Automatic Modulation Classification Using Combination of Genetic Programming and KNN

By Muhammad Waqar Aslam, Zhechen Zhu, Asoke Kumar Nandi. IEEE Transactions on Wireless Communications, 2012

323 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence, Plant Science

View Paper: https://www.semanticscholar.org/paper/c847622cbe5c0cb955f96a784d65da9fd27db037

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Yong Ren
2. JingJing Wang
3. Lajos Hanzo
4. Chunxiao Jiang
5. Haijun Zhang
6. Kwang-Cheng Chen

In Artificial Intelligence

1. Yong Ren
2. JingJing Wang
3. Lajos Hanzo
4. Chunxiao Jiang
5. Haijun Zhang
6. Kwang-Cheng Chen

Citations in the corpus

• Modulation Classification Based on Signal Constellation Diagrams and Deep Learning (2019, methodology): “2) SVM-7: An ML-based algorithm using the SVM with seven features, including three fourth-order cumulants C40, C41, and C42 and four sixth-order cumulants C60, C61, C62, and C63 [14].”
• Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks (2019, methodology): “[207] combined genetic programming and the KNN in order to improve the modulation classification accuracy, which can be viewed as a reliable modulation classification scheme for the SU in cognitive radio networks.”