Work

Diffusion model-based underwater acoustic communication receiver

Year: 2025

Type: article

Abstract: 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.

Source: The Journal of the Acoustical Society of America

DOI: 10.1121/10.0039870

Authors: Yufei Liu, Yunjiang Zhao, Peter Gerstoft, Li Ma

Institutions: Chinese Academy of Sciences, Institute of Acoustics, Scripps Institution of Oceanography, University of California San Diego

Language: English

Topic: Underwater Vehicles and Communication Systems

Subfield: Ocean Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: bronze

Funders: Chinese Academy of Sciences, Youth Innovation Promotion Association of the Chinese Academy of Sciences, Youth Innovation Promotion Association

Awards: G211477167, G2429320192, G7884047758