Edge-Enabled Modulation Classification in Internet of Underwater Things Based on Network Pruning and Ensemble Learning

The automatic modulation classification for surface and underwater sensors in the perception layer is crucial in the Internet of Underwater Things (IoUT), where deep learning (DL) is becoming an important tool to improve classification accuracy. This work focuses on the radio environment in the perception layer. The biggest challenge in popular DL-based methods is deploying the algorithm in edge devices with limited computing power. Network pruning has been found to be a critical and effective method for network lightweight and the improvement of resources, thus mitigating potential interference. While not studied in previous work, this article fills the hole in algorithm deployment’s criterion selection and accuracy loss. Specifically, we develop a convolutional neural network (CNN)-based lightweight framework on distinguishing modulated signals from generated data sets (which is named DLocean) in different signal-to-noise ratios (SNR). The performance of the lightweight framework is tested on the edge device. The experiments demonstrate that the proposed model compensates for accuracy and can successfully classify the modulation schemes with 93.4% accuracy at the SNR=5 dB. Our results also show that the proposed framework can improve performance without exceeding the original network complexity on edge device deployment.