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新時代·技術新未來移動通信大數據分析:數據挖掘與機器學習實戰 版權信息
- ISBN：9787302541240
- 條形碼：9787302541240 ; 978-7-302-54124-0
- 裝幀：80g膠版紙
- 冊數：暫無
- 重量：暫無
- 所屬分類：>>
新時代·技術新未來移動通信大數據分析:數據挖掘與機器學習實戰 本書特色
全彩印刷， 4G/5G無線技術、機器學習和數據挖掘的新研究和新應用。亞信科技董事長田溯寧博士，AT&T(美國電話電報)公司首席科學家大衛·貝蘭格博士聯袂推薦本書作者在移動通信領域擁有豐富的技術管理經驗，親身經歷、領導并實踐了過去10年中通信領域的數據科學在美國通信運營商蓬勃發展的歷程。本書的內容以數據科學和移動通信網絡理論為基礎，應用于運營商真實的業務場景，將通信大數據與機器學習算法技術深入地應用于通信運營商網絡領域與業務領域的各種實際案例中。書中的每一個通信場景案例都用實證分析和量化數據分析的形式呈現，作者將通信網絡與業務領域的知識與機器學習算法相結合，演繹并推導出量化可執行的決策，為運營商探索數字化時代以數據驅動網絡與業務運營提供了很多寶貴的經驗總結。 —— 亞信科技董事長 田溯寧博士 作為通信業與數據科學領域的一名老兵，我見證了全世界通信領域的大數據分析在過去20年的發展。本書是一本里程碑式的著作，系統總結了數據分析如何賦能美國通信業網絡和業務領域的成果。歐陽曄博士不僅是我學術上緊密的合作者，也是美國威瑞森電信的Fellow和通信人工智能系統部經理。我相信他對通信數據科學技術的探索旅程會對我們通信業的同行們有所啟發，激勵著同人們利用數據科學對移動通信技術的演進持續賦能。 —— AT&T(美國電話電報)公司首席科學家 大衛·貝蘭格博士
新時代·技術新未來移動通信大數據分析:數據挖掘與機器學習實戰 內容簡介
本書以4G/5G無線技術、機器學習和數據挖掘的新研究和新應用為基礎，對分析方法和案例進行研究；從工程和社會科學的角度，提高讀者對行業的洞察力，提升運營商的運營效益。本書利用機器學習和數據挖掘技術，研究移動網絡中傳統方法無法解決的問題，包括將數據科學與移動網絡技術進行完美結合的方法、解決方案和算法。 本書可以作為研究生、本科生、科研人員、移動網絡工程師、業務分析師、算法分析師、軟件開發工程師等的參考書，具有很強的實踐指導意義，是的專業著作。
新時代·技術新未來移動通信大數據分析:數據挖掘與機器學習實戰 目錄
第1章概述
1.1 電信業大數據分析 ···························1
1.2 電信大數據分析的驅動力 ················2
1.3 大數據分析對電信產業價值鏈的
益處 ··················································3
1.4 電信大數據的實現范圍····················4
1.4.1 網絡分析 ···················································5
1.4.2 用戶與市場分析 ·······································8
1.4.3 創新的商業模式 ·······································91.5 本書概要 ··········································9
參考文獻 ·················································10
第2章電信分析方法論
2.1 回歸方法 ········································12
2.1.1 線性回歸 ··················································13
2.1.2 非線性回歸 ··············································15
2.1.3 特征選擇 ··················································16
2.2 分類方法 ········································18
2.2.1 邏輯回歸 ··················································18
2.2.2 其他分類方法 ··········································19
2.3 聚類方法 ········································20
2.3.1 K均值聚類 ··············································21
2.3.2 高斯混合模型 ··········································23
2.3.3 其他聚類方法 ··········································24
2.3.4 聚類方法在電信數據中的應用 ·················25
2.4 預測方法 ········································25
2.4.1 時間序列分解 ··········································26
2.4.2 指數平滑模型 ··········································27
2.4.3 ARIMA模型 ············································28
2.5 神經網絡和深度學習 ·····················29
2.5.1 神經網絡 ··················································29
2.5.2 深度學習 ··················································31
2.6 強化學習 ········································32
2.6.1 模型和策略 ··············································33
2.6.2 強化學習算法 ··········································33
參考文獻 ·················································34
XII
XII
第3章 LTE網絡性能趨勢分析
3.1 網絡性能預測策略 ·························39
3.1.1 直接預測策略 ··········································39
3.1.2 分析模型 ··················································39
3.2 網絡資源與性能指標之間的關系 ···40
3.2.1 LTE網絡KPI與資源之間的關系 ···········40
3.2.2 回歸模型 ··················································41
3.3 網絡資源預測 ·································43
3.3.1 LTE網絡流量與資源預測模型 ···············43
3.3.2 預測網絡資源 ··········································43
3.4 評估RRC連接建立的應用 ············46
3.4.1 數據準備與特征選取 ······························46
3.4.2 LTE KPI與網絡資源之間的關系推導 ····47
3.4.3 預測RRC連接建立成功率 ·····················49
參考文獻 ·················································50
第4章熱門設備就緒和返修率分析
4.1 設備返修率與設備就緒的預測
策略 ················································53
4.2 設備返修率和就緒預測模型 ··········54
4.2.1 預測模型的移動通信服務 ························54
4.2.2 參數獲取與存儲 ······································55
4.2.3 分析引擎 ··················································56
4.3 實現和結果 ·····································58
4.3.1 設備返修率預測 ······································58
4.3.2 設備就緒預測 ··········································62
第5章 VoLTE語音質量評估
5.1 應用POLQA評估語音質量··········68
5.1.1 POLQA標準···········································68
5.1.2 語音質量評價中的可擴展性和
可診斷性 ··················································69
5.2 CrowdMi方法論 ····························69
5.2.1 基于RF特征的分類 ·······························70
5.2.2 網絡指標選擇與聚類 ······························70
5.2.3 網絡指標與POLQA評分之間的關系····70
5.2.4 模型測試 ··················································70
5.3 CrowdMi中的技術細節 ·················71
5.3.1 記錄分類 ··················································71
5.3.2 網絡指標的選擇 ······································71
5.3.3 聚類 ·························································72
5.3.4 回歸 ·························································73
5.4 CrowdMi原型設計與試驗 ·············74
5.4.1 客戶端和服務器架構 ······························74
5.4.2 測試和結果 ··············································76
參考文獻 ·················································78
目 錄XIII
目 錄XIII
第6章移動APP無線資源使用分析
6.1 起因和系統概述 ·····························80
6.1.1 背景和挑戰 ··············································80
6.1.2 移動資源管理 ··········································81
6.1.3 系統概述 ··················································82
6.2 AppWiR眾包工具 ··························83
6.3 AppWiR挖掘算法 ··························84
6.3.1 網絡指標的選擇 ······································84
6.3.2 LOESS方法 ············································87
6.3.3 基于時間序列的網絡資源使用預測 ·······87
6.4 實現和試驗 ·····································88
6.4.1 數據收集與研究 ······································88
6.4.2 結果和準確度 ··········································89
參考文獻 ·················································91
第7章電信數據的異常檢測
7.1 模型 ················································93
7.1.1 高斯模型 ··················································94
7.1.2 時間依賴的高斯模型 ······························94
7.1.3 高斯混合模型（GMM）·························95
7.1.4 時間依賴的高斯混合模型 ·······················95
7.1.5 高斯概率潛在語義模型（GPLSA）·······95
7.2 模型對比 ········································97
7.2.1 樣本定義 ··················································97
7.2.2 異常識別 ··················································98
7.2.3 時間依賴GMM與GPLSA的對比 ·········997.3 仿真與討論 ···································100
參考文獻 ···············································103
第8章基于大數據分析的LTE網絡自優化
8.1 SON（自組織網絡）···················105
8.2 APP-SON ······································107
8.3 APP-SON架構 ·····························108
8.4 APP-SON算法 ·····························110
8.4.1 匈牙利算法輔助聚類（HAAC）··········111
8.4.2 單位回歸輔助聚類數的確定 ·················114
8.4.3 基于DNN的回歸·································114
8.4.4 每個小區在時序空間的標簽組合 ·········116
8.4.5 基于相似性的參數調整 ·························1168.5 仿真與討論 ···································117
參考文獻 ···············································122
第9章電信數據和市場營銷
9.2.1 數據采集和數據類型 ····························130
9.1 電信營銷專題 ·······························127
9.2.2 網絡的提取和管理 ································131
9.2 社交網絡的總體構建 ···················130
9.3 網絡結構的度量 ···························133
參考文獻 ···············································135
9.4 網絡中的消費者行為建模 ············134
第10章傳染式客戶流失
10.1 問題引入 ·····································138
10.1.1 流失率問題 ··········································138
10.1.2 社交學習和網絡效應 ··························139
10.2 網絡數據的處理 ·························141
10.3 動態模型 ·····································143
10.3.1 模型介紹 ··············································143
10.3.2 模型的定義 ··········································144
10.3.3 自身經驗建模、社交學習和
社交網絡效應 ······································146
10.3.4 模型估計 ··············································148
10.4 結果 ············································149
參考文獻 ···············································151
第11章基于社交網絡的精準營銷
11.1 網絡效應的渠道 ·························158
11.2 社交網絡數據處理 ·····················159
11.3 建模策略問題 ·····························160
11.3.1 線性空間自回歸模式 ···························160
11.3.2 社交網絡交互模型 ······························162
11.3.3 內生同伴效應 ······································162
11.4 發現與應用 ·································164
11.4.1 結果的解釋 ··········································164
11.4.2 基于社交網絡的精準營銷 ···················165
參考文獻 ···············································168
第12章社交影響和動態社交網絡結構
12.1 動態模型 ·····································17712.1.1 連續時間馬爾可夫模型假設 ···············17712.1.2 模型估計與識別 ··································17912.1.3 網絡結構對社交影響的多元分析 ·······18012.2 研究發現總結 ·····························18112.2.1 隨機行動者動態網絡模型的
估計結果··············································182
12.2.2 元回歸分析結果 ··································184
12.2.3 策略模擬 ··············································18812.3 結論 ············································193
參考文獻 ···············································194
展開全部
新時代·技術新未來移動通信大數據分析:數據挖掘與機器學習實戰 作者簡介
第一作者簡介 歐陽曄 博士 亞信科技首席技術官、高級副總裁 歐陽曄博士目前全面負責亞信科技的技術與產品的研究、開發與創新工作。加入亞信科技之前，歐陽曄博士曾任職于美國第一大移動通信運營商威瑞森電信（Verizon）集團，擔任通信人工智能系統部經理，是威瑞森電信的Fellow。歐陽曄博士在移動通信領域擁有豐富的研發與大型團隊管理經驗，工作中承擔過科學家、研究員、研發經理、大型研發團隊負責人等多個角色。歐陽曄博士專注于移動通信、數據科學與人工智能領域跨學科研究，致力于5G網絡智能化、BSS/OSS融合、通信人工智能、網絡切片、MEC、網絡體驗感知、網絡智能優化、5G行業賦能、云網融合等領域的研發創新與商業化。
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