From Real-World Data to Synthetic Models: A Time-Varying Approach for Underwater Networks

The selection of the most appropriate method for modeling the acoustic channel for underwater network simulation has always been a trade-off between computational cost, accuracy, and statistical significance. While some models, such as ray tracers [2], offer high physical accuracy, they are also highly demanding in terms of computational cost. Other models, such as Urick-Thorp [18], are very lightweight but lack physical accuracy in some aspects. Recently, the research community has more often resorted to models based on data collected during sea trials and experiments, to retain accuracy but using them to generate different statistical realizations to gain statistical variability. In this work, we describe and analyze an approach based on Hidden Markov models. We explore how to extract the model’s parameters from real-world experimental data, for a better representation of the underwater acoustic channel to be used in underwater network simulations, and how to use it in an efficient and time-effective way. We then show the results of a simulation session and compare them with the results retrieved from the experiment data.