Cognitive intelligence in the mapping of underwater acoustic communication environments to channel models

Abstract

The recent growing interest in underwater acoustic communication (UAC) requires an in depth understanding of a vast and diverse water medium. The UAC channel presents many difficulties such as high frequency, space, and time selectivity, frequency dependent noise, and significant range and band limitation on transmission. The existing channel models may not be sufficient to cover the entire UAC channel domain and its many applications. In addition, the choice of channel model from the existing ones may not always be accurate to represent the channel environment. Thus, it is necessary to explore higher communication intelligence in the mapping of UAC channel models according to environment. This paper will present novel cognitive intelligent (CI) algorithm in accurate selection of UAC channel models. To the best knowledge of the authors, cognitive intelligence in the mapping of UAC environment to channel models has not been presented in the literature. Thus this research presents a valuable work.