本文選題：話務(wù)量 + 小波變換�。� 參考：《重慶大學(xué)》2015年碩士論文

【摘要】：進入21世紀以后,隨著科技和網(wǎng)絡(luò)的發(fā)展,我國通信行業(yè)迅速壯大,特別是移動、聯(lián)通和電信三大運營商,其網(wǎng)絡(luò)規(guī)模不斷擴大,業(yè)務(wù)種類多種多樣,客戶數(shù)量大量增加。在這個行業(yè)中,話務(wù)量是一個重要概念,它的大小不僅關(guān)系到客戶的通信質(zhì)量,而且為運營商提供了發(fā)展的依據(jù)。雖然地面上基站數(shù)量的多少和運營商網(wǎng)絡(luò)設(shè)備的性能以及用戶通話次數(shù)直接決定了話務(wù)量的大小,但是我們要做的是根據(jù)已知的話務(wù)量歷史數(shù)據(jù)如何提前準確地預(yù)測話務(wù)量大小,以便及時地作出調(diào)整,避免潛在的風(fēng)險。話務(wù)量歷史數(shù)據(jù)作為一種時間序列,我們可以用很多成熟的時間序列模型來進行預(yù)測,但是如何更加準確地來進行預(yù)測是我們研究的重要課題。在本文中把小波變換思想引入到ARMA模型中來對其進行改進而后預(yù)測�；诖�,本文的主要研究內(nèi)容如下:①首先詳細介紹了小波變換思想和單支重構(gòu)算法。②著重分析了話務(wù)量的幾種預(yù)測模型,主要包括自回歸模型(AR)、滑動平均模型(MA)、自回歸滑動平均模型(ARMA)等。③對已經(jīng)采集的話務(wù)量原始序列進行小波分解,得到近似部分和各細節(jié)部分,并分別單支重構(gòu)到原級別上,對各個重構(gòu)后的序列分別建立ARMA模型,進而對原序列進行預(yù)測。④基于預(yù)測模型的理論分析,本論文也給出常用的ARMA模型對已有數(shù)據(jù)的預(yù)測結(jié)果,并將其結(jié)果與改進模型下的預(yù)測結(jié)果進行了對比研究。⑤系統(tǒng)地提出了通信網(wǎng)絡(luò)話務(wù)量預(yù)測系統(tǒng)的設(shè)計思路。上述幾方面的研究,為通信運營商預(yù)測話務(wù)量及相關(guān)需求分析給出了理論指導(dǎo),也為國內(nèi)通信行業(yè)話務(wù)量需求預(yù)測的工程應(yīng)用作了有益的探索。
[Abstract]:After entering the 21st century, with the development of science and technology and network, the communication industry of our country grows rapidly, especially the three major operators of mobile, Unicom and telecom, whose network scale is expanding constantly, the service types are various, and the number of customers is increasing greatly. In this industry, traffic is an important concept, its size not only relates to the customer's communication quality, but also provides the basis for the development of the operator. Although the number of base stations on the ground and the performance of the operator's network equipment and the number of user calls directly determine the size of the traffic, But what we need to do is how to predict the traffic accurately and ahead of time according to the historical data of known traffic so as to adjust in time and avoid the potential risks. As a kind of time series, traffic history data can be predicted by many mature time series models. However, how to predict more accurately is an important topic of our research. In this paper, wavelet transform is introduced into ARMA model to improve it and then predict it. Based on this, the main research contents of this paper are as follows: firstly, wavelet transform and single-branch reconstruction algorithm .2 are introduced in detail, and several prediction models of traffic are analyzed. It mainly includes autoregressive model, moving average model, autoregressive moving average model and so on. 3. The original sequence of traffic that has been collected is decomposed by wavelet, and the approximate parts and the detail parts are obtained, and each branch is reconstructed to the original level. The ARMA model is established for each reconstructed sequence, and then the theoretical analysis based on the prediction model is carried out for the original sequence. The paper also gives the prediction results of the existing data by the commonly used ARMA model. The results are compared with the prediction results under the improved model. 5. The design idea of the traffic prediction system in the communication network is put forward systematically. The above research provides the theoretical guidance for the communication operators to predict the traffic volume and the related demand analysis, and also makes a beneficial exploration for the engineering application of the traffic demand prediction in the domestic communication industry.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：F626.12;F224

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