Deep Learning CNN-Based Hybrid Extreme Learning Machine with Bagging Classifier for Automatic Modulation Classification - N. Durga Indira; M. Venu Gopala Rao (International Journal of Intelligent Systems and Applications in Engineering, 2022)
Automatic modulation classification (AMC) becomes the important process in the various communication systems including commercial, telecommunication and military applications. Further, the accuracy of AMC impacts the performance of these applications. Various machine learning approaches were developed to improve the performance of AMC. However, they failed to classify the different modulation schemes, which needs to satisfy all the spectrum requirements under multipath fading environment. Further, the conventional methods are suffering with computational complexity in training to satisfy the real-time operational requirements.So, this article focuses on implementation of extreme learning machine (ELM) for reduction of training complexities and improves the classification performance. Initially, deep leaning convolutional neural network (DLCNN) model is introduced for extracting the inter dependent modulation features based on different modulation types. Further, the hybrid ELM with bagging (HELM-B) classifier is used to classify the various modulation types, i.e., families. The simulation results shows that the performance of proposed AMC system is superior to the conventional AMC systems with an accuracy of 99.15%, and F1-score of 98.73%, respectively.