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The evolution of wireless networks towards high mobility has led to the concept of connecting vehicles. Wireless link quality is a vital attribute of a dynamic vehicular environment and a backbone for successful communication among vehicles. The vehicles are expected to be equipped with a number of sensors and measurement devices, making a lot of data available for research. To solve the challenges in the context of the connectivity of vehicles, data-driven approaches are recently applied to estimate the dynamic attributes of vehicular networks. In this paper, a deep learning model based on an artificial neural network technique is proposed to predict wireless link quality in vehicular networks. The proposed algorithm uses the essential features, including the current location of a vehicle, to estimate the quality of a wireless link.

A Deep Learning-Based Model for Link Quality Estimation in Vehicular Networks