移動(dòng)網(wǎng)絡(luò)優(yōu)化中業(yè)務(wù)量預(yù)測(cè)及移動(dòng)用戶高度分層方法研究
發(fā)布時(shí)間：2018-07-23 12:35
【摘要】：虛擬化和容器技術(shù)使得核心網(wǎng)資源分配越來(lái)越靈活高效。因此需要網(wǎng)絡(luò)能夠提前對(duì)業(yè)務(wù)量進(jìn)行感知和預(yù)測(cè)。通過(guò)預(yù)測(cè)話務(wù)量和用戶數(shù)并按照預(yù)測(cè)的結(jié)果合理分配有限的網(wǎng)絡(luò)資源,能有效提高服務(wù)質(zhì)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(lái)越重要,但測(cè)試人員無(wú)法進(jìn)入高層建筑測(cè)試也是這一問(wèn)題的難點(diǎn)。尋求能有效的利用用戶數(shù)據(jù)來(lái)判斷高層樓宇內(nèi)的信號(hào)覆蓋質(zhì)量的方法,在學(xué)術(shù)領(lǐng)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(luò)的網(wǎng)絡(luò)架構(gòu),給出通過(guò)LTE系統(tǒng)接口進(jìn)行控制面信令探測(cè),進(jìn)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(wù)量和用戶感知的Wi Fi信息的過(guò)程。本文還研究了時(shí)間序列的原理與特點(diǎn),分析了利用時(shí)間序列分析中廣泛使用的ARIMA的原理以及模型識(shí)別的標(biāo)準(zhǔn),并給出了預(yù)測(cè)的步驟與預(yù)測(cè)的評(píng)價(jià)標(biāo)準(zhǔn)。其次,本文對(duì)每小時(shí)話務(wù)量的數(shù)據(jù)特性進(jìn)行分析,并基于分析給出預(yù)測(cè)話務(wù)量的模型。研究了話務(wù)量預(yù)測(cè)中乘積季節(jié)ARIMA模型的應(yīng)用條件,給出了模型識(shí)別、階數(shù)確定與殘差校驗(yàn)等建模步驟。并應(yīng)用STL方法將話務(wù)量時(shí)間序列分解為季節(jié)項(xiàng)、趨勢(shì)項(xiàng)以及隨機(jī)項(xiàng),對(duì)序列進(jìn)行季節(jié)性調(diào)整后再用ETS模型擬合,預(yù)測(cè)時(shí)以最近一周期的數(shù)據(jù)作為季節(jié)項(xiàng)的預(yù)測(cè)結(jié)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(luò)訓(xùn)練建模并預(yù)測(cè)的過(guò)程。本文應(yīng)用這四種模型對(duì)某運(yùn)營(yíng)商在特定景區(qū)的話務(wù)量及終端數(shù)量進(jìn)行預(yù)測(cè),實(shí)驗(yàn)表明預(yù)測(cè)精度良好,能夠滿足移動(dòng)網(wǎng)絡(luò)性能優(yōu)化要求,通過(guò)代碼實(shí)現(xiàn)并應(yīng)用于運(yùn)營(yíng)商核心網(wǎng)的網(wǎng)絡(luò)優(yōu)化中。最后,本文在利用LTE網(wǎng)絡(luò)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(guò)數(shù)據(jù)積累得到高層樓宇的Wi Fi信息,構(gòu)建Wi Fi的能量矩陣,矩陣的元素是采樣點(diǎn)采集到的Wi Fi的RSSI值。兩個(gè)Wi Fi的相關(guān)系數(shù)是能量矩陣對(duì)應(yīng)的Wi Fi列之間的相關(guān)性,進(jìn)而獲得Wi Fi的相關(guān)矩陣。應(yīng)用k-means、PAM、譜聚類(lèi)與Fast Unfolding算法對(duì)Wi Fi樣本進(jìn)行聚類(lèi)分析,獲得三個(gè)Wi Fi簇。在確定底層簇后,利用簇間相關(guān)性確定其他簇,得到Wi Fi的高度標(biāo)簽。最后依據(jù)LTE終端測(cè)量的RSRP的強(qiáng)度確定移動(dòng)用戶的信號(hào)覆蓋質(zhì)量。實(shí)驗(yàn)結(jié)果具備較高精度并能滿足無(wú)線側(cè)網(wǎng)絡(luò)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(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TN929.5
本文編號(hào)：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(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TN929.5
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