Research And Implementation Of TD-SCDMA Network Performance Prediction And Monitoirng System

Along with the rapid development of mobile communication networks and the increasingcompetition in the market, the pros and cons of network quality have become a key factor toimprove the competitiveness of enterprises. Therefore, greater attentions have paid to themeasurement of build level of itself network quality and customer satisfaction by the mobileoperators. The rapid development of mobile communications business will pose greaterchallenges to the network. The rapid growth of network users and applications and theoverloading of network equipment, promote increasingly heavy burden of the network. Thelack of reliable technologies and means on network planning and network optimization,results in decreased network performance. Therefore, the evaluation system is needed to dealwith these problems, which not only predict and analyze network performance, but alsoreal-time monitor it as much as possible to avoid problems, ameliorate network performanceand improve operational quality, then satisfied the customer.The existing communications operator's network management doesn't technically realize"active monitoring" of communication network according to user needs and businessdevelopment, and can not predict the impending failure of the network, and take preventivemeasures before the occurrence of the switch alarms to optimize network in a timely manner.In addition, business and operation and maintenance management system are independent ofeach other, with no automatic process flow and data sharing. So it can not achieve theproactive monitoring of network management for business to format the network dynamicalarm threshold in real time based on business.TD-SCDMA network performance prediction analysis and optimization system is aplatform for3G and above that it can assist network managers implement network qualityassessment, network planning and performance optimization quickly and efficiently, sinceextensive and in-depth intelligent analysis be conducted based on large amount of historicaldata such as the parameters of configuration information, a variety of performance indicatorsand alarms, and a wealth of performance prediction, statistical computing, network planning,graphical display of report output and problems seeking functions be performed. The projectwill compensate for the deficiencies of the traditional communication NMS. High-endsoftware engineering technology and advanced management thinking, implementingTD-SCDMA switched network real-time performance analysis and active monitoring system,proactively monitoring the network performance according business, makingrecommendations on business management, will make a real solution for operation andmaintenance management oriented business development. Network performance prediction is an important part of active surveillance system thatoptimize the network in a timely manner to predict the approximate trend of the performanceindicators for the next hours, day or few days, for the impending failure be solved in advanceby the systems and even network management staff. Measures are prevented by take measuresbefore the switch alarm timely.It is always difficult to accurately predict System for Mobile Communications networkperformance, for it is affected by a number of factors. A variety of methods and techniques,such as the gray prediction, BP neural network, time series analysis, regression analysis, alsowidely used in network performance prediction, and each method has its own advantages anddisadvantages. In this paper, a new prediction method, Gaussian process regression modelbased on the median filtering, is applied to the prediction of network performance indicators.Compared with neural network prediction method, Gaussian process have fewer modelparameters, easier to parameter optimization problem, and to convergence, predict effect rapidand apparently. However, it brings a number of unexpected challenges to the Gaussianprocess regression model prediction to make the effect of prediction undesirable after trainingwith large deviation of predictions, due to several factors such as fluctuations in networkperformance indicators, stochastic and nonlinear strongly. In this paper, by using medianfiltering, fluctuations indicator data become relatively smooth after filtered, trends is morepronounced. This will give a significant improvement for the training of the GP modellearning, and make the predictive value more accurate. Predictive analysis features havesubstantial, effective, and rational function and role for network managers or optimize staff.Experiments show that the combination method, presented by this paper, improves theprediction accuracy with a strong self-learning ability, and provides a new method and meanfor network performance analysis.Active monitoring system is an important part of the network performance predictionanalysis and optimize system of TD-SCDMA, is the main body of the prediction analysis andindicators monitoring alarm, of which the "active monitoring real-time performance alarmalgorithm" is core of the system monitoring alarm module, consisting of the "baselinealgorithm","tolerance calculation method" and "alarm mechanism".The algorithm comes tureby comparing the collected performance indicators data with the predefined tolerate line(alarm trigger threshold),when it is bigger than the tolerate line,the alarm generationmechanism be triggered.At last in this paper the completed work have been summarized simplely.And it givesviews and outlook for the further experiment research of prediction.