LRCPN: A Lightweight Parallel Scheme for Underwater Acoustic Modulation Recognition

This letter proposes a lightweight parallel recurrent–convolutional scheme to improve generalization capability and recognition accuracy while maintaining low computational complexity in resource-constrained underwater acoustic channels. In this scheme, the lightweight convolutional network is used to extract time–frequency features, and the lightweight recurrent network with gated recurrent units is used to capture long-term temporal phase correlations, thereby alleviating the Doppler-induced phase rotation and inter-symbol interference in time-varying multipath underwater acoustic channels. Sea-trial data are collected during shallow-water sea trials with strictly separated training and evaluation datasets. Experimental results on ten underwater acoustic modulation types show that the proposed scheme improves recognition accuracy by 6.2% and reduces computational cost by 22.4%, while exhibiting stronger generalization capability compared with benchmark schemes.