【摘要】：隨著中國的進(jìn)一步開放和中國經(jīng)濟(jì)的增長,越來越多的演唱會、會展、賽事等大型賽事活動開始登陸中國,大量人群的短時聚集對無線網(wǎng)絡(luò)的建設(shè)和配置提出了更高的要求。然而如何設(shè)置最合理的硬件配置來達(dá)到信道利用率最大化,仍缺乏有效的話務(wù)預(yù)測方法作為基礎(chǔ),使網(wǎng)絡(luò)資源配置缺乏依據(jù),也使得大型賽事的保障缺乏理論的突破。 大型賽事活動話務(wù)預(yù)測方法要求訓(xùn)練序列短、輸入數(shù)據(jù)類型少,現(xiàn)有預(yù)測方法中時間序列預(yù)測法要求下一時刻的話務(wù)量與當(dāng)前時刻和過去時刻的話務(wù)量存在相關(guān)性,即從長期來看數(shù)據(jù)序列連續(xù)且處于一種穩(wěn)定的趨勢狀態(tài),無法直接用于大型賽事活動的話務(wù)預(yù)測;一般使用的一元線性回歸模型僅將預(yù)測觀眾數(shù)乘以市場占有率計算得到的預(yù)測用戶數(shù)作為自變量,即使不考慮預(yù)測觀眾人數(shù)的準(zhǔn)確性,觀眾并不能完全包含活動區(qū)域的全部用戶,由于區(qū)域長期駐留的用戶數(shù)并不容易準(zhǔn)確統(tǒng)計得到,造成預(yù)測精度不穩(wěn)定。其他預(yù)測方法如灰色系統(tǒng)理論、神經(jīng)網(wǎng)絡(luò)理論等方法過于復(fù)雜,對輸入訓(xùn)練序列的長度要求較高,輸入?yún)?shù)過多且參數(shù)沒有明確的理論依據(jù)來確定,用于長期話務(wù)趨勢預(yù)測尚可,用于大型賽事活動這類短期突發(fā)話務(wù)預(yù)測難度較大。 本文提出了將大型賽事活動期間的話務(wù)量拆分成日常部分和活動部分兩個相互獨立的部分分開進(jìn)行預(yù)測。日常部分具有較多的歷史數(shù)據(jù)可以使用,可以使用如時間序列預(yù)測法、神經(jīng)網(wǎng)絡(luò)預(yù)測法等需要較長訓(xùn)練序列的預(yù)測方法;活動部分為離散數(shù)據(jù),樣本較少,但主要與用戶人數(shù)有關(guān),可適用回歸分析法等預(yù)測方法。最后合并兩個獨立預(yù)測部分得到最終的預(yù)測結(jié)果。這種預(yù)測算法簡單有效,可用于各類大型賽事活動。
[Abstract]:With the further opening of China and the growth of Chinese economy, more and more large-scale events, such as concerts, exhibitions, events and so on, have begun to land in China. The short time gathering of a large number of people has put forward higher requirements for the construction and configuration of wireless networks. However, how to set up the most reasonable hardware configuration to maximize the channel utilization still lacks the effective traffic prediction method as the foundation, makes the network resource allocation lack the basis, also makes the large-scale competition guarantee lacks the theoretical breakthrough. The traffic prediction methods for large-scale events require short training sequence and few input data types. Among the existing prediction methods, the time series prediction method requires the traffic at the next moment to be correlated with the traffic at the current and past moments. That is, the data sequence is continuous and in a stable trend state in the long run, which can not be directly used for traffic prediction of large-scale events. The commonly used linear regression model only takes the predicted audience number multiplied by the market share as the independent variable, even if the accuracy of the forecast audience size is not considered. The audience can not completely include all the users in the active area. Because the number of users residing in the area for a long time is not easy to get accurate statistics, the prediction accuracy is not stable. Other prediction methods, such as grey system theory, neural network theory and so on, are too complicated to require the length of input training sequence, too many input parameters and no clear theoretical basis to determine the parameters. It is very difficult to predict the trend of long term traffic, and to predict the short term emergency traffic such as large-scale events. In this paper, the traffic during the event is divided into two independent parts, the daily part and the activity part, to be predicted separately. The daily part has more historical data that can be used, such as time series prediction method, neural network prediction method and so on. But mainly related to the number of users, regression analysis and other forecasting methods can be applied. Finally, the final prediction results are obtained by merging the two independent forecasting parts. This prediction algorithm is simple and effective and can be used in all kinds of events.
【學(xué)位授予單位】：上海交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2011
【分類號】：TP393.09

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