Self-Organizing Networks (eBook, ePUB)

Self-Planning, Self-Optimization and Self-Healing for GSM, UMTS and LTE
Redaktion: Ramiro, Juan; Hamied, Khalid

• Format: ePub

Produktbeschreibung

With the current explosion in network traffic, and mounting pressure on operators' business case, Self-Organizing Networks (SON) play a crucial role. They are conceived to minimize human intervention in engineering processes and at the same time improve system performance to maximize Return-on-Investment (ROI) and secure customer loyalty. Written by leading experts in the planning and optimization of Multi-Technology and Multi-Vendor wireless networks, this book describes the architecture of Multi-Technology SON for GSM, UMTS and LTE, along with the enabling technologies for SON planning, optimization and healing. This is presented mainly from a technology point of view, but also covers some critical business aspects, such as the ROI of the proposed SON functionalities and Use Cases. Key features: * Follows a truly Multi-Technology approach: covering not only LTE, but also GSM and UMTS, including architectural considerations of deploying SON in today's GSM and UMTS networks * Features detailed discussions about the relevant trade-offs in each Use Case * Includes field results of today's GSM and UMTS SON implementations in live networks * Addresses the calculation of ROI for Multi-Technology SON, contributing to a more complete and strategic view of the SON paradigm This book will appeal to network planners, optimization engineers, technical/strategy managers with operators and R&D/system engineers at infrastructure and software vendors. It will also be a useful resource for postgraduate students and researchers in automated wireless network planning and optimization.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 304
• Erscheinungstermin: 27. Oktober 2011
• Englisch
• ISBN-13: 9781119960928
• Artikelnr.: 37349465

Autorenporträt

Dr. Juan Ramiro is currently the Corporate Marketing Director of Optimi, where he has held several technical and managerial positions since the company was founded in 2003. He has ten years of experience in the wireless industry, mostly focused on RAN performance simulation and optimization. Before joining Optimi, he worked for Telefónica I+D, and then he carried out R&D activities about smart antenna systems and radio resource management for UMTS/HSDPA at Aalborg University, Denmark, in close co-operation with Nokia Networks. He is co-author of one international patent, several international patent applications, ten conference papers, two journal papers and contributions to another two books. Dr. Ramiro earned a Master's degree in Telecommunications Engineering from University of Málaga (Spain), with awards to the best student record and the best master thesis; a Ph.D. degree in Wireless Communications (Electrical and Electronic Engineering) from Aalborg University (Denmark); and an Executive MBA degree from Instituto Internacional San Telmo (Spain). Dr. Khalid Hamied is the founder and Chief Technology Officer of Optimi, a leading supplier of advanced planning and optimization solutions for GSM, UMTS and LTE wireless networks. He received a Ph.D. degree in Electrical Engineering from the Georgia Institute of Technology, Atlanta, Georgia, in 1994. His Ph.D. thesis was on Advanced Radio Link Design and Radio Receiver Design for Mobile Communications. In 1994, he joined the Cellular Infrastructure Group of Motorola where he worked on high-speed data for third generation CDMA systems. In August 1997, he joined Mobile Systems International as a Principal Engineer where he developed software planning solutions for CDMA networks. From 1999 to 2001, he was a Senior Staff Engineer in the Wireless Access and Applications Group, Motorola Labs, Arlington Heights, Illinois. Dr. Hamied has twelve refereed papers and two patents. His research interests include coding, modulation and mobile wireless systems.

Inhaltsangabe

Foreword xi Preface xiii Acknowledgements xv List of Contributors xvii List
of Abbreviations xix 1 Operating Mobile Broadband Networks 1 1.1. The
Challenge of Mobile Traffic Growth 1 1.1.1. Differences between Smartphones
3 1.1.2. Driving Data Traffic - Streaming Media and Other Services 5 1.2.
Capacity and Coverage Crunch 5 1.3. Meeting the Challenge - the Network
Operator Toolkit 6 1.3.1. Tariff Structures 6 1.3.2. Advanced Radio Access
Technologies 7 1.3.3. Femto Cells 10 1.3.4. Acquisition and Activation of
New Spectrum 11 1.3.5. Companion Networks, Offloading and Traffic
Management 12 1.3.6. Advanced Source Coding 14 1.4. Self-Organizing
Networks (SON) 16 1.5. Summary and Book Contents 17 1.6. References 19 2
The Self-Organizing Networks (SON) Paradigm 21 2.1. Motivation and Targets
from NGMN 21 2.2. SON Use Cases 23 2.2.1. Use Case Categories 23 2.2.2.
Automatic versus Autonomous Processes 25 2.2.3. Self-Planning Use Cases 25
2.2.4. Self-Deployment Use Cases 26 2.2.5. Self-Optimization Use Cases 28
2.2.6. Self-Healing Use Cases 32 2.2.7. SON Enablers 34 2.3. SON versus
Radio Resource Management 35 2.4. SON in 3GPP 37 2.4.1. 3GPP Organization
37 2.4.2. SON Status in 3GPP (up to Release 9) 38 2.4.3. SON Objectives for
3GPP Release 10 40 2.5. SON in the Research Community 41 2.5.1. SOCRATES:
Self-Optimization and Self-ConfiguRATion in wirelEss networkS 41 2.5.2.
Celtic Gandalf: Monitoring and Self-Tuning of RRM Parameters in a
Multi-System Network 42 2.5.3. Celtic OPERA-Net: Optimizing Power
Efficiency in mobile RAdio Networks 42 2.5.4. E3: End-to-End Efficiency 43
2.6. References 43 3 Multi-Technology SON 47 3.1. Drivers for
Multi-Technology SON 47 3.2. Architectures for Multi-Technology SON 49
3.2.1. Deployment Architectures for Self-Organizing Networks 49 3.2.2.
Comparison of SON Architectures 50 3.2.3. Coordination of SON Functions 53
3.2.4. Layered Architecture for Centralized Multi-Technology SON 59 3.3.
References 64 4 Multi-Technology Self-Planning 65 4.1. Self-Planning
Requirements for 2G, 3G and LTE 65 4.2. Cross-Technology Constraints for
Self-Planning 66 4.3. Self-Planning as an Integrated Process 66 4.4.
Planning versus Optimization 69 4.5. Information Sources for Self-Planning
70 4.5.1. Propagation Path-Loss Predictions 70 4.5.2. Drive Test
Measurements 71 4.6. Automated Capacity Planning 71 4.6.1. Main Inputs for
Automated Capacity Planning 73 4.6.2. Traffic and Network Load Forecast 74
4.6.3. Automated Capacity Planning Process 75 4.6.4. Outputs of the Process
and Implementation of Capacity Upgrades in the Network 78 4.7. Automated
Transmission Planning 79 4.7.1. Self-Organizing Protocols 80 4.7.2.
Additional Requirements for Automated Transmission Planning 82 4.7.3.
Automatic Transmission Planning Process 83 4.7.4. Automatic Transmission
Planning Algorithms 84 4.7.5. Practical Example 87 4.8. Automated Site
Selection and RF Planning 87 4.8.1. Solution Space 89 4.8.2. RF Planning
Evaluation Model 90 4.8.3. RF Optimization Engine 91 4.8.4.
Technology-Specific Aspects of RF Planning 92 4.9. Automated Neighbor
Planning 98 4.9.1. Technology-Specific Aspects of Neighbor Lists 99 4.9.2.
Principles of Automated Neighbor List Planning 103 4.10. Automated Spectrum
Planning for GSM/GPRS/EDGE 105 4.10.1. Spectrum Planning Objectives 107
4.10.2. Inputs to Spectrum Planning 108 4.10.3. Automatic Frequency
Planning 112 4.10.4. Spectrum Self-Planning for GSM/GPRS/EDGE 114 4.10.5.
Trade-Offs and Spectrum Plan Evaluation 115 4.11. Automated Planning of 3G
Scrambling Codes 117 4.11.1. Scrambling Codes in UMTS-FDD 117 4.11.2.
Primary Scrambling Code Planning 119 4.11.3. PSC Planning and Optimization
in SON 122 4.12. Automated Planning of LTE Physical Cell Identifiers 124
4.12.1. The LTE Physical Cell ID 124 4.12.2. Planning LTE Physical Cell IDs
125 4.12.3. Automated Planning of PCI in SON 126 4.13. References 127 5
Multi-Technology Self-Optimization 131 5.1. Self-Optimization Requirements
for 2G, 3G and LTE 131 5.2. Cross-Technology Constraints for
Self-Optimization 132 5.3. Optimization Technologies 132 5.3.1. Control
Engineering Techniques for Optimization 132 5.3.2. Technology Discussion
for Optimizing Cellular Communication Systems 136 5.4. Sources for
Automated Optimization of Cellular Networks 136 5.4.1. Propagation
Predictions 137 5.4.2. Drive Test Measurements 137 5.4.3. Performance
Counters Measured at the OSS 138 5.4.4. Call Traces 138 5.5. Self-Planning
versus Open-Loop Self-Optimization 139 5.5.1. Minimizing Human Intervention
in Open-Loop Automated Optimization Systems 140 5.6. Architectures for
Automated and Autonomous Optimization 140 5.6.1. Centralized, Open-Loop
Automated Self-Optimization 140 5.6.2. Centralized, Closed-Loop Autonomous
Self-Optimization 141 5.6.3. Distributed, Autonomous Self-Optimization 143
5.7. Open-Loop, Automated Self-Optimization of Cellular Networks 144 5.7.1.
Antenna Settings 144 5.7.2. Neighbor Lists 146 5.7.3. Frequency Plans 148
5.8. Closed-Loop, Autonomous Self-Optimization of 2G Networks 148 5.8.1.
Mobility Load Balance for Multi-Layer 2G Networks 149 5.8.2. Mobility
Robustness Optimization for Multi-Layer 2G Networks 151 5.9. Closed-Loop,
Autonomous Self-Optimization of 3G Networks 153 5.9.1. UMTS Optimization
Dimensions 153 5.9.2. Key UMTS Optimization Parameters 155 5.9.3. Field
Results of UMTS RRM Self-Optimization 163 5.10. Closed-Loop, Autonomous
Self-Optimization of LTE Networks 165 5.10.1. Automatic Neighbor Relation
166 5.10.2. Mobility Load Balance 168 5.10.3. Mobility Robustness
Optimization 176 5.10.4. Coverage and Capacity Optimization 178 5.10.5.
RACH Optimization 179 5.10.6. Inter-Cell Interference Coordination 179
5.10.7. Admission Control Optimization 184 5.11. Autonomous Load Balancing
for Multi-Technology Networks 185 5.11.1. Load Balancing Driven by Capacity
Reasons 186 5.11.2. Load Balancing Driven by Coverage Reasons 189 5.11.3.
Load Balancing Driven by Quality Reasons 190 5.11.4. Field Results 190
5.12. Multi-Technology Energy Saving for Green IT 191 5.12.1. Approaching
Energy Saving through Different Angles 192 5.12.2. Static Energy Saving 193
5.12.3. Dynamic Energy Saving 195 5.12.4. Operational Challenges 196
5.12.5. Field Results 197 5.13. Coexistence with Network Management Systems
197 5.13.1. Network Management System Concept and Functions 197 5.13.2.
Other Management Systems 201 5.13.3. Interworking between SON Optimization
Functions and NMS 201 5.14. Multi-Vendor Self-Optimization 202 5.15.
References 204 6 Multi-Technology Self-Healing 207 6.1. Self-Healing
Requirements for 2G, 3G and LTE 207 6.2. The Self-Healing Process 208
6.2.1. Detection 209 6.2.2. Diagnosis 210 6.2.3. Cure 210 6.3. Inputs for
Self-Healing 211 6.4. Self-Healing for Multi-Layer 2G Networks 211 6.4.1.
Detecting Problems 211 6.4.2. Diagnosis 211 6.4.3. Cure 214 6.5.
Self-Healing for Multi-Layer 3G Networks 214 6.5.1. Detecting Problems 214
6.5.2. Diagnosis 214 6.5.3. Cure 218 6.6. Self-Healing for Multi-Layer LTE
Networks 220 6.6.1. Cell Outage Compensation Concepts 222 6.6.2. Cell
Outage Compensation Algorithms 223 6.6.3. Results for P0 Tuning 224 6.6.4.
Results for Antenna Tilt Optimization 224 6.7. Multi-Vendor Self-Healing
227 6.8. References 229 7 Return on Investment (ROI) for Multi-Technology
SON 231 7.1. Overview of SON Benefits 231 7.2. General Model for ROI
Calculation 233 7.3. Case Study: ROI for Self-Planning 235 7.3.1. Scope of
Self-Planning and ROI Components 235 7.3.2. Automated Capacity Planning 237
7.3.3. Modeling SON for Automated Capacity Planning 237 7.3.4.
Characterizing the Traffic Profile 238 7.3.5. Modeling the Need for
Capacity Expansions 241 7.3.6. CAPEX Computations 243 7.3.7. OPEX
Computations 243 7.3.8. Sample Scenario and ROI 245 7.4. Case Study: ROI
for Self-Optimization 249 7.4.1. Self-Optimization and ROI Components 249
7.4.2. Modeling SON for Self-Optimization 250 7.4.3. Characterizing the
Traffic Profile 250 7.4.4. Modeling the Need for Capacity Expansions 251
7.4.5. Quality, Churn and Revenue 252 7.4.6. CAPEX Computations 254 7.4.7.
OPEX Computations 255 7.4.8. Sample Scenario and ROI 255 7.5. Case Study:
ROI for Self-Healing 260 7.5.1. OPEX Reduction through Automation 260
7.5.2. Extra Revenue due to Improved Quality and Reduced Churn 260 7.5.3.
Sample Scenario and ROI 261 7.6. References 261 Appendix A Geo-Location
Technology for UMTS 263 A.1. Introduction 263 A.2. Observed Time
Differences (OTDs) 264 A.3. Algorithm Description 264 A.3.1. Geo-Location
of Events 264 A.3.2. Synchronization Recovery 265 A.3.3. Filtering of
Events 265 A.4. Scenario and Working Assumptions 266 A.5. Results 266
A.5.1. Reported Sites per Event 266 A.5.2. Event Status Report 268 A.5.3.
Geo-Location Accuracy 268 A.5.4. Impact of Using PD Measurements 269 A.6.
Concluding Remarks 269 A.7. References 271 Appendix B X-Map Estimation for
LTE 273 B.1. Introduction 273 B.2. X-Map Estimation Approach 274 B.3.
Simulation Results 275 B.4. References 277 Index 279