用扩散模型生成逼真水下声学信道，支持快速适配新环境。
Diffusion-based Surrogate Model for Time-varying Underwater Acoustic Channels
- 基于条件扩散模型生成多样且真实的水下信道样本。
- 仅需少量数据即可适应新环境，还原关键通信性能指标。
- 适合水下通信系统设计与机器学习应用的高效建模工具。
准确建模时变水下声学信道对可靠水下通信系统的设计、评估与部署至关重要。传统物理模型依赖详细环境信息，而随机重放方法受限于实测信道多样性，难以泛化至未见场景，实用性受限。为此，我们提出StableUASim——一个预训练的条件隐空间扩散代理模型，可捕捉水下通信信道的随机动态特性。该模型利用生成建模技术，实现多样化且统计上真实可信的信道实例生成，并支持从特定测量样本进行条件生成。预训练使模型仅需少量附加数据即可快速适应新环境，隐编码表示还支持高效的信道分析与压缩。实验表明，StableUASim能准确复现关键信道特征与通信性能，为系统设计及机器学习驱动的水下应用提供可扩展、数据高效且物理一致的代理模型。
原文摘要 · Abstract (English)
Accurate modeling of time-varying underwater acoustic channels is essential for the design, evaluation, and deployment of reliable underwater communication systems. Conventional physics models require detailed environmental knowledge, while stochastic replay methods are constrained by the limited diversity of measured channels and often fail to generalize to unseen scenarios, reducing their practical applicability. To address these challenges, we propose StableUASim, a pre-trained conditional latent diffusion surrogate model that captures the stochastic dynamics of underwater acoustic communication channels. Leveraging generative modeling, StableUASim produces diverse and statistically realistic channel realizations, while supporting conditional generation from specific measurement samples. Pre-training enables rapid adaptation to new environments using minimal additional data, and the autoencoder latent representation facilitates efficient channel analysis and compression. Experimental results demonstrate that StableUASim accurately reproduces key channel characteristics and communication performance, providing a scalable, data-efficient, and physically consistent surrogate model for both system design and machine learning-driven underwater applications.
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