Automated Modulation Classification of Communication Signals Using Hybrid Deep Learning

For the recognition of automated modulation, algorithms based on frequency, amplitude, and signal phase were extensively used. But the algorithm is affected considerably by noise, and the performance is degraded significantly in low signal-to-noise ratio (SNR) conditions. Deep learning (DL) is an effective mechanism that has accomplished greater attainment in various applications. But the usage in transmission systems has not been well-studied. This study explores an algorithm for modulation classification and multi-signal detection that is substantial in different transmission techniques. In this aspect, this study develops an Automated Modulation Classification of Communication Signals using Metaheuristics with Deep Learning (AMCCS-MDL) model. The presented AMCCS-MDL technique focuses on identifying and classifying different types of modulations in communication signals. To attain this, the presented AMCCS-MDL technique follows the SqueezeNet-based transfer learning technique to extract features from the communication signals. In addition, the AMCCS-MDL technique uses an attention-based bidirectional long short-term memory (ABiLSTM) model for classification purposes. At last, improved artificial bee colony optimization (IABC) algorithm is employed for the hyperparameter optimization process. A wide-ranging experimental validation process is made to demonstrate the enhanced efficacy of the AMCCS-MDL technique.