Predictive QoS of wireless communication for autonomous driving vehicles by fine tuning

In this paper, a system for predicting degradation of wireless-communication quality in the case of autonomous vehicles is proposed. It uses historical-data-based throughput estimation using a neural network trained on location information and past performance. A method for improving the accuracy by fine tuning is also proposed. We evaluate the accuracy of throughput estimation using the proposed system in two outdoor use cases (field and urban) and one indoor use case. The results show that the proposed method of fine tuning using a large amount of received power data and a small amount of throughput data can improve the estimation accuracy of throughput.