Deep-Learning-Based CSI Feedback Compression for Underwater Acoustic OFDM Communications

The feedback of downlink channel state information (CSI) is essential for adaptive resource allocation to improve performance of orthogonal frequency-division multiplexing (OFDM) systems. However, excessive feedback incurs significant communication overhead. In this study, we propose UWAcsiNet, a deep learning (DL)-based model that encodes the downlink CSI feedback directly into compact codewords while preserving critical channel characteristics for efficient and accurate recovery at the transmitter. Specifically, a data processing method is designed to guide the focus of model towards sparsely distributed channel features. Furthermore, a binarization layer is employed to dynamically allocate information bits within the codeword, enabling efficient bit-level compression. Simulation results show that the proposed model achieves lower reconstruction error with limited feedback, even under extremely high compression ratios, while maintaining decent computational complexity.