CHANNEL STATE INFORMATION ACQUISITION FOR ADAPTIVE UNDERWATER ACOUSTIC NETWORKING
Date
2022-10-19
Authors
Ghannadrezaii, Hossein
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Abstract
This dissertation investigates the design aspects of an adaptive cross-layer architecture to optimize the energy efficiency, the spectral efficiency, and the reliability of underwater acoustic multihop relaying networks by utilizing channel state information (CSI). Specifically, an energy efficient channel-aware routing protocol for reliably relaying data packets, as well as a media access control to maximize the network throughput and maintain connectivity are described. These tasks are approached by predicting CSI using a novel data-driven probabilistic model. As the main contribution, a CSI acquisition approach based on a Markov chain process is proposed that exploits information from the physical environmental conditions, including the tide phase and flow, to improve the accuracy of channel characteristics predictions. Specifically, the method is intended to obtain the channel characteristics, including the gain, delay, Doppler spread, as well as the standard deviation of intrapaths delays in time varying conditions.
Description
In this dissertation, the correlation between different oceanic processes and
the acoustic channel characteristics is investigated to define a set of tide-dependent states corresponding to a particular channel condition. To analyze the impact of flow and surface elevation variations, channel soundings from a 34-day sea trial conducted in Grand Passage, Nova Scotia, are applied to a parametric model of the propagation channel. The probabilistic parametric model forms a data set by characterizing the time varying channel impulse response and by describing the channel tapped-delay structure statistically as a function of the tide phase. The proposed Markov chain is driven by the measured channel data set and predicts the future channel characteristics one tide cycle ahead.
Keywords
Underwater Acoustic Networking, Channel State Information, Channel Characteristics Prediction