Frequency Offset Prediction of Oven Controlled Crystal Oscillator Based on GRU Neural Network
The frequency stability of oven controlled crystal oscillator,an important component of 5G communication system in the new era is very important.Accurate prediction of oven controlled crystal oscillator frequency offset can improve the security and reliability of system working state.In order to further improve the prediction accuracy of frequency offset and meet the requirements of 5G communi-cation system,a crystal frequency prediction model based on gated recurrent unit(GRU)neural network is proposed.The model takes into account both temperature and aging factors,and uses neural network with excellent adaptability and nonlinear generalization ability to learn the change rule of oven controlled crystal oscillator frequency offset.Finally,the measured data of 14 days are analyzed and compared with those got by using recurrent neural network and long short-term memory neural network.Root mean square error,mean absolute error and algorithm running time are taken as evaluation indexes,the results show that GRU network has higher prediction ac-curacy and faster operation speed.
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