Cognitive intelligence in UAC channel parameter identification, measurement, estimation, and environment mapping

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, time, and space selectivity, frequency dependent noise, and significant range and band limitation on transmission. The traditional channel parameter measurement and estimation methods may not be sufficient and sometimes inapplicable to UAC, as many of the underwater situations are beyond human reach. This paper will first identify and classify the UAC channel environment, its parameters, parameter measurement and estimation methods. Then two novel cognitive intelligent algorithms will be presented to measure and estimate the parameters and map them to appropriate UAC channel environment models. To the best knowledge of the authors, the concept of cognitive intelligence (CI) and the proposed CI algorithms in UAC channel parameter measurement, estimation, and mapping ha ve not been addressed in literature to current date. Thus this research presents a pioneering work in this domain.