Wireless Link Selection Methods for Maritime Communication Access Networks—A Deep Learning Approach

Abstract

In recent years, we have been witnessing a growing interest in the subject of communication at sea. One of the promising solutions to enable widespread access to data transmission capabilities in coastal waters is the possibility of employing an on-shore wireless access infrastructure. However, such an infrastructure is a heterogeneous one, managed by many independent operators and utilizing a number of different communication technologies. If a moving sea vessel is to maintain a reliable communication within such a system, it needs to employ a set of network mechanisms dedicated for this purpose. In this paper, we provide a short overview of such requirements and overall characteristics of maritime communication, but our main focus is on the link selection procedure—an element of critical importance for the process of changing the device/system which the mobile vessel uses to retain communication with on-shore networks. The paper presents the concept of employing deep neural networks for the purpose of link selection. The proposed methods have been verified using propagation models dedicated to realistically represent the environment of maritime communications and compared to a number of currently popular solutions. The results of evaluation indicate a significant gain in both accuracy of predictions and reduction of the amount of test traffic which needs to be generated for measurements.

A Sliced Parabolic Equation Method to Characterize Maritime Radio Propagation

“…In contrast to the homogeneous assumptions in [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ], the duct layers are range-dependent. The strong spatial–temporal variability of the lower atmosphere in near-shore areas can cause horizontal variability in atmospheric ducts.…”

“…The authors in [ 39 ] proposed a log-distance maritime wireless channel at 8 GHz, and the meteorological data pointed out that the difference in the evaporation duct profiles at the Tx and Rx increases with increasing link distance. Machine learning has been widely used in the processing of wireless channel measurements [ 40 ]. In [ 41 ], a two-stage deep learning network was designed to characterize duct effects.…”