Online Modeling and Prediction of the Large-Scale Temporal Variation in Underwater Acoustic Communication Channels

Influenced by environmental conditions, underwater acoustic communication channels exhibit dynamics on various time scales. The channel dynamics within a short transmission duration have been extensively studied in existing research. In this paper, we investigate online modeling and prediction of slowly-varying channel parameters in a long term, by exploiting their inherent temporal correlation and correlation with water environmental conditions. Examples of those parameters include the locally-averaged channel properties within a transmission, such as the average channel-gain-to-noise-power ratio, the fast fading statistics, the average delay spread, and the average Doppler spread. Adopting a data-driven perspective, this paper models the temporal evolution of a slowly-varying channel parameter of interest as the summation of a time-invariant component, a time-varying process that can be explicitly represented by available environmental parameters, and a Markov latent process that describes the contribution from unknown or unmeasurable physical mechanisms. An algorithm is developed to recursively estimate the unknown model parameters and predict the channel parameter of interest, based on the sequentially collected channel measurements and environmental parameters in real time. We further extend the above model and the recursive algorithm to channels that exhibit periodic (a.k.a.
seasonal
) dynamics, by introducing a multiplicative seasonal autoregressive process to model the seasonal correlation. The proposed models and algorithms are evaluated via extensive simulations and data sets from two shallow-water experiments. The experimental results reveal that the average channel-gain-to-noise-power ratio, the fast fading statistics, and the average delay spread can be well predicted.