Technology Category
Search and Optimization: Sampling/Simulation-based SearchMachine Learning: Deep Generative Models & AutoencodersCognitive Modeling & Cognitive Systems: Simulating Human Behavior
Diffusion-based Surrogate Model for Time-varying Underwater Acoustic Channels
🤖 AI Summary
Existing underwater acoustic channel modeling approaches face two key limitations: physics-based models require accurate prior environmental parameters, while stochastic replay methods suffer from poor generalizability and limited diversity. To address these issues, this paper proposes StableUASim—a pretrained conditional latent-space diffusion surrogate model. It integrates an autoencoder with a conditional diffusion process to achieve physically consistent channel representation and efficient generation in the latent space. StableUASim enables rapid adaptation to new scenarios with only minimal target-domain data, balancing statistical fidelity and cross-environment scalability. Experimental results demonstrate that it significantly outperforms conventional methods in modeling critical channel characteristics—including multipath structure, delay spread, and Doppler shift—as well as in end-to-end communication performance simulation. StableUASim thus offers high-fidelity, generalizable channel emulation, making it particularly suitable for system design and data-driven underwater acoustic applications.
Technology Category
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Modeling time-varying underwater acoustic channels accurately
Overcoming limitations of conventional physics and stochastic models
Providing scalable data-efficient channel simulations for communication systems
Innovation
Methods, ideas, or system contributions that make the work stand out.
Pre-trained conditional latent diffusion surrogate model
Generates diverse realistic underwater acoustic channels
Autoencoder latent representation enables efficient analysis
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