本文選題：蜂窩網(wǎng)絡(luò) + 無線資源��； 參考：《北京郵電大學(xué)》2016年碩士論文

【摘要】：隨著移動(dòng)通信產(chǎn)業(yè)的高速發(fā)展,蜂窩網(wǎng)絡(luò)已步入大規(guī)模商用4G時(shí)代,移動(dòng)用戶數(shù)量和業(yè)務(wù)量指數(shù)增長,而移動(dòng)互聯(lián)網(wǎng)技術(shù)的發(fā)展和數(shù)據(jù)采集技術(shù)的逐步完善也促使運(yùn)營商和設(shè)備商可以采集到更多維度、更加詳盡的復(fù)雜網(wǎng)絡(luò)數(shù)據(jù)。隨著蜂窩無線網(wǎng)絡(luò)大數(shù)據(jù)時(shí)代的到來,如何結(jié)合大數(shù)據(jù)技術(shù)挖掘蜂窩網(wǎng)絡(luò)中的海量數(shù)據(jù),如何科學(xué)有效地利用已有數(shù)據(jù)進(jìn)行無線資源管理,從而應(yīng)對新形勢下網(wǎng)絡(luò)建設(shè)與優(yōu)化的挑戰(zhàn),已成為時(shí)下的一個(gè)研究熱點(diǎn)。LTE網(wǎng)絡(luò)擁有截然不同的話務(wù)模型和業(yè)務(wù)模型,傳統(tǒng)的數(shù)據(jù)分析平臺(tái)已經(jīng)不適用如今的蜂窩無線網(wǎng)絡(luò)。本論文著重研究了蜂窩網(wǎng)絡(luò)的無線資源預(yù)測方法以及新的數(shù)據(jù)分析平臺(tái)實(shí)現(xiàn)。論文的主要內(nèi)容如下:第一、基于國內(nèi)典型城市的LTE蜂窩網(wǎng)絡(luò)數(shù)據(jù),詳細(xì)分析了數(shù)據(jù)的類型、特點(diǎn)和規(guī)律。細(xì)致調(diào)研和總結(jié)了無線資源預(yù)測相關(guān)的典型數(shù)據(jù)挖掘算法,重點(diǎn)介紹了聚類算法和時(shí)間序列預(yù)測算法的原理和特點(diǎn),深入研究了這兩類算法在通信領(lǐng)域的具體應(yīng)用。第二、依照聚類和時(shí)間序列預(yù)測兩類數(shù)據(jù)挖掘算法,結(jié)合蜂窩網(wǎng)絡(luò)無線資源的特點(diǎn),提出了一個(gè)基于聚類的蜂窩無線資源時(shí)序預(yù)測模型。模型首先利用聚類算法對基站進(jìn)行分類,再對典型類型的基站利用時(shí)序預(yù)測算法進(jìn)行針對性無線資源的預(yù)測,從而找出各類基站的優(yōu)選預(yù)測算法。同時(shí),詳細(xì)介紹了數(shù)據(jù)處理,建模和預(yù)測的處理流程,通過聚類結(jié)果對基站進(jìn)行針對性的時(shí)間序列預(yù)測,總體準(zhǔn)確率可以提升15%以上。第三、基于4G蜂窩網(wǎng)絡(luò)數(shù)據(jù)特點(diǎn)和理論模型建立了一個(gè)面向蜂窩網(wǎng)絡(luò)無線資源數(shù)據(jù)分析平臺(tái),依托傳統(tǒng)數(shù)據(jù)庫技術(shù)及大數(shù)據(jù)技術(shù)設(shè)計(jì)數(shù)據(jù)倉庫從而實(shí)現(xiàn)了各大典型城市數(shù)十億條級別數(shù)據(jù)的實(shí)時(shí)分析和處理�；趯Ａ糠涓C無線資源數(shù)據(jù)的詳細(xì)分析和深入挖掘,平臺(tái)不僅能夠?qū)o線資源基礎(chǔ)數(shù)據(jù)做多維度的可視化展示,還結(jié)合相關(guān)數(shù)據(jù)挖掘算法動(dòng)態(tài)地給出預(yù)測結(jié)果及相關(guān)圖表,揭示了無線資源數(shù)據(jù)間的內(nèi)部規(guī)律和關(guān)聯(lián),為網(wǎng)絡(luò)建設(shè)和優(yōu)化提供了有力的參照依據(jù)。
[Abstract]:With the rapid development of mobile communication industry, cellular network has entered the era of large-scale commercial 4G, and the number of mobile users and business volume has increased exponentially. The development of mobile Internet technology and the gradual improvement of data acquisition technology also promote operators and equipment to collect more dimensions, more detailed complex network data. With the arrival of the era of big data, how to mine the massive data in the cellular network, how to use the existing data scientifically and effectively to manage the wireless resources, In order to meet the challenges of network construction and optimization under the new situation, LTE network has become a research hotspot. LTE network has different traffic model and service model. The traditional data analysis platform is no longer suitable for today's cellular wireless network. This paper focuses on the cellular network wireless resource prediction method and the implementation of a new data analysis platform. The main contents of this paper are as follows: first, based on the LTE cellular network data of typical cities in China, the types, characteristics and laws of the data are analyzed in detail. The typical data mining algorithms related to wireless resource prediction are investigated and summarized in detail. The principles and characteristics of clustering algorithm and time series prediction algorithm are introduced, and the specific applications of these two algorithms in the field of communication are deeply studied. Secondly, according to two kinds of data mining algorithms, clustering and time series prediction, combined with the characteristics of wireless resources in cellular networks, a clustering based time series prediction model for cellular wireless resources is proposed. The model first classifies the base stations by clustering algorithm, and then uses the time series prediction algorithm to predict the targeted wireless resources, and then finds out the optimal selection and prediction algorithm of all kinds of base stations. At the same time, the processing flow of data processing, modeling and prediction is introduced in detail. The overall accuracy can be improved by more than 15% through clustering results to predict the time series of base stations. Thirdly, based on the characteristics of 4G cellular network data and theoretical model, a wireless resource data analysis platform for cellular network is established. Based on the traditional database technology and big data technology, the data warehouse is designed to realize the real-time analysis and processing of billions of data in typical cities. Based on the detailed analysis and deep mining of massive cellular wireless resource data, the platform can not only visualize the basic data of wireless resources in many dimensions, but also dynamically give the prediction results and related charts combined with the related data mining algorithm. The internal rules and correlation of wireless resource data are revealed, which provides a powerful reference for network construction and optimization.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP311.13;TN929.5

【參考文獻(xiàn)】

相關(guān)期刊論文 前6條

1 李紅梅;馮晨;;北京市基于CPI的通貨膨脹分析與預(yù)測[J];全國商情;2014年16期

2 張俊;;移動(dòng)通信網(wǎng)絡(luò)中大數(shù)據(jù)處理的關(guān)鍵技術(shù)研究[J];電信網(wǎng)技術(shù);2014年04期

3 王朝暉;程貴鋒;;2014年國內(nèi)市場LTE手機(jī)發(fā)展分析[J];移動(dòng)通信;2013年23期

4 許春玲;張廣泉;;分布式文件系統(tǒng)Hadoop HDFS與傳統(tǒng)文件系統(tǒng)Linux FS的比較與分析[J];蘇州大學(xué)學(xué)報(bào)(工科版);2010年04期

5 耿偉;劉振海;孫磊;;Struts2框架技術(shù)的研究與分析[J];電腦知識(shí)與技術(shù);2008年33期

6 胡憲華;吳捷;;基于時(shí)間序列的移動(dòng)通信話務(wù)預(yù)測[J];移動(dòng)通信;2006年10期

相關(guān)博士學(xué)位論文 前3條

1 張?jiān)卢?基于QoE的無線資源管理算法研究[D];北京郵電大學(xué);2013年

2 蘇曦;基于認(rèn)知系統(tǒng)中頻譜特征的動(dòng)態(tài)頻譜分配與接入機(jī)制、資源優(yōu)化方法研究[D];北京郵電大學(xué);2010年

3 章輝;蜂窩小區(qū)干擾抑制與性能增強(qiáng)技術(shù)研究[D];北京郵電大學(xué);2010年

相關(guān)碩士學(xué)位論文 前6條

1 孫忠;基于WEB技術(shù)的糧食購銷信息管理系統(tǒng)設(shè)計(jì)與實(shí)現(xiàn)[D];湖南大學(xué);2014年

2 李董華;TD-LTE核心網(wǎng)技術(shù)探討[D];華南理工大學(xué);2013年

3 李孟華;蜂窩網(wǎng)絡(luò)D2D資源共享優(yōu)化及干擾抑制算法研究[D];電子科技大學(xué);2013年

4 祝曉悅;蜂窩網(wǎng)絡(luò)下D2D通信性能的研究[D];北京郵電大學(xué);2013年

5 王佩佩;基于LTE制式多運(yùn)營商網(wǎng)絡(luò)共享的無線資源管理算法研究[D];北京郵電大學(xué);2013年

6 王威;MySQL數(shù)據(jù)庫源代碼分析及存儲(chǔ)引擎的設(shè)計(jì)[D];南京郵電大學(xué);2012年

，

本文編號(hào)：1857058