Optimization of Abnormal Signal Recognition in Mobile Communication Net-works Based on Neural Networks
Conventional mobile communication network abnormal signal recognition is carried out in an ideal environment,but actual abnormal signal recognition is often affected by other in-terferences,resulting in recognition errors.Therefore,an optimization method for identifying abnormal signals in mobile communication networks based on neural networks was designed.Extract the characteristics of abnormal signals in mobile communication networks,extract the time-domain of short-term energy signals and zero crossing signals,filter the noise signals in the time-domain,and retain the parts of abnormal signals.A communication network abnormal sig-nal recognition model is constructed based on neural networks.The abnormal signal feature neu-rons are used as inputs,and the weighted sum of the input neuron features is used to activate the threshold to determine whether the current signal is abnormal,thereby optimizing the accuracy of abnormal signal recognition.Optimize the regression loss of network abnormal signal recog-nition,reduce model training loss,and thus meet the expected output of the model.Through comparative experiments,it was verified that this method has higher recognition accuracy and better optimization effect,and can be applied in practical life.
neural networkMobile communicationAbnormal signal recognition
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