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

【摘要】：隨著移動通信產(chǎn)業(yè)的高速發(fā)展,蜂窩網(wǎng)絡已步入大規(guī)模商用4G時代,移動用戶數(shù)量和業(yè)務量指數(shù)增長,而移動互聯(lián)網(wǎng)技術的發(fā)展和數(shù)據(jù)采集技術的逐步完善也促使運營商和設備商可以采集到更多維度、更加詳盡的復雜網(wǎng)絡數(shù)據(jù)。隨著蜂窩無線網(wǎng)絡大數(shù)據(jù)時代的到來,如何結(jié)合大數(shù)據(jù)技術挖掘蜂窩網(wǎng)絡中的海量數(shù)據(jù),如何科學有效地利用已有數(shù)據(jù)進行無線資源管理,從而應對新形勢下網(wǎng)絡建設與優(yōu)化的挑戰(zhàn),已成為時下的一個研究熱點。LTE網(wǎng)絡擁有截然不同的話務模型和業(yè)務模型,傳統(tǒng)的數(shù)據(jù)分析平臺已經(jīng)不適用如今的蜂窩無線網(wǎng)絡。本論文著重研究了蜂窩網(wǎng)絡的無線資源預測方法以及新的數(shù)據(jù)分析平臺實現(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)絡數(shù)據(jù),詳細分析了數(shù)據(jù)的類型、特點和規(guī)律。細致調(diào)研和總結(jié)了無線資源預測相關的典型數(shù)據(jù)挖掘算法,重點介紹了聚類算法和時間序列預測算法的原理和特點,深入研究了這兩類算法在通信領域的具體應用。第二、依照聚類和時間序列預測兩類數(shù)據(jù)挖掘算法,結(jié)合蜂窩網(wǎng)絡無線資源的特點,提出了一個基于聚類的蜂窩無線資源時序預測模型。模型首先利用聚類算法對基站進行分類,再對典型類型的基站利用時序預測算法進行針對性無線資源的預測,從而找出各類基站的優(yōu)選預測算法。同時,詳細介紹了數(shù)據(jù)處理,建模和預測的處理流程,通過聚類結(jié)果對基站進行針對性的時間序列預測,總體準確率可以提升15%以上。第三、基于4G蜂窩網(wǎng)絡數(shù)據(jù)特點和理論模型建立了一個面向蜂窩網(wǎng)絡無線資源數(shù)據(jù)分析平臺,依托傳統(tǒng)數(shù)據(jù)庫技術及大數(shù)據(jù)技術設計數(shù)據(jù)倉庫從而實現(xiàn)了各大典型城市數(shù)十億條級別數(shù)據(jù)的實時分析和處理�；趯Ａ糠涓C無線資源數(shù)據(jù)的詳細分析和深入挖掘,平臺不僅能夠?qū)o線資源基礎數(shù)據(jù)做多維度的可視化展示,還結(jié)合相關數(shù)據(jù)挖掘算法動態(tài)地給出預測結(jié)果及相關圖表,揭示了無線資源數(shù)據(jù)間的內(nèi)部規(guī)律和關聯(lián),為網(wǎng)絡建設和優(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.
【學位授予單位】：北京郵電大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP311.13;TN929.5

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