Abstract
Automatic modulation classification (AMC) plays a crucial role in the cognitive radio networks, to which feature-based (FB) methods are the dominating solutions. However, the original features in FB methods are redundant, leading to the ambiguity of classification. To tackle this problem, this paper proposes a novel multi-objective modulation classification (MOMC) method. To reduce the redundant features, the original multi-features are recombined into a single feature by multiobjective genetic programming (MOGP) algorithm. Two quantitative objectives, the classification error rate and the variance for robustness, are then presented to jointly optimize the algorithm as two fitness functions. Furthermore, the single feature generated by MOGP is classified by logistic regression (LR) with low computational complexity. Simulation results verify the enhanced robustness and classification accuracy performance yielded by our proposed MOMC method compared to the existing classification methods.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
