Ensemble of Deep Learning Enabled Modulation Signal

Automated classification of underwater acoustic signals allows to effectively utilize the acoustic spectrum by certain actions such as interference circumvention and marine mammal protections. The commonly utilized modulation techniques for underwater acoustic communication are MPSK (BPSK, QPSK, and 8PSK) and MQAM. Detection of modulation type of a received waveform enables sonar and communication in a similar bandwidth with least collision and it can determine the system functioning exterior to the permitted regime. Automated modulation classification models can make utilize of machine learning (ML) and deep learning (DL) techniques, particularly for underwater acoustic communication. With this motivation, this study designs a new ensemble of deep learning based modulation signal classification (EDL-MSC) models for underwater acoustic communication. Initially, an impulsive noise pre-processor was used for eliminating the impulse from the target signal. Besides, three deep learning models namely bi-directional long short term memory (BiLSTM), gated recurrent unit (GRU), and stacked sparse autoencoder (SSAE) models are used to derive features in the temporal waveform and square spectra of the pre-processed signal. In addition, black widow optimization (BWO) is applied for the optimal hyperparameter tuning of the DL models. Lastly, an ensemble of voting schemes is applied to integrate the outcome of the three DL models. The proposed model has the ability to perform effective modulation classification process in underwater acoustic communication. The performance validation of the EDLMSC technique takes place under several aspects and the comprehensive comparative analysis ensured the supremacy of the EDL-MSC technique over the recent approaches.