| Acoustic channel models provide a tool for predicting the performance of underwater communication systems prior to deployment, and are thus essential for system design. In this dissertation, we offer a statistical channel model, which incorporates physical laws of acoustic propagation (frequency-dependent attenuation, bottom-surface reflections) as well as the effects of inevitable random local displacements.;We focus on random displacements on two scales: small-scale effects, that involve distances on the order of a few wavelengths, and large-scale effects, that involve many wavelengths. Small-scale effects include scattering and motion-induced Doppler shifting, and are responsible for fast variations of the instantaneous channel response; while large-scale effects describe the location uncertainty and changing environmental conditions, and affect the locally-averaged received power. We model each propagation path by a large-scale gain and micro-multipath components that cumulatively result in a complex Gaussian distortion. Random surface motion and transducer displacement introduce additional variation whose temporal correlation is described by Bessel-type functions. The total power, or the gain contained in the channel, averaged over small-scale, is modeled as log-normally distributed. The models are validated using real data obtained from four experiments. Specifically, experimental data are used to assess the distribution and the auto-correlation functions of the large-scale transmission loss and the short-term path gains. While the former indicates a log-normal distribution with an exponentially decaying auto-correlation, the latter indicates a conditional Ricean distribution with Bessel-type auto-correlation.;Based on the proposed model, we design a channel simulator which we employ to generate a time-varying channel whose statistical characteristics match with those of a real underwater channel. The simulated channel is applied to convey an OFDM signal to coherent and differentially coherent detectors, and the MSE performance of the experimental and simulated systems are shown to be similar.;Finally, we investigate the feasibility of adaptive power control using an experimental data set as well as theoretically. Based on the observed time-correlation properties of the large-scale channel gain, linear power prediction is employed and achievable power savings are obtained analytically (assuming a log-normal gain distribution) and experimentally. The results indicate that substantial power savings are possible over extended periods of time. |
