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.

翻译：精确建模时变水声信道对于可靠水下通信系统的设计、评估与部署至关重要。传统物理模型需要详细的环境知识，而随机重放方法受限于测量信道的有限多样性，且往往难以泛化至未见场景，降低了其实际适用性。为应对这些挑战，我们提出了StableUASim——一种预训练的条件潜在扩散替代模型，能够捕捉水声通信信道的随机动态特性。通过利用生成建模，StableUASim可生成多样化且统计上真实的信道实现，同时支持基于特定测量样本的条件生成。预训练机制使其能够利用极少量的额外数据快速适应新环境，且自编码器的潜在表示有助于实现高效的信道分析与压缩。实验结果表明，StableUASim能准确复现关键信道特性与通信性能，为系统设计及机器学习驱动的水下应用提供了一个可扩展、数据高效且物理一致的替代模型。