Self-Organizing Networks: Self-Planning, Self-Optimization and Self-Healing for GSM, UMTS and LTE -- Contents -- Foreword -- Preface -- Acknowledgements -- List of Contributors -- List of Abbreviations -- 1 Operating Mobile Broadband Networks -- 1.1. The Challenge of Mobile Traffic Growth -- 1.1.1. Differences between Smartphones -- 1.1.2. Driving Data Traffic - Streaming Media and Other Services -- 1.2. Capacity and Coverage Crunch -- 1.3. Meeting the Challenge - the Network Operator Toolkit -- 1.3.1. Tariff Structures -- 1.3.2. Advanced Radio Access Technologies -- 1.3.3. Femto Cells -- 1.3.4. Acquisition and Activation of New Spectrum -- 1.3.5. Companion Networks, Offloading and Traffic Management -- 1.3.6. Advanced Source Coding -- 1.4. Self-Organizing Networks (SON) -- 1.5. Summary and Book Contents -- 1.6. References -- 2 The Self-Organizing Networks (SON) Paradigm -- 2.1. Motivation and Targets from NGMN -- 2.2. SON Use Cases -- 2.2.1. Use Case Categories -- 2.2.2. Automatic versus Autonomous Processes -- 2.2.3. Self-Planning Use Cases -- 2.2.4. Self-Deployment Use Cases -- 2.2.5. Self-Optimization Use Cases -- 2.2.6. Self-Healing Use Cases -- 2.2.7. SON Enablers -- 2.3. SON versus Radio Resource Management -- 2.4. SON in 3GPP -- 2.4.1. 3GPP Organization -- 2.4.2. SON Status in 3GPP (up to Release 9) -- 2.4.3. SON Objectives for 3GPP Release 10 -- 2.5. SON in the Research Community -- 2.5.1. SOCRATES: Self-Optimization and Self-ConfiguRATion in wirelEss networkS -- 2.5.2. Celtic Gandalf: Monitoring and Self-Tuning of RRM Parameters in a Multi-System Network -- 2.5.3. Celtic OPERA-Net: Optimizing Power Efficiency in mobile RAdio Networks -- 2.5.4. E3: End-to-End Efficiency -- 2.6. References -- 3 Multi-Technology SON -- 3.1. Drivers for Multi-Technology SON -- 3.2. Architectures for Multi-Technology SON.

3.2.1. Deployment Architectures for Self-Organizing Networks -- 3.2.2. Comparison of SON Architectures -- 3.2.3. Coordination of SON Functions -- 3.2.4. Layered Architecture for Centralized Multi-Technology SON -- 3.3. References -- 4 Multi-Technology Self-Planning -- 4.1. Self-Planning Requirements for 2G, 3G and LTE -- 4.2. Cross-Technology Constraints for Self-Planning -- 4.3. Self-Planning as an Integrated Process -- 4.4. Planning versus Optimization -- 4.5. Information Sources for Self-Planning -- 4.5.1. Propagation Path-Loss Predictions -- 4.5.2. Drive Test Measurements -- 4.6. Automated Capacity Planning -- 4.6.1. Main Inputs for Automated Capacity Planning -- 4.6.2. Traffic and Network Load Forecast -- 4.6.3. Automated Capacity Planning Process -- 4.6.4. Outputs of the Process and Implementation of Capacity Upgrades in the Network -- 4.7. Automated Transmission Planning -- 4.7.1. Self-Organizing Protocols -- 4.7.2. Additional Requirements for Automated Transmission Planning -- 4.7.3. Automatic Transmission Planning Process -- 4.7.4. Automatic Transmission Planning Algorithms -- 4.7.5. Practical Example -- 4.8. Automated Site Selection and RF Planning -- 4.8.1. Solution Space -- 4.8.2. RF Planning Evaluation Model -- 4.8.3. RF Optimization Engine -- 4.8.4. Technology-Specific Aspects of RF Planning -- 4.9. Automated Neighbor Planning -- 4.9.1. Technology-Specific Aspects of Neighbor Lists -- 4.9.2. Principles of Automated Neighbor List Planning -- 4.10. Automated Spectrum Planning for GSM/GPRS/EDGE -- 4.10.1. Spectrum Planning Objectives -- 4.10.2. Inputs to Spectrum Planning -- 4.10.3. Automatic Frequency Planning -- 4.10.4. Spectrum Self-Planning for GSM/GPRS/EDGE -- 4.10.5. Trade-Offs and Spectrum Plan Evaluation -- 4.11. Automated Planning of 3G Scrambling Codes -- 4.11.1. Scrambling Codes in UMTS-FDD.

4.11.2. Primary Scrambling Code Planning -- 4.11.3. PSC Planning and Optimization in SON -- 4.12. Automated Planning of LTE Physical Cell Identifiers -- 4.12.1. The LTE Physical Cell ID -- 4.12.2. Planning LTE Physical Cell IDs -- 4.12.3. Automated Planning of PCI in SON -- 4.13. References -- 5 Multi-Technology Self-Optimization -- 5.1. Self-Optimization Requirements for 2G, 3G and LTE -- 5.2. Cross-Technology Constraints for Self-Optimization -- 5.3. Optimization Technologies -- 5.3.1. Control Engineering Techniques for Optimization -- 5.3.2. Technology Discussion for Optimizing Cellular Communication Systems -- 5.4. Sources for Automated Optimization of Cellular Networks -- 5.4.1. Propagation Predictions -- 5.4.2. Drive Test Measurements -- 5.4.3. Performance Counters Measured at the OSS -- 5.4.4. Call Traces -- 5.5. Self-Planning versus Open-Loop Self-Optimization -- 5.5.1. Minimizing Human Intervention in Open-Loop Automated Optimization Systems -- 5.6. Architectures for Automated and Autonomous Optimization -- 5.6.1. Centralized, Open-Loop Automated Self-Optimization -- 5.6.2. Centralized, Closed-Loop Autonomous Self-Optimization -- 5.6.3. Distributed, Autonomous Self-Optimization -- 5.7. Open-Loop, Automated Self-Optimization of Cellular Networks -- 5.7.1. Antenna Settings -- 5.7.2. Neighbor Lists -- 5.7.3. Frequency Plans -- 5.8. Closed-Loop, Autonomous Self-Optimization of 2G Networks -- 5.8.1. Mobility Load Balance for Multi-Layer 2G Networks -- 5.8.2. Mobility Robustness Optimization for Multi-Layer 2G Networks -- 5.9. Closed-Loop, Autonomous Self-Optimization of 3G Networks -- 5.9.1. UMTS Optimization Dimensions -- 5.9.2. Key UMTS Optimization Parameters -- 5.9.3. Field Results of UMTS RRM Self-Optimization -- 5.10. Closed-Loop, Autonomous Self-Optimization of LTE Networks -- 5.10.1. Automatic Neighbor Relation.

5.10.2. Mobility Load Balance -- 5.10.3. Mobility Robustness Optimization -- 5.10.4. Coverage and Capacity Optimization -- 5.10.5. RACH Optimization -- 5.10.6. Inter-Cell Interference Coordination -- 5.10.7. Admission Control Optimization -- 5.11. Autonomous Load Balancing for Multi-Technology Networks -- 5.11.1. Load Balancing Driven by Capacity Reasons -- 5.11.2. Load Balancing Driven by Coverage Reasons -- 5.11.3. Load Balancing Driven by Quality Reasons -- 5.11.4. Field Results -- 5.12. Multi-Technology Energy Saving for Green IT -- 5.12.1. Approaching Energy Saving through Different Angles -- 5.12.2. Static Energy Saving -- 5.12.3. Dynamic Energy Saving -- 5.12.4. Operational Challenges -- 5.12.5. Field Results -- 5.13. Coexistence with Network Management Systems -- 5.13.1. Network Management System Concept and Functions -- 5.13.2. Other Management Systems -- 5.13.3. Interworking between SON Optimization Functions and NMS -- 5.14. Multi-Vendor Self-Optimization -- 5.15. References -- 6 Multi-Technology Self-Healing -- 6.1. Self-Healing Requirements for 2G, 3G and LTE -- 6.2. The Self-Healing Process -- 6.2.1. Detection -- 6.2.2. Diagnosis -- 6.2.3. Cure -- 6.3. Inputs for Self-Healing -- 6.4. Self-Healing for Multi-Layer 2G Networks -- 6.4.1. Detecting Problems -- 6.4.2. Diagnosis -- 6.4.3. Cure -- 6.5. Self-Healing for Multi-Layer 3G Networks -- 6.5.1. Detecting Problems -- 6.5.2. Diagnosis -- 6.5.3. Cure -- 6.6. Self-Healing for Multi-Layer LTE Networks -- 6.6.1. Cell Outage Compensation Concepts -- 6.6.2. Cell Outage Compensation Algorithms -- 6.6.3. Results for P0 Tuning -- 6.6.4. Results for Antenna Tilt Optimization -- 6.7. Multi-Vendor Self-Healing -- 6.8. References -- 7 Return on Investment (ROI) for Multi-Technology SON -- 7.1. Overview of SON Benefits -- 7.2. General Model for ROI Calculation -- 7.3. Case Study: ROI for Self-Planning.

7.3.1. Scope of Self-Planning and ROI Components -- 7.3.2. Automated Capacity Planning -- 7.3.3. Modeling SON for Automated Capacity Planning -- 7.3.4. Characterizing the Traffic Profile -- 7.3.5. Modeling the Need for Capacity Expansions -- 7.3.6. CAPEX Computations -- 7.3.7. OPEX Computations -- 7.3.8. Sample Scenario and ROI -- 7.4. Case Study: ROI for Self-Optimization -- 7.4.1. Self-Optimization and ROI Components -- 7.4.2. Modeling SON for Self-Optimization -- 7.4.3. Characterizing the Traffic Profile -- 7.4.4. Modeling the Need for Capacity Expansions -- 7.4.5. Quality, Churn and Revenue -- 7.4.6. CAPEX Computations -- 7.4.7. OPEX Computations -- 7.4.8. Sample Scenario and ROI -- 7.5. Case Study: ROI for Self-Healing -- 7.5.1. OPEX Reduction through Automation -- 7.5.2. Extra Revenue due to Improved Quality and Reduced Churn -- 7.5.3. Sample Scenario and ROI -- 7.6. References -- Appendix A Geo-Location Technology for UMTS -- A.1. Introduction -- A.2. Observed Time Differences (OTDs) -- A.3. Algorithm Description -- A.3.1. Geo-Location of Events -- A.3.2. Synchronization Recovery -- A.3.3. Filtering of Events -- A.4. Scenario and Working Assumptions -- A.5. 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 -- A.6. Concluding Remarks -- A.7. References -- Appendix B X-Map Estimation for LTE -- B.1. Introduction -- B.2. X-Map Estimation Approach -- B.3. Simulation Results -- B.4. References -- Index.