Article ID: 2020COL0039
In manufacturing fields such as factories, multiple wireless communication systems often operate in the same area simultaneously. Moreover, it is known that industrial equipment emits electromagnetic noise over channels for wireless communications [1]. In order to ensure reliable communications under such an environment, monitoring radio wave environments specific to each manufacturing field and finding channels and timing which enable stable communications are required. The authors have studied technologies to efficiently analyze a large amount of monitoring data including signals which show unknown spectrum such as wide band electromagnetic noise [2, 3]. This paper proposes performing machine learning using cepstrum vectors as features to grasp types of noise and signals from data measured under environments in which electromagnetic noise and communication signals coexist. By using this features, the authors demonstrate that reduction of computational loads and improvement of detection accuracy can be expected.
