Research On Uplink Interference Identification And Avoiding Methods Of TD-LTE System

By the end of 2018,the number of users of 4G has reached 1.13 billion.Under the condition of almost full coverage of LTE network in China,the research of 4G has great significance.However,interference has always been an important factor affecting the quality of TD-LTE network.At present,domestic operators' interference detection methods and processes are single,and they basically use manual detection to identify and deal with interference.However,with the expansion of the network scale year by year,the total number of LTE districts in Liaoning Province has reached 35,000.The traditional manual identification method has been unable to meet the demand,which requires a lot of labor costs,and the efficiency is low,and the accuracy can not be guaranteed.Therefore,it is imperative to construct an automatic interference detection method.Starting from the practical problems encountered in the network optimization of operators,this paper analyses the characteristics of 35000 LTE districts in Dalian City from the aspects of disturbance degree,disturbed frequency band,and disturbed cell 100 RB frequency domain disturbance waveform shape characteristics.Starting from the waveform characteristics in frequency domain,two different types of interference recognition algorithms are proposed for F-band common interference types in TD-LTE interference diagnosis system.First,according to the different frequency domain characteristics of the disturbed cells,the disturbed cells are classified,four characteristic waveforms are extracted and the corresponding recognition algorithm is designed.Finally,the algorithm is simulated by MATLAB.Second,the algorithm is based on the BP neural network model for automatic recognition.According to the difference of output data and the number of hidden layers,we design four models,and through pre-training.In summary,one of the best recognition rate and training speed is selected.Finally,based on the optimal model,the best hidden layer is determined by trial and error method in the process of training data.On the basis of determining the hidden layer,the number of training times is determined,which makes the automatic recognition rate of the algorithm reach more than 90%.In the process of verification,the accuracy of the algorithm is verified based on artificial identification.According to the actual situation of the network,the threshold of the algorithm is modified to improve the accuracy.In the verification stage,the recognition rate of the two models selected by the two methods is analyzed by using 35000 blocks of Dalian whole network,and the advantages and disadvantages of the two models are compared.It is concluded that the BP neural network model has the characteristics of high learning efficiency,strong expansibility and high recognition accuracy compared with the traditional recognition algorithm.Finally,the accuracy of the algorithm is validated again by manual checking,which is an effective complement to the research of interference checking methods and makes the interference checking work come true.Through the convenient and effective interference automatic classification model and processing measures,the interference cells are sorted out and classified quickly in the process of wireless network optimization,and the interference is reduced or eliminated by the means of effective investigation,which can reduce the index pressure of operators' front-line staff and enhance the satisfaction of Dalian mobile users on the Internet.This is the ultimate goal and effect of this paper.