A Data-Driven Underwater Acoustic Channel Simulator Based on Variable-Parameter Autoregressive Modeling

For testing underwater communication performance, model-driven channel simulators have high computational complexity, and the required dynamic environmental parameters are difficult to provide, making it challenging to generate realistic time-varying channels. In contrast, data-driven simulators do not suffer from these problems. Traditional underwater acoustic channel replay methods employ time-varying impulse responses; however, herein, a time-varying transfer function (TVTF) is employed to overcome the complexities associated with the large number of scattered acoustic paths in the time-delay domain. Singular value decomposition (SVD) is employed to implement stochastic acoustic channel replay by decomposing TVTF into principal and random components. A new random component can be generated randomly via the inverse process of the variable-parameter autoregressive (VPAR) model. To solve VPAR model, basis functions were used to translate frequency-dependent coefficients into constant coefficients, which can be estimated via a least square algorithm. SVD-VPAR replay (SVD-VPARR) method was proposed to generate realistic channel samples by inputting different complex noises into the inverse process of VPAR. The proposed simulator is validated using two measured channels from the WATERMARK benchmark and is compared with direct replay and an empirical mode decomposition-based stochastic replay method. Local time-frequency statistics and binary phase-shift keying / orthogonal frequency-division multiplexing communication results show that SVD-VPARR preserves the principal frequency-dependent channel characteristics and the major bit-error-rate variations with signal-to-noise ratio, time, and frequency.