelerate the feature engineering process. In the classification stage, the classification decision is made by the trained classifier using the OMGP-features. Through simulation results, we demonstrate that the proposed scheme outperforms other existing methods in terms of classification performance and robustness in case of varying power ratios and fading channel.",
- keywords = "genetic algorithms, genetic programming",
-
DOI = "
doi:10.1109/ACCESS.2018.2868224",
- ISSN = "2169-3536",
-
notes = "Key Laboratory of Universal Wireless Communications,
Ministry of Education, Beijing University of Posts and
Telecommunications, Beijing, China
Also known as \cite{8452898}",
