Monitoring a cellular wireless network for a spectral anomaly and training a spectral anomaly neural network

Abstract

A monitoring system and monitoring method for detecting a spectral anomaly in a cellular wireless network, in particular a 5G private uRLLC network, wherein an RF receiver monitors the cellular wireless network spectrum and derives spectrum and/or physical measurement values of the spectrum of the cellular wireless network, and a processing unit of the monitoring system executes a spectral anomaly neural network trained by a machine learning algorithm in a training system, wherein the processing unit obtains the spectrum and/or the physical measurement values of the spectrum and processes it to detect a spectral anomaly information. Further, a training system and training method for training a spectral anomaly neural network, wherein the training system/method is used in a cellular wireless network, in particular a 5G private uRLLC network, and an RF receiver of the training system monitors the cellular wireless network spectrum and derives spectrum and/or physical measurement values of the spectrum of the cellular wireless network, and a processor of the training system executes a machine learning algorithm to train the spectral anomaly neural network based upon the derived spectrum and/or physical measurement values of the spectrum of the cellular wireless network.