Self-Similar Processes in Telecommunications
暫譯: 電信中的自相似過程

Oleg Sheluhin, Sergey Smolskiy, Andrew Osin

  • 出版商: Wiley
  • 出版日期: 2007-05-01
  • 售價: $1,580
  • 貴賓價: 9.8$1,548
  • 語言: 英文
  • 頁數: 334
  • 裝訂: Hardcover
  • ISBN: 0470014865
  • ISBN-13: 9780470014868
  • 相關分類: 通訊系統 Communication-systems
  • 立即出貨 (庫存=1)

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Description

For the first time the problems of voice services self-similarity are discussed systematically and in detail with specific examples and illustrations.

Self-Similar Processes in Telecommunications considers the self-similar (fractal and multifractal) models of telecommunication traffic and efficiency based on the assumption that its traffic has fractal or multifractal properties (is self-similar).  The theoretical aspects of the most well-known traffic models demonstrating self-similar properties are discussed in detail and the comparative analysis of the different models’ efficiency for self-similar traffic is presented.

This book demonstrates how to use self-similar processes for designing new telecommunications systems and optimizing existing networks so as to achieve maximum efficiency and serviceability. The approach is rooted in theory, describing the algorithms (the logical arithmetical or computational procedures that define how a task is performed) for modeling these self-similar processes. However, the language and ideas are essentially accessible for those who have a general knowledge of the subject area and the advice is highly practical: all models, problems and solutions are illustrated throughout using numerous real-world examples.

  • Adopts a detailed, theoretical, yet broad-based and practical mathematical approach for designing and operating numerous types of telecommunications systems and networks so as to achieve maximum efficiency
  • Places the subject in context, describing the current algorithms that make up the fractal or self-similar processes while pointing to the future development of the technology
  • Offers a comparative analysis of the different types of self-similar process usage within the context of local area networks, wide area networks and in the modeling of video traffic and mobile communications networks
  • Describes how mathematical models are used as a basis for building numerous types of network, including voice, audio, data, video, multimedia services and IP (Internet Protocol) telephony

The book will appeal to the wide range of specialists dealing with the design and exploitation of telecommunication systems. It will be useful for the post-graduate students, lecturers and researchers connected with communication networks disciplines.

Table of Contents

Foreword.

About the authors.

Acknowledgements.

1 Principal Concepts of Fractal Theory and Self-Similar Processes.

1.1 Fractals and Multifractals.

1.1.1 Fractal Dimension of a Set.

1.1.2 Multifractals.

1.1.3 Fractal Dimension D0 and Informational Dimension D1.

1.1.4 Legendre Transform.

1.2 Self-Similar Processes.

1.2.1 Definitions and Properties of Self-Similar Processes.

1.2.2 Multifractal Processes.

1.2.3 Long-Range and Short-Range Dependence.

1.2.4 Slowly Decaying Variance.

1.3 ‘Heavy Tails’.

1.3.1 Distribution with ‘Heavy Tails’ (DHT).

1.3.2 ‘Heavy Tails’ Estimation.

1.4 Hurst Exponent Estimation.

1.4.1 Time Domain Methods of Hurst Exponent Estimation.

1.4.2 Frequency Domain Methods of Hurst Exponent.

Estimation.

1.5 Hurst Exponent Estimation Problems.

1.5.1 Estimation Problems.

1.5.2 Nonstationarity Problems.

1.5.3 Computational Problems.

1.6 Self-Similarity Origins in Telecommunication Traffic.

1.6.1 User’s Behaviour.

1.6.2 Data Generation Data Structure and Its Search.

1.6.3 Traffic Aggregation.

1.6.4 Means of Network Control.

1.6.5 Control Mechanisms based on Feedback.

1.6.6 Network Development.

References.

2 Simulation Methods for Fractal Processes.

2.1 Fractional Brownian Motion.

2.1.1 RMD Algorithm for FBM Generation.

2.1.2 SRA Algorithm for FBM Generation.

2.2 Fractional Gaussian Noise.

2.2.1 FFT Algorithm for FGN Synthesis.

2.2.2 Advantages and Shortcomings of FBM/FGN Models.

in Network Applications.

2.3 Regression Models of Traffic.

2.3.1 Linear Autoregressive (AR) Processes.

2.3.2 Processes of Moving Average (MA).

2.3.3 Autoregressive Models of Moving Average, ARMAðp; qÞ.

2.3.4 Fractional Autoregressive Integrated Moving Average.

(FARIMA) Process.

2.3.5 Parametric Estimation Methods.

2.3.6 FARIMAðp,d,qÞ Process Synthesis.

2.4 Fractal Point Process.

2.4.1 Statistical Characteristics of the Point Process.

2.4.2 Fractal Structure of FPP.

2.4.3 Methods of FPP Formation.

2.5 Fractional Levy Motion and its Application to Network.

Traffic Modelling.

2.5.1 Fractional Levy Motion and Its Properties.

2.5.2 Algorithm of Fractional Levy Motion Modelling.

2.5.3 Fractal Traffic Formation Based on FLM.

2.6 Models of Multifractal Network Traffic.

2.6.1 Multiplicative Cascades.

2.6.2 Modified Estimation Method of Multifractal Functions.

2.6.3 Generation of Traffic the Multifractal Model.

2.7 LRD Traffic Modelling with the Help of Wavelets.

2.8 M/G/1Model.

2.8.1 M/G/1Model and Pareto Distribution.

2.8.2 M/G/1Model and Log-Normal Distribution.

References.

3 Self-Similarity of Real Time Traffic.

3.1 Self-Similarity of Real Time Traffic Preliminaries.

3.2 Statistical Characteristics of Telecommunication Real Time Traffic.

3.2.1 Measurement Organization.

3.2.2 Pattern of TN Traffic.

3.3 Voice Traffic Characteristics.

3.3.1 Voice Traffic Characteristics at the Call Layer.

3.3.2 Voice Traffic Characteristics at the Packet Layer.

3.4 Multifractal Analysis of Voice Traffic.

3.4.1 Basics.

3.4.2 Algorithm for the Partition Function SmðqÞ Calculation.

3.4.3 Multifractal Properties of Multiplexed Voice Traffic.

3.4.4 Multifractal Properties of Two-Component Voice Traffic.

3.5 Mathematical Models of VoIP Traffic.

3.5.1 Problem Statement.

3.5.2 Voice Traffic Models at the Call Layer.

3.5.3 Estimation of Semi-Markovian Model Parameters and the Modelling.

Results of the Voice Traffic at the Call Layer.

3.5.4 Mathematical Models of Voice Traffic at the Packets Layer.

3.6 Simulation of the Voice Traffic.

3.6.1 Simulation Structure.

3.6.2 Parameters Choice of Pareto Distributions for Voice.

Traffic Source in ns2.

3.6.3 Results of Separate Sources Modelling.

3.6.4 Results of Traffic Multiplexing for the Separate.

ON/OFF Sources.

3.7 Long-Range Dependence for the VBR-Video.

3.7.1 Distinguished Characteristics of Video Traffic.

3.7.2 Video Conferences.

3.7.3 Video Broadcasting.

3.7.4 MPEG Video Traffic.

3.7.5 Nonstationarity of VBR Video Traffic.

3.8 Self-Similarity Analysis of Video Traffic.

3.8.1 Video Broadcasting Wavelet Analysis.

3.8.2 Numerical Results.

3.8.3 Multifractal Analysis.

3.9 Models and Modelling of Video Sequences.

3.9.1 Nonstationarity Types for VBR Video Traffic.

3.9.2 Model of the Video Traffic Scene Changing Based on the.

Shifting Level Process.

3.9.3 Video Traffic Models in the Limits of the Separate Scene.

3.9.4 Fractal Autoregressive Models of p-Order.

3.9.5 MPEG Data Modelling Using I, P and B Frames Statistics.

3.9.6 ON/OFF Model of the Video Sequences.

3.9.7 Self-Similar Norros Model.

3.9.8 Hurst Exponent Dependence on N.

References.

4 Self-Similarity of Telecommunication Networks Traffic.

4.1 Problem Statement.

4.2 Self-Similarity and ‘Heavy Tails’ in Lan Traffic.

4.2.1 Experimental Investigations of Ethernet Traffic Self-Similar.

Structure.

4.2.2 Estimation of Testing Results.

4.3 Self-Similarity of WAN Traffic.

4.3.1 WAN Traffic at the Application Level.

4.3.2 Some Limiting Results for Aggregated WAN Traffic.

4.3.3 The Statistical Analysis of WAN Traffic at the.

Application Level.

4.3.4 Multifractal Analysis of WAN Traffic.

4.4 Self-Similarity of Internet Traffic.

4.4.1 Results of Experimental Studies.

4.4.2 Stationarity Analysis of IP Traffic.

4.4.3 Nonstationarity of Internet Traffic.

4.4.4 Scaling Analysis.

4.5 Multilevel ON/OFF Model of Internet Traffic.

4.5.1 Problem Statement.

4.5.2 Estimation of Parameters and Model Parameterization.

4.5.3 Parallel Buffer Structure for Active Queue Control.

References.

5 Queuing and Performance Evaluation of Telecommunication.

Networks under Traffic Self-Similarity Conditions.

5.1 Traffic Fractality Influence Estimate on Telecommunication.

Networks Queuing.

5.1.1 Monofractal Traffic.

5.1.2 Communication System Model and the Packet Loss Probability.

Estimate for the Asymptotic Self-Similar Traffic Described by.

Pareto Distribution.

5.1.3 Queuing Model with Fractal Levy Motion.

5.1.4 Estimate of the Effect of Traffic Multifractality Effect on Queuing.

5.2 Estimate of Voice Traffic Self-Similarity Effects on the iP Networks.

Input Parameter Optimization.

5.2.1 Problem Statement.

5.2.2 Simulation Structure.

5.2.3 Estimate of the Traffic Self-Similarity Influence on QoS.

5.2.4 TN input Parameter Optimization for Given QoS Characteristics.

5.2.5 Conclusions.

5.3 Telecomminication Network Parameters Optimization Using the Tikhonov.

Regularization Approach.

5.3.1 Problem Statement.

5.3.2 Telecommunication Network Parameter Optimization on the Basis of.

the Minimization of the Discrepancy Functional of QoS Parameters.

5.3.3 Optimization Results.

5.3.4 TN Parameter Optimization on the Basis of Tikhonov.

Functional Minimization.

5.3.5 Regularization Results.

5.3.6 Conclusions.

5.4 Estimation of the Voice Traffic Self-Similarity Influence on QoS.

with Frame Relay Networks.

5.4.1 Pocket Delay at Transmission through the Frame Relay Network.

5.4.2 Frame Relay Router Modelling.

5.4.3 Simulation Results.

5.5 Bandwidth Prediction in Telecommunication Networks.

5.6 Congestion Control of Self-Similar Traffic.

5.6.1 Unimodal Ratio Loading/Productivity.

5.6.2 Selecting Aggressiveness Control (SAC) Scheme.

References.

Appendix A List of Symbols.

Appendix B List of Acronyms.

Index.

商品描述(中文翻譯)

描述

首次系統性且詳細地討論語音服務自相似性問題,並提供具體的例子和插圖。《電信中的自相似過程》考慮了基於其流量具有分形或多重分形特性的假設(即自相似)的電信流量和效率的自相似(分形和多重分形)模型。詳細討論了最著名的顯示自相似性質的流量模型的理論方面,並呈現了不同模型對自相似流量的效率的比較分析。

本書展示了如何利用自相似過程來設計新的電信系統並優化現有網絡,以實現最大效率和可服務性。這種方法根植於理論,描述了建模這些自相似過程的算法(定義任務執行方式的邏輯算術或計算程序)。然而,語言和思想對於對該主題有一般知識的人來說基本上是可接近的,並且建議非常實用:所有模型、問題和解決方案都通過大量的現實世界例子進行說明。

- 採用詳細的理論,但又廣泛且實用的數學方法來設計和運營多種類型的電信系統和網絡,以實現最大效率
- 將主題置於上下文中,描述構成分形或自相似過程的當前算法,同時指向技術的未來發展
- 提供不同類型自相似過程在局域網、廣域網以及視頻流量和移動通信網絡建模中的使用的比較分析
- 描述數學模型如何作為構建多種類型網絡的基礎,包括語音、音頻、數據、視頻、多媒體服務和IP(網際網路協議)電話

本書將吸引廣泛的專家,涉及電信系統的設計和利用。對於與通信網絡學科相關的研究生、講師和研究人員將非常有用。

目錄

前言
關於作者
致謝
1 分形理論和自相似過程的主要概念
1.1 分形和多重分形
1.1.1 集合的分形維度
1.1.2 多重分形
1.1.3 分形維度 D0 和信息維度 D1
1.1.4 勒讓德變換
1.2 自相似過程
1.2.1 自相似過程的定義和性質
1.2.2 多重分形過程
1.2.3 長程和短程依賴
1.2.4 緩慢衰減的方差
1.3 “重尾”
1.3.1 具有“重尾”的分佈(DHT)
1.3.2 “重尾”的估計
1.4 赫斯特指數估計
1.4.1 赫斯特指數的時間域方法
1.4.2 赫斯特指數的頻域方法
1.5 赫斯特指數估計問題
1.5.1 估計問題
1.5.2 非平穩性問題
1.5.3 計算問題
1.6 電信流量中的自相似性起源
1.6.1 用戶行為
1.6.2 數據生成數據結構及其搜索
1.6.3 流量聚合
1.6.4 網絡控制手段
1.6.5 基於反饋的控制機制
1.6.6 網絡發展
參考文獻
2 分形過程的模擬方法
2.1 分數布朗運動
2.1.1 用於FBM生成的RMD算法
2.1.2 用於FBM生成的SRA算法
2.2 分數高斯噪聲
2.2.1 用於FGN合成的FFT算法
2.2.2 FBM/FGN模型在網絡應用中的優缺點
2.3 流量的回歸模型
2.3.1 線性自回歸(AR)過程
2.3.2 移動平均(MA)過程
2.3.3 自回歸移動平均模型,ARMA(p; q)
2.3.4 分數自回歸整合移動平均(FARIMA)過程
2.3.5 參數估計方法
2.3.6 FARIMA(p,d,q)過程合成
2.4 分形點過程
2.4.1 點過程的統計特徵
2.4.2 FPP的分形結構
2.4.3 FPP形成的方法
2.5 分數萊維運動及其在網絡流量建模中的應用
2.5.1 分數萊維運動及其性質
2.5.2 分數萊維運動建模的算法
2.5.3 基於FLM的分形流量形成
2.6 多重分形網絡流量模型
2.6.1 乘法級聯
2.6.2 多重分形函數的修正估計方法
2.6.3 多重分形模型的流量生成
2.7 利用小波進行LRD流量建模
2.8 M/G/1模型
2.8.1 M/G/1模型與Pareto分佈
2.8.2 M/G/1模型與對數正態分佈
參考文獻
3 實時流量的自相似性
3.1 實時流量自相似性的初步研究
3.2 電信實時流量的統計特徵
3.2.1 測量組織
3.2.2 TN流量的模式
3.3 語音流量特徵
3.3.1 呼叫層的語音流量特徵
3.3.2 封包層的語音流量特徵
3.4 語音流量的多重分形分析
3.4.1 基礎
3.4.2 分區函數 Sm(q) 計算的算法
3.4.3 多重分形的多路復用語音流量的性質
3.4.4 兩組分量語音流量的多重分形性質
3.5 VoIP流量的數學模型
3.5.1 問題陳述
3.5.2 呼叫層的語音流量模型
3.5.3 半馬爾可夫模型參數的估計及呼叫層語音流量的建模結果
3.5.4 封包層的語音流量數學模型
3.6 語音流量的模擬
3.6.1 模擬結構
3.6.2 ns2中語音流量源的Pareto分佈參數選擇
3.6.3 單獨源建模的結果
3.6.4 單獨ON/OFF源的流量多路復用結果
3.7 VBR視頻的長程依賴
3.7.1 視頻流量的顯著特徵
3.7.2 視頻會議
3.7.3 視頻廣播
3.7.4 MPEG視頻流量
3.7.5 VBR視頻流量的非平穩性
3.8 視頻流量的自相似性分析
3.8.1 視頻廣播小波分析
3.8.2 數值結果
3.8.3 多重分形分析
3.9 視頻序列的模型和建模
3.9.1 VBR視頻流量的非平穩性類型
3.9.2 基於轉移水平過程的視頻流量場景變化模型
3.9.3 在單獨場景範圍內的視頻流量模型
3.9.4 p階的分形自回歸模型
3.9.5 使用I、P和B幀統計的MPEG數據建模
3.9.6 視頻序列的ON/OFF模型
3.9.7 自相似Norros模型
3.9.8 赫斯特指數對N的依賴
參考文獻
4 電信網絡流量的自相似性
4.1 問題陳述
4.2 LAN流量中的自相似性和“重尾”
4.2.1 以太網流量自相似結構的實驗研究
4.2.2 測試結果的估計
4.3 WAN流量的自相似性
4.3.1 應用層的WAN流量
4.3.2 聚合WAN流量的一些限制結果
4.3.3 應用層的WAN流量的統計分析
4.3.4 WAN流量的多重分形分析
4.4 互聯網流量的自相似性
4.4.1 實驗研究的結果
4.4.2 IP流量的平穩性分析
4.4.3 互聯網流量的非平穩性
4.4.4 縮放分析
4.5 互聯網流量的多層ON/OFF模型
4.5.1 問題陳述
4.5.2 參數估計和模型參數化
4.5.3 用於主動隊列控制的並行緩衝區結構
參考文獻
5 在流量自相似性條件下的電信網絡排隊和性能評估
5.1 流量分形性對電信網絡排隊的影響估計
5.1.1 單分形流量
5.1.2 通信系統模型及其包丟失概率估計
5.1.3 具有分形萊維運動的排隊模型
5.1.4 流量多重分形性對排隊的影響估計
5.2 語音流量自相似性對iP網絡的影響估計
5.2.1 問題陳述
5.2.2 模擬結構
5.2.3 流量自相似性對QoS的影響估計
5.2.4 根據給定QoS特徵的TN輸入參數優化
5.2.5 結論
5.3 使用Tikhonov正則化方法的電信網絡參數優化
5.3.1 問題陳述
5.3.2 基於最小化差異的電信網絡參數優化

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