Efficiency Enhancement for Underwater Adaptive Modulation and Coding Systems: Via Sparse Principal Component Analysis

In this letter, to explore key channel state information (CSI) as a more efficient switching metric in the task of underwater adaptive modulation and coding (AMC), a sparse principal component analysis (SPCA) based approach is proposed from the perspective of statistical analysis plus machine learning (ML). This data-driven sparse learning method can offer significant system efficiency enhancement in the procedures of both channel estimation and communication scheme switching. By leveraging a dataset that contains real-world channel measurements collected from three field experiments, simulations demonstrate the effectiveness of the proposed scheme.