Design and Implementation of a Lightweight Underwater Acoustic Modulation Classification

To develop an efficient and accurate lightweight underwater acoustic (UWA) modulation recognition algorithm, this study proposes an algorithm called Channel Expansion-Lightweight Recognition Network (CE-LRN). The CE-LRN sig-nificantly reduces computational complexity by adopting small convolutional kernels and quantized convolutional layer techniques, achieving deployment standards friendly to embedded devices. Additionally, a channel expansion module is introduced in the CE-LRN algorithm, which effectively improves recognition accuracy with only a slight increase in computational complexity. Compared with two recently proposed recognition algorithms, the CE-LRN greatly reduced computational complexity, with its floating-point operations (FLOPs) being only 16.9M, a reduction of over 83.4%. Experimental results demonstrated that CE-LRN has good classification performance for Multi-Frequency Shift Keying (MFSK) and Multi-Phase Shift Keying (MPSK) signals.