End-to-End Modulation Recognition in Underwater Acoustic Communications Using Temporal Large Kernel Convolution with Gated Channel Mixer

Modulation recognition plays a crucial role in the acoustic communication systems of autonomous underwater vehicles (AUVs). However, deploying accurate modulation recognition models on resource-constrained edge devices remains a significant challenge. To address this issue, we propose GIQNet, an end-to-end deep-learning framework for underwater acoustic modulation classification. GIQNet incorporates two key components: a Temporal Large Kernel Convolution (TLKC) module and a Gated I/Q Mixer (GIQM) module. TLKC applies large kernel convolutions to capture long-range dependencies in the temporal domain. At the same time, GIQM facilitates the adaptive fusion of information from the in-phase and quadrature channels using gating mechanisms. Experiments on real-world underwater acoustic datasets demonstrate GIQNet's effectiveness. Moreover, when evaluated on publicly available radio modulation datasets, GIQNet outperforms existing approaches while using 55% fewer parameters. Further analyses validate the effectiveness of TLKC and GIQM in learning robust features for modulation recognition. The results show that the proposed architecture provides an efficient and effective deep-learning solution tailored for underwater acoustic modulation classification, making it suitable for deployment on resource-constrained AUV edge devices.