DeWater: Towards Efficient Underwater Communication via Fine-tuned Learning-enhanced Demodulation

摘要

Acoustic communication is becoming increasingly pivotal in underwater environments, which are often challenged by strong multipath propagation and high ambient noise. However, existing solutions typically depend on specialized hardware, complex modulation schemes, or increased transmission power to ensure reliability, which are costly and unsustainable for energy-constrained underwater systems. To overcome these limitations, we propose DeWater, a deep learning-enhanced Chirp Spread Spectrum (CSS) demodulation framework designed for robust and energy-efficient communication. DeWater integrates a Swin Transformer-based demodulator with two novel components: a Frequency Transformation Block (FTB) and a Wavelet Convolution (WTConv) module, which together enable accurate demodulation without increasing transmitter complexity or power consumption. To further enhance noise resilience, we propose a two-stage fine-tuning strategy: Stage 1 applies noise-aware data augmentation through temporal and spectral masking, and Stage 2 employs adversarial training using Gradient Norm Adversarial Augmentation (GNAA). Extensive real-world underwater experiments using scenario-specific training demonstrate that DeWater achieves a significant performance gain, yielding an 82.81% reduction in symbol error rate compared to traditional dechirp demodulation. Notably, to maintain a 10% symbol error rate, DeWater requires 66.52% less transmitter power than the baseline, demonstrating its effectiveness as a low-power, robust solution for underwater acoustic communication.