Diffusion model-based underwater acoustic communication receiver

This paper proposes a frequency hopping binary frequency shift keying underwater acoustic (UWA) communication system, where a denoising diffusion probabilistic model (DDPM) and a convolutional neural network (CNN) are sequentially used for signal reconstruction and signal demodulation, respectively. Unlike the deep transfer learning (DTL)-based system, this system employs a DDPM to process the received Mel-spectrogram, reconstructing the distorted Mel-spectrogram caused by UWA channel effects, and generating a spectrogram that approximates the transmitted signal, which is then demodulated by the CNN. The proposed system outperforms conventional systems and achieves performance comparable to DTL-based systems in simulation and experiment. DTL requires data samples from new scenarios to learn signal characteristics during deployment; in contrast, this method uses the generative capability of DDPM to enable direct deployment in dynamic underwater environments without additional adaptation processes, offering flexibility and suitability for complex and variable UWA propagation channels.