Research On Performance Analysis And Prediction Method In TD-SCDMA Networks

With the development of communication technology, the bearing technology ofthe wireless network communication is no longer the key factor that restricts thewireless network development. On the other hand, in order to improve the usersatisfaction with the performance of the network, each network operating comprise isundertaking various network performance testing methods and all kinds of faultprediction and screening methods. While, specific to the network performanceindicators, the discourage stage is that we only monitor the network performance dataand only a small part is applied to the analysis of the status of the network operationalsituation. That is to say, the detected network performance data has not been fullyutilized, such as to improve its control of the network running status and theforecasting accuracy. Specific for the features of network performance indicator datain TD-SCDMA, as well as the theoretically relevance of performance indicators, andto meet the management need of TD-SCDMA network, the network performanceanalysis and prediction method are researched, mainly including the artificial neuralnetwork based analysis method and the multivariate time series prediction method.Network performance analysis is to recognize the network running status basedon the historical data, which is collected in the network operation, and to forecast thefuture trend of the network operation accordingly. Network performance prediction isthe foundation of network optimization and network fault pretreatment. On the basisof studying the theory of network performance indicator data, we detailed study thebasic principle and the prediction algorithm process of error back propagation (BP)neural network prediction algorithm, and we use it to realize the phone trafficforecasting analysis. After studying the learning rule of neural network and predictionprinciple, we mainly research the algorithm process of error back propagation network prediction, and its learning rules and error propagation and correction process.Simulation experiment analyzed and forecast the TD-SCDMA network trafficperformance indicators. Experimental results show that the error back propagationneural network prediction accuracy is above90%, and the prediction precision ispositively correlated with the dimensions of the input vector. And the algorithmexecution speed can meet the requirement of real-time phone traffic performanceindicator data prediction.The network indicators can be seen as time series, so we use time seriesforecasting method to realize the prediction of network performance indicator. In thesection of fuzzy time series prediction algorithm, we describe the symbolization offuzzy time series method, correlation calculation method, the fuzzy rule matrixconstruction and the matching rules. In the simulation, we judge the smoothness ofthe phone traffic performance data after normalizing, log calculating, differencing.Connection rate and congestion rate are stationary series after first order differencing.Then in the simulation test, a binary fuzzy time series prediction based on connectionrate and congestion rate is conducted to realize the connection rate prediction.Through the example of binary fuzzy time series prediction, it verified theeffectiveness of the proposed fuzzy time series prediction algorithm.