移動網(wǎng)絡優(yōu)化中業(yè)務量預測及移動用戶高度分層方法研究
發(fā)布時間：2018-07-23 12:35
【摘要】：虛擬化和容器技術使得核心網(wǎng)資源分配越來越靈活高效。因此需要網(wǎng)絡能夠提前對業(yè)務量進行感知和預測。通過預測話務量和用戶數(shù)并按照預測的結果合理分配有限的網(wǎng)絡資源,能有效提高服務質量。另一方面,隨著城市高層樓宇增多,深度覆蓋特別是垂直覆蓋的優(yōu)化也變得越來越重要,但測試人員無法進入高層建筑測試也是這一問題的難點。尋求能有效的利用用戶數(shù)據(jù)來判斷高層樓宇內(nèi)的信號覆蓋質量的方法,在學術領域和工業(yè)界還屬于空白。首先,本文介紹LTE系統(tǒng)核心網(wǎng)及接入網(wǎng)絡的網(wǎng)絡架構,給出通過LTE系統(tǒng)接口進行控制面信令探測,進而利用控制面信令及用戶面數(shù)據(jù),分析得到用戶數(shù)、話務量和用戶感知的Wi Fi信息的過程。本文還研究了時間序列的原理與特點,分析了利用時間序列分析中廣泛使用的ARIMA的原理以及模型識別的標準,并給出了預測的步驟與預測的評價標準。其次,本文對每小時話務量的數(shù)據(jù)特性進行分析,并基于分析給出預測話務量的模型。研究了話務量預測中乘積季節(jié)ARIMA模型的應用條件,給出了模型識別、階數(shù)確定與殘差校驗等建模步驟。并應用STL方法將話務量時間序列分解為季節(jié)項、趨勢項以及隨機項,對序列進行季節(jié)性調整后再用ETS模型擬合,預測時以最近一周期的數(shù)據(jù)作為季節(jié)項的預測結果。此外還分析了Holt-Winters加法和乘法模型,并探討了利用BP神經(jīng)網(wǎng)絡訓練建模并預測的過程。本文應用這四種模型對某運營商在特定景區(qū)的話務量及終端數(shù)量進行預測,實驗表明預測精度良好,能夠滿足移動網(wǎng)絡性能優(yōu)化要求,通過代碼實現(xiàn)并應用于運營商核心網(wǎng)的網(wǎng)絡優(yōu)化中。最后,本文在利用LTE網(wǎng)絡用戶面深度包分析獲得特定區(qū)域及建筑物內(nèi)用戶可感知的Wi Fi物理地址及RSSI后,通過數(shù)據(jù)積累得到高層樓宇的Wi Fi信息,構建Wi Fi的能量矩陣,矩陣的元素是采樣點采集到的Wi Fi的RSSI值。兩個Wi Fi的相關系數(shù)是能量矩陣對應的Wi Fi列之間的相關性,進而獲得Wi Fi的相關矩陣。應用k-means、PAM、譜聚類與Fast Unfolding算法對Wi Fi樣本進行聚類分析,獲得三個Wi Fi簇。在確定底層簇后,利用簇間相關性確定其他簇,得到Wi Fi的高度標簽。最后依據(jù)LTE終端測量的RSRP的強度確定移動用戶的信號覆蓋質量。實驗結果具備較高精度并能滿足無線側網(wǎng)絡優(yōu)化需求。
[Abstract]:Virtualization and container technology make the distribution of core network more and more flexible and efficient. Therefore, it is necessary for the network to be able to perceive and predict the volume of business in advance. By predicting the traffic volume and the number of users and rationally distributing the limited network resources according to the predicted results, the quality of service can be improved effectively. On the other hand, with the increase of the high level of urban buildings in the city The optimization of depth coverage, especially the vertical coverage, is becoming more and more important, but it is also a difficult problem for the tester to be unable to enter the high-rise building test. To find a method that can effectively use the user data to judge the quality of the signal coverage in the high-rise building is still blank in the academic field and industry. First of all, this paper introduces LTE The system core network and the network architecture of the access network, give the control surface signaling detection through the LTE system interface, and then use the control surface signaling and user surface data to analyze the process of getting the number of users, the traffic volume and the user perception of the Wi Fi information. This paper also studies the principle and characteristics of the time series, and analyzes the analysis of the time series analysis. The principle of ARIMA and the standard of model recognition are widely used, and the prediction steps and evaluation criteria are given. Secondly, this paper analyzes the data characteristics of the traffic volume per hour, and gives a model for predicting the traffic volume based on the analysis. The application conditions of the product season ARIMA model in the prediction of traffic volume are studied, and the model is given. The modeling steps of pattern recognition, order determination and residual check are used to decompose the time series of traffic volume into seasonal, trend and random terms, and then the sequence is seasonally adjusted and then fitted with ETS model, and the prediction results of the latest period are predicted with the data of the latest period. In addition, the Holt-Winters addition is also analyzed. And the multiplication model, and the process of training modeling and prediction using BP neural network. This paper uses these four models to predict the traffic volume and terminal number of a certain operator in a specific scenic area. The experiment shows that the prediction accuracy is good and can meet the requirements of the performance optimization of the mobile network, and it is implemented and applied to the core network of the operators through the code. Finally, after using the LTE network user surface depth packet analysis to obtain the perceived Wi Fi physical address and RSSI in the specific area and building, the Wi Fi information of the high-rise building is obtained through the accumulation of data, and the energy matrix of the Wi Fi is constructed. The element of the matrix is the RSSI value of the Wi Fi collected by the sampling point. Two Wi The correlation coefficient is the correlation between the Wi Fi columns corresponding to the energy matrix, and then the correlation matrix of the Wi Fi is obtained. K-means, PAM, spectral clustering and Fast Unfolding algorithm are used to cluster analysis of Wi Fi samples, and three Wi Fi clusters are obtained. According to the intensity of RSRP measured by the LTE terminal, the coverage quality of mobile users is determined. The experimental results are of high accuracy and can satisfy the optimization requirements of wireless side network.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN929.5
本文編號：2139429
[Abstract]:Virtualization and container technology make the distribution of core network more and more flexible and efficient. Therefore, it is necessary for the network to be able to perceive and predict the volume of business in advance. By predicting the traffic volume and the number of users and rationally distributing the limited network resources according to the predicted results, the quality of service can be improved effectively. On the other hand, with the increase of the high level of urban buildings in the city The optimization of depth coverage, especially the vertical coverage, is becoming more and more important, but it is also a difficult problem for the tester to be unable to enter the high-rise building test. To find a method that can effectively use the user data to judge the quality of the signal coverage in the high-rise building is still blank in the academic field and industry. First of all, this paper introduces LTE The system core network and the network architecture of the access network, give the control surface signaling detection through the LTE system interface, and then use the control surface signaling and user surface data to analyze the process of getting the number of users, the traffic volume and the user perception of the Wi Fi information. This paper also studies the principle and characteristics of the time series, and analyzes the analysis of the time series analysis. The principle of ARIMA and the standard of model recognition are widely used, and the prediction steps and evaluation criteria are given. Secondly, this paper analyzes the data characteristics of the traffic volume per hour, and gives a model for predicting the traffic volume based on the analysis. The application conditions of the product season ARIMA model in the prediction of traffic volume are studied, and the model is given. The modeling steps of pattern recognition, order determination and residual check are used to decompose the time series of traffic volume into seasonal, trend and random terms, and then the sequence is seasonally adjusted and then fitted with ETS model, and the prediction results of the latest period are predicted with the data of the latest period. In addition, the Holt-Winters addition is also analyzed. And the multiplication model, and the process of training modeling and prediction using BP neural network. This paper uses these four models to predict the traffic volume and terminal number of a certain operator in a specific scenic area. The experiment shows that the prediction accuracy is good and can meet the requirements of the performance optimization of the mobile network, and it is implemented and applied to the core network of the operators through the code. Finally, after using the LTE network user surface depth packet analysis to obtain the perceived Wi Fi physical address and RSSI in the specific area and building, the Wi Fi information of the high-rise building is obtained through the accumulation of data, and the energy matrix of the Wi Fi is constructed. The element of the matrix is the RSSI value of the Wi Fi collected by the sampling point. Two Wi The correlation coefficient is the correlation between the Wi Fi columns corresponding to the energy matrix, and then the correlation matrix of the Wi Fi is obtained. K-means, PAM, spectral clustering and Fast Unfolding algorithm are used to cluster analysis of Wi Fi samples, and three Wi Fi clusters are obtained. According to the intensity of RSRP measured by the LTE terminal, the coverage quality of mobile users is determined. The experimental results are of high accuracy and can satisfy the optimization requirements of wireless side network.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN929.5
【參考文獻】
相關期刊論文 前3條
1 吳衛(wèi)江;李沐南;李國和;;Louvain算法的并行化處理[J];計算機與數(shù)字工程;2016年08期
2 王佳敏;張紅燕;;基于ARIMA-SVM組合模型的移動通信用戶數(shù)預測[J];計算機時代;2014年09期
3 陳傳鐘;汪文俊;繆光美;;基于缺失值的海南旅游總收入的季節(jié)ARIMA模型及預測[J];海南師范大學學報(自然科學版);2014年01期
相關碩士學位論文 前3條
1 林國華;時間序列分析法在移動通信數(shù)據(jù)分析中的研究與應用[D];廣東工業(yè)大學;2013年
2 田萌;移動數(shù)據(jù)業(yè)務話務分析及預測[D];北京郵電大學;2012年
3 陳蓉;話務量分析和多種預測模型的比較研究[D];北京郵電大學;2008年
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