CARNet: An Efficient Cascaded and Attention-Based RNN Architecture for Modulation Classification in Cognitive Radio Network Using Improved Kookaburra Optimization Strategy

In cognitive radio (CR) networks, the automatic modulation classification (AMC) is considered as the significant role in smart wireless communications. Due to the high growth of deep learning in the modern days, neural network-aided automated modulation categorization tasks have become highly demanded. Nevertheless, an enormous amount of attributes and the neural network's complexity make them complex to adopt in various scenarios. Moreover, the receiver systems are limited by the latency and less storage resources. Additionally, the detection system of the signal modulation is mostly hampered by overfitting issues and insufficient information. Therefore, a robust AMC model based on adaptive deep learning is implemented to determine the type of modulation used at the transmitter by observing the received signal. Initially, the necessary raw data for the suggested model is garnered from benchmark dataset. Also, the optimal features from the raw data are selected with the help of the fitness-revised position updating in Kookaburra optimization (FPUKO) for minimizing the computation time and enhancing the accuracy rates in the classification process. Moreover, this optimal feature selection makes the modulation selection process quick and efficient. Finally, the optimal features are fed to a cascaded and attention-based recurrent neural network (CA-RNN) in the modulation classification, which is designed to classify the type of modulation used on the transmitter side. Various experiments are conducted for the designed framework by comparing it with the existing models to view its efficiency.