Wireless Link Quality Prediction Using Physical Space Information based on Deep Learning

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  • フィジカル空間情報を用いた深層学習に基づく無線通信品質予測

Abstract

Many types of the wireless terminals are emerging because of the great advances in wireless communication systems. Various novel services are expected to be available in Society 5.0 that is based on Internet of Things (IoT) by a high degree of convergence between cyberspace (virtual world) and physical space (real world). This paper discusses the potential of the physical space information use for the future wireless communication systems in Society 5.0. In wireless LAN systems, the throughput prediction was conducted using physical space information such as robot position information and camera images. The performances of the prediction models using deep learning algorithms including recurrent neural network (RNN) were evaluated by using the measured dataset in the indoor environment. The results showed the effectiveness of the physical space information use for wireless link quality prediction.

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