SELF-ORGANIZING NETWORKS SELF-PLANNING, SELF-OPTIMIZATION AND SELF-HEALING FOR GSM, UMTS AND LTE

Editors

Juan Ramiro Ericsson, Malaga, Spain

Khalid Hamied Ericsson, Atlanta, GA, USA

©WILEY A John Wiley & Sons, Ltd., Publication

Contents

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 7.7.7. Differences between Smartphones 3 7.7.2. 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

2.2.7. 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. SONEnablers 34

vi Contents

2.3. SON versus Radio Resource Management 35 2.4. SONin3GPP 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 ofRRM

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 1Ъ 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

Contents vii

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 5.5.7. Minimizing Human Intervention in Open-Loop Automated

Optimization Systems 140 5.6. Architectures for Automated and Autonomous Optimization 140

5.6.7. 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

viii Contents

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 5.5.2. 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

Contents ix

6.3. Inputs for Self-Healing 211 6.4. Self-Healing for Multi-Layer 2G Networks 211

6.4.7. Detecting Problems 211 6.4.2. Diagnosis 211 6.4.3. Cure 214

6.5. Self-Healing for Multi-Layer 3G Networks 214 6.5.7. Detecting Problems 214 6.5.2. Diagnosis 214 6.5.3. Cure 218

6.6. Self-Healing for Multi-Layer LTE Networks 220 6.6.7. 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 22A

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. С APEX Computations 243 7.3.7. ОРЕХ 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. С APEX Computations 254 7.4.7. ОРЕХ Computations 255 7.4.8. Sample Scenario and ROI 255

7.5. Case Study: ROI for Self-Healing 260 7.5.7. ОРЕХ 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

X Contents

Appendix A Geo-Location Technology for UMTS 263

263 264 264 264 265 265 266 266 266 268 268 269 269 271

273 273 274 275 277

Index 279

A.l. A.2. A.3.

A.4. A.5.

A.6. A.7.

Append

B.l. B.2. B.3. B.4.

Introduction Observed Time Differences (OTDs) Algorithm Description A.3.1. Geo-Location of Events A.3.2. Synchronization Recovery A.3.3. Filtering of Events Scenario and Working Assumptions Results A.5.1. Reported Sites per Event A.5.2. Event Status Report A.5.3. Geo-Location Accuracy A.5.4. Impact of Using PD Measurements Concluding Remarks References

ix В X-Map Estimation for LTE

Introduction X-Map Estimation Approach Simulation Results References