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Self-Organizing Networks Self-Planning, Self-Optimization and Self-Healing for GSM, UMTS and LTE

by: Ramiro, Juan; Hamied, Khalid

Self-Organizing Networks Self-Planning, Self-Optimization and Self-Healing for GSM, UMTS and LTE

by: Ramiro, Juan; Hamied, Khalid

ISBN 13:
9780470973523
ISBN 10:
0470973528
Edition: 1st
Format: Hardcover
Copyright: 12/19/2011
Publisher: Wiley

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Self-Organizing Networks Self-Planning, Self-Optimization and Self-Healing for GSM, UMTS and LTE > ISBN13: 9780470973523

Summary

The book offers a multi-technology approach as it will consider the implications of the different SON requirements for 2G and 3G networks, as well as 4G wireless technologies. The challenges in each layer will be different due to the nature of the different mechanisms involved in each technology, and also due to the fact that currently deployed 2G and 3G networks were not conceived to facilitate the fulfillment of the different SON requirements. The most relevant use cases from the area of self-planning, self-optimization and self-healing are covered including theoretical, simulative and practical aspects. Business case aspects are considered in the framework of return on invest methodologies and case studies.

Author Biography

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.

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

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