Hybrid Deep Learning-Based Adaptive Multiple Access Schemes Underwater Wireless Networks

Achieving sound communication systems in Under Water Acoustic (UWA) environment remains challenging for researchers. The communication scheme is complex since these acoustic channels exhibit uneven characteristics such as long propagation delay and irregular Doppler shifts. The development of machine and deep learning algorithms has reduced the burden of achieving reliable and good communication schemes in the underwater acoustic environment. This paper proposes a novel intelligent selection method between the different modulation schemes such as Code Division Multiple Access(CDMA), Time Division Multiple Access(TDMA), and Orthogonal Frequency Division Multiplexing (OFDM) techniques using the hybrid combination of the convolutional neural networks(CNN) and ensemble single feedforward layers(SFL). The convolutional neural networks are used for channel feature extraction, and boosted ensembled feedforward layers are used for modulation selection based on the CNN outputs. The extensive experimentation is carried out and compared with other hybrid learning models and conventional methods. Simulation results demonstrate that the performance of the proposed hybrid learning model has achieved nearly 98% accuracy and a 30% increase in BER performance which outperformed the other learning models in achieving the communication schemes under dynamic underwater environments.