{"product_id":"self-organizing-networks-isbn-9780470973523","title":"Self-Organizing Networks","description":"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.  \u003cp\u003eWritten 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.\u003c\/p\u003e \u003cp\u003eKey features:\u003c\/p\u003e \u003cul type=\"disc\"\u003e \u003cli\u003eFollows 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\u003c\/li\u003e \u003cli\u003eFeatures detailed discussions about the relevant trade-offs in each Use Case\u003c\/li\u003e \u003cli\u003eIncludes field results of today’s GSM and UMTS SON implementations in live networks\u003c\/li\u003e \u003cli\u003eAddresses the calculation of ROI for Multi-Technology SON, contributing to a more complete and strategic view of the SON paradigm\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThis book will appeal to network planners, optimization engineers, technical\/strategy managers with operators and R\u0026amp;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.\u003c\/p\u003e  \u003cb\u003eForeword xi\u003c\/b\u003e  \u003cp\u003e\u003cb\u003ePreface xiii\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAcknowledgements xv\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eList of Contributors xvii\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eList of Abbreviations xix\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Operating Mobile Broadband Networks 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1. The Challenge of Mobile Traffic Growth 1\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.1.1. Differences between Smartphones\u003c\/i\u003e 3\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.1.2. Driving Data Traffic – Streaming Media and Other Services\u003c\/i\u003e 5\u003c\/p\u003e \u003cp\u003e1.2. Capacity and Coverage Crunch 5\u003c\/p\u003e \u003cp\u003e1.3. Meeting the Challenge – the Network Operator Toolkit 6\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.3.1. Tariff Structures\u003c\/i\u003e 6\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.3.2. Advanced Radio Access Technologies\u003c\/i\u003e 7\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.3.3. Femto Cells\u003c\/i\u003e 10\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.3.4. Acquisition and Activation of New Spectrum\u003c\/i\u003e 11\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.3.5. Companion Networks, Offloading and Traffic Management\u003c\/i\u003e 12\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.3.6. Advanced Source Coding\u003c\/i\u003e 14\u003c\/p\u003e \u003cp\u003e1.4. Self-Organizing Networks (SON) 16\u003c\/p\u003e \u003cp\u003e1.5. Summary and Book Contents 17\u003c\/p\u003e \u003cp\u003e1.6. References 19\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 The Self-Organizing Networks (SON) Paradigm 21\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1. Motivation and Targets from NGMN 21\u003c\/p\u003e \u003cp\u003e2.2. SON Use Cases 23\u003c\/p\u003e \u003cp\u003e\u003ci\u003e2.2.1. Use Case Categories\u003c\/i\u003e 23\u003c\/p\u003e \u003cp\u003e\u003ci\u003e2.2.2. Automatic versus Autonomous Processes\u003c\/i\u003e 25\u003c\/p\u003e \u003cp\u003e\u003ci\u003e2.2.3. Self-Planning Use Cases\u003c\/i\u003e 25\u003c\/p\u003e \u003cp\u003e\u003ci\u003e2.2.4. Self-Deployment Use Cases\u003c\/i\u003e 26\u003c\/p\u003e \u003cp\u003e\u003ci\u003e2.2.5. Self-Optimization Use Cases\u003c\/i\u003e 28\u003c\/p\u003e \u003cp\u003e\u003ci\u003e2.2.6. Self-Healing Use Cases\u003c\/i\u003e 32\u003c\/p\u003e \u003cp\u003e\u003ci\u003e2.2.7. SON Enablers\u003c\/i\u003e 34\u003c\/p\u003e \u003cp\u003e2.3. SON versus Radio Resource Management 35\u003c\/p\u003e \u003cp\u003e2.4. SON in 3GPP 37\u003c\/p\u003e \u003cp\u003e\u003ci\u003e2.4.1. 3GPP Organization\u003c\/i\u003e 37\u003c\/p\u003e \u003cp\u003e\u003ci\u003e2.4.2. SON Status in 3GPP (up to Release 9)\u003c\/i\u003e 38\u003c\/p\u003e \u003cp\u003e\u003ci\u003e2.4.3. SON Objectives for 3GPP Release 10\u003c\/i\u003e 40\u003c\/p\u003e \u003cp\u003e2.5. SON in the Research Community 41\u003c\/p\u003e \u003cp\u003e\u003ci\u003e2.5.1. SOCRATES: Self-Optimization and Self-ConfiguRATion in wirelEss networkS\u003c\/i\u003e 41\u003c\/p\u003e \u003cp\u003e\u003ci\u003e2.5.2. Celtic Gandalf: Monitoring and Self-Tuning of RRM Parameters in a Multi-System Network\u003c\/i\u003e 42\u003c\/p\u003e \u003cp\u003e\u003ci\u003e2.5.3. Celtic OPERA-Net: Optimizing Power Efficiency in mobile RAdio Networks\u003c\/i\u003e 42\u003c\/p\u003e \u003cp\u003e\u003ci\u003e2.5.4. E3: End-to-End Efficiency\u003c\/i\u003e 43\u003c\/p\u003e \u003cp\u003e2.6. References 43\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Multi-Technology SON 47\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1. Drivers for Multi-Technology SON 47\u003c\/p\u003e \u003cp\u003e3.2. Architectures for Multi-Technology SON 49\u003c\/p\u003e \u003cp\u003e\u003ci\u003e3.2.1. Deployment Architectures for Self-Organizing Networks\u003c\/i\u003e 49\u003c\/p\u003e \u003cp\u003e\u003ci\u003e3.2.2. Comparison of SON Architectures\u003c\/i\u003e 50\u003c\/p\u003e \u003cp\u003e\u003ci\u003e3.2.3. Coordination of SON Functions\u003c\/i\u003e 53\u003c\/p\u003e \u003cp\u003e\u003ci\u003e3.2.4. Layered Architecture for Centralized Multi-Technology SON\u003c\/i\u003e 59\u003c\/p\u003e \u003cp\u003e3.3. References 64\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Multi-Technology Self-Planning 65\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1. Self-Planning Requirements for 2G, 3G and LTE 65\u003c\/p\u003e \u003cp\u003e4.2. Cross-Technology Constraints for Self-Planning 66\u003c\/p\u003e \u003cp\u003e4.3. Self-Planning as an Integrated Process 66\u003c\/p\u003e \u003cp\u003e4.4. Planning versus Optimization 69\u003c\/p\u003e \u003cp\u003e4.5. Information Sources for Self-Planning 70\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.5.1. Propagation Path-Loss Predictions\u003c\/i\u003e 70\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.5.2. Drive Test Measurements\u003c\/i\u003e 71\u003c\/p\u003e \u003cp\u003e4.6. Automated Capacity Planning 71\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.6.1. Main Inputs for Automated Capacity Planning\u003c\/i\u003e 73\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.6.2. Traffic and Network Load Forecast\u003c\/i\u003e 74\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.6.3. Automated Capacity Planning Process\u003c\/i\u003e 75\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.6.4. Outputs of the Process and Implementation of Capacity Upgrades in the Network\u003c\/i\u003e 78\u003c\/p\u003e \u003cp\u003e4.7. Automated Transmission Planning 79\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.7.1. Self-Organizing Protocols\u003c\/i\u003e 80\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.7.2. Additional Requirements for Automated Transmission Planning\u003c\/i\u003e 82\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.7.3. Automatic Transmission Planning Process\u003c\/i\u003e 83\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.7.4. Automatic Transmission Planning Algorithms\u003c\/i\u003e 84\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.7.5. Practical Example\u003c\/i\u003e 87\u003c\/p\u003e \u003cp\u003e4.8. Automated Site Selection and RF Planning 87\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.8.1. Solution Space\u003c\/i\u003e 89\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.8.2. RF Planning Evaluation Model\u003c\/i\u003e 90\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.8.3. RF Optimization Engine\u003c\/i\u003e 91\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.8.4. Technology-Specific Aspects of RF Planning\u003c\/i\u003e 92\u003c\/p\u003e \u003cp\u003e4.9. Automated Neighbor Planning 98\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.9.1. Technology-Specific Aspects of Neighbor Lists\u003c\/i\u003e 99\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.9.2. Principles of Automated Neighbor List Planning\u003c\/i\u003e 103\u003c\/p\u003e \u003cp\u003e4.10. Automated Spectrum Planning for GSM\/GPRS\/EDGE 105\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.10.1. Spectrum Planning Objectives\u003c\/i\u003e 107\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.10.2. Inputs to Spectrum Planning\u003c\/i\u003e 108\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.10.3. Automatic Frequency Planning\u003c\/i\u003e 112\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.10.4. Spectrum Self-Planning for GSM\/GPRS\/EDGE\u003c\/i\u003e 114\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.10.5. Trade-Offs and Spectrum Plan Evaluation\u003c\/i\u003e 115\u003c\/p\u003e \u003cp\u003e4.11. Automated Planning of 3G Scrambling Codes 117\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.11.1. Scrambling Codes in UMTS-FDD\u003c\/i\u003e 117\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.11.2. Primary Scrambling Code Planning\u003c\/i\u003e 119\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.11.3. PSC Planning and Optimization in SON\u003c\/i\u003e 122\u003c\/p\u003e \u003cp\u003e4.12. Automated Planning of LTE Physical Cell Identifiers 124\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.12.1. The LTE Physical Cell ID\u003c\/i\u003e 124\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.12.2. Planning LTE Physical Cell IDs\u003c\/i\u003e 125\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.12.3. Automated Planning of PCI in SON\u003c\/i\u003e 126\u003c\/p\u003e \u003cp\u003e4.13. References 127\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Multi-Technology Self-Optimization 131\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1. Self-Optimization Requirements for 2G, 3G and LTE 131\u003c\/p\u003e \u003cp\u003e5.2. Cross-Technology Constraints for Self-Optimization 132\u003c\/p\u003e \u003cp\u003e5.3. Optimization Technologies 132\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.3.1. Control Engineering Techniques for Optimization\u003c\/i\u003e 132\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.3.2. Technology Discussion for Optimizing Cellular Communication Systems\u003c\/i\u003e 136\u003c\/p\u003e \u003cp\u003e5.4. Sources for Automated Optimization of Cellular Networks 136\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.4.1. Propagation Predictions\u003c\/i\u003e 137\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.4.2. Drive Test Measurements\u003c\/i\u003e 137\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.4.3. Performance Counters Measured at the OSS\u003c\/i\u003e 138\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.4.4. Call Traces\u003c\/i\u003e 138\u003c\/p\u003e \u003cp\u003e5.5. Self-Planning versus Open-Loop Self-Optimization 139\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.5.1. Minimizing Human Intervention in Open-Loop Automated Optimization Systems\u003c\/i\u003e 140\u003c\/p\u003e \u003cp\u003e5.6. Architectures for Automated and Autonomous Optimization 140\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.6.1. Centralized, Open-Loop Automated Self-Optimization\u003c\/i\u003e 140\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.6.2. Centralized, Closed-Loop Autonomous Self-Optimization\u003c\/i\u003e 141\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.6.3. Distributed, Autonomous Self-Optimization\u003c\/i\u003e 143\u003c\/p\u003e \u003cp\u003e5.7. Open-Loop, Automated Self-Optimization of Cellular Networks 144\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.7.1. Antenna Settings\u003c\/i\u003e 144\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.7.2. Neighbor Lists\u003c\/i\u003e 146\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.7.3. Frequency Plans\u003c\/i\u003e 148\u003c\/p\u003e \u003cp\u003e5.8. Closed-Loop, Autonomous Self-Optimization of 2G Networks 148\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.8.1. Mobility Load Balance for Multi-Layer 2G Networks\u003c\/i\u003e 149\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.8.2. Mobility Robustness Optimization for Multi-Layer 2G Networks\u003c\/i\u003e 151\u003c\/p\u003e \u003cp\u003e5.9. Closed-Loop, Autonomous Self-Optimization of 3G Networks 153\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.9.1. UMTS Optimization Dimensions\u003c\/i\u003e 153\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.9.2. Key UMTS Optimization Parameters\u003c\/i\u003e 155\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.9.3. Field Results of UMTS RRM Self-Optimization\u003c\/i\u003e 163\u003c\/p\u003e \u003cp\u003e5.10. Closed-Loop, Autonomous Self-Optimization of LTE Networks 165\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.10.1. Automatic Neighbor Relation\u003c\/i\u003e 166\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.10.2. Mobility Load Balance\u003c\/i\u003e 168\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.10.3. Mobility Robustness Optimization\u003c\/i\u003e 176\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.10.4. Coverage and Capacity Optimization\u003c\/i\u003e 178\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.10.5. RACH Optimization\u003c\/i\u003e 179\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.10.6. Inter-Cell Interference Coordination\u003c\/i\u003e 179\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.10.7. Admission Control Optimization\u003c\/i\u003e 184\u003c\/p\u003e \u003cp\u003e5.11. Autonomous Load Balancing for Multi-Technology Networks 185\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.11.1. Load Balancing Driven by Capacity Reasons\u003c\/i\u003e 186\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.11.2. Load Balancing Driven by Coverage Reasons\u003c\/i\u003e 189\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.11.3. Load Balancing Driven by Quality Reasons\u003c\/i\u003e 190\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.11.4. Field Results\u003c\/i\u003e 190\u003c\/p\u003e \u003cp\u003e5.12. Multi-Technology Energy Saving for Green IT 191\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.12.1. Approaching Energy Saving through Different Angles\u003c\/i\u003e 192\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.12.2. Static Energy Saving\u003c\/i\u003e 193\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.12.3. Dynamic Energy Saving\u003c\/i\u003e 195\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.12.4. Operational Challenges\u003c\/i\u003e 196\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.12.5. Field Results\u003c\/i\u003e 197\u003c\/p\u003e \u003cp\u003e5.13. Coexistence with Network Management Systems 197\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.13.1. Network Management System Concept and Functions\u003c\/i\u003e 197\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.13.2. Other Management Systems\u003c\/i\u003e 201\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.13.3. Interworking between SON Optimization Functions and NMS\u003c\/i\u003e 201\u003c\/p\u003e \u003cp\u003e5.14. Multi-Vendor Self-Optimization 202\u003c\/p\u003e \u003cp\u003e5.15. References 204\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Multi-Technology Self-Healing 207\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1. Self-Healing Requirements for 2G, 3G and LTE 207\u003c\/p\u003e \u003cp\u003e6.2. The Self-Healing Process 208\u003c\/p\u003e \u003cp\u003e\u003ci\u003e6.2.1. Detection\u003c\/i\u003e 209\u003c\/p\u003e \u003cp\u003e\u003ci\u003e6.2.2. Diagnosis\u003c\/i\u003e 210\u003c\/p\u003e \u003cp\u003e\u003ci\u003e6.2.3. Cure\u003c\/i\u003e 210\u003c\/p\u003e \u003cp\u003e6.3. Inputs for Self-Healing 211\u003c\/p\u003e \u003cp\u003e6.4. Self-Healing for Multi-Layer 2G Networks 211\u003c\/p\u003e \u003cp\u003e\u003ci\u003e6.4.1. Detecting Problems\u003c\/i\u003e 211\u003c\/p\u003e \u003cp\u003e\u003ci\u003e6.4.2. Diagnosis\u003c\/i\u003e 211\u003c\/p\u003e \u003cp\u003e\u003ci\u003e6.4.3. Cure\u003c\/i\u003e 214\u003c\/p\u003e \u003cp\u003e6.5. Self-Healing for Multi-Layer 3G Networks 214\u003c\/p\u003e \u003cp\u003e\u003ci\u003e6.5.1. Detecting Problems\u003c\/i\u003e 214\u003c\/p\u003e \u003cp\u003e\u003ci\u003e6.5.2. Diagnosis\u003c\/i\u003e 214\u003c\/p\u003e \u003cp\u003e\u003ci\u003e6.5.3. Cure\u003c\/i\u003e 218\u003c\/p\u003e \u003cp\u003e6.6. Self-Healing for Multi-Layer LTE Networks 220\u003c\/p\u003e \u003cp\u003e\u003ci\u003e6.6.1. Cell Outage Compensation Concepts\u003c\/i\u003e 222\u003c\/p\u003e \u003cp\u003e\u003ci\u003e6.6.2. Cell Outage Compensation Algorithms\u003c\/i\u003e 223\u003c\/p\u003e \u003cp\u003e\u003ci\u003e6.6.3. Results for P0 Tuning\u003c\/i\u003e 224\u003c\/p\u003e \u003cp\u003e\u003ci\u003e6.6.4. Results for Antenna Tilt Optimization\u003c\/i\u003e 224\u003c\/p\u003e \u003cp\u003e6.7. Multi-Vendor Self-Healing 227\u003c\/p\u003e \u003cp\u003e6.8. References 229\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Return on Investment (ROI) for Multi-Technology SON 231\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1. Overview of SON Benefits 231\u003c\/p\u003e \u003cp\u003e7.2. General Model for ROI Calculation 233\u003c\/p\u003e \u003cp\u003e7.3. Case Study: ROI for Self-Planning 235\u003c\/p\u003e \u003cp\u003e\u003ci\u003e7.3.1. Scope of Self-Planning and ROI Components\u003c\/i\u003e 235\u003c\/p\u003e \u003cp\u003e\u003ci\u003e7.3.2. Automated Capacity Planning\u003c\/i\u003e 237\u003c\/p\u003e \u003cp\u003e\u003ci\u003e7.3.3. Modeling SON for Automated Capacity Planning\u003c\/i\u003e 237\u003c\/p\u003e \u003cp\u003e\u003ci\u003e7.3.4. Characterizing the Traffic Profile\u003c\/i\u003e 238\u003c\/p\u003e \u003cp\u003e\u003ci\u003e7.3.5. Modeling the Need for Capacity Expansions\u003c\/i\u003e 241\u003c\/p\u003e \u003cp\u003e\u003ci\u003e7.3.6. CAPEX Computations\u003c\/i\u003e 243\u003c\/p\u003e \u003cp\u003e\u003ci\u003e7.3.7. OPEX Computations\u003c\/i\u003e 243\u003c\/p\u003e \u003cp\u003e\u003ci\u003e7.3.8. Sample Scenario and ROI\u003c\/i\u003e 245\u003c\/p\u003e \u003cp\u003e7.4. Case Study: ROI for Self-Optimization 249\u003c\/p\u003e \u003cp\u003e\u003ci\u003e7.4.1. Self-Optimization and ROI Components\u003c\/i\u003e 249\u003c\/p\u003e \u003cp\u003e\u003ci\u003e7.4.2. Modeling SON for Self-Optimization\u003c\/i\u003e 250\u003c\/p\u003e \u003cp\u003e\u003ci\u003e7.4.3. Characterizing the Traffic Profile\u003c\/i\u003e 250\u003c\/p\u003e \u003cp\u003e\u003ci\u003e7.4.4. Modeling the Need for Capacity Expansions\u003c\/i\u003e 251\u003c\/p\u003e \u003cp\u003e\u003ci\u003e7.4.5. Quality, Churn and Revenue\u003c\/i\u003e 252\u003c\/p\u003e \u003cp\u003e\u003ci\u003e7.4.6. CAPEX Computations\u003c\/i\u003e 254\u003c\/p\u003e \u003cp\u003e\u003ci\u003e7.4.7. OPEX Computations\u003c\/i\u003e 255\u003c\/p\u003e \u003cp\u003e\u003ci\u003e7.4.8. Sample Scenario and ROI\u003c\/i\u003e 255\u003c\/p\u003e \u003cp\u003e7.5. Case Study: ROI for Self-Healing 260\u003c\/p\u003e \u003cp\u003e\u003ci\u003e7.5.1. OPEX Reduction through Automation\u003c\/i\u003e 260\u003c\/p\u003e \u003cp\u003e\u003ci\u003e7.5.2. Extra Revenue due to Improved Quality and Reduced Churn\u003c\/i\u003e 260\u003c\/p\u003e \u003cp\u003e\u003ci\u003e7.5.3. Sample Scenario and ROI\u003c\/i\u003e 261\u003c\/p\u003e \u003cp\u003e7.6. References 261\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A Geo-Location Technology for UMTS 263\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA.1. Introduction 263\u003c\/p\u003e \u003cp\u003eA.2. Observed Time Differences (OTDs) 264\u003c\/p\u003e \u003cp\u003eA.3. Algorithm Description 264\u003c\/p\u003e \u003cp\u003e\u003ci\u003eA.3.1. Geo-Location of Events\u003c\/i\u003e 264\u003c\/p\u003e \u003cp\u003e\u003ci\u003eA.3.2. Synchronization Recovery\u003c\/i\u003e 265\u003c\/p\u003e \u003cp\u003e\u003ci\u003eA.3.3. Filtering of Events\u003c\/i\u003e 265\u003c\/p\u003e \u003cp\u003eA.4. Scenario and Working Assumptions 266\u003c\/p\u003e \u003cp\u003eA.5. Results 266\u003c\/p\u003e \u003cp\u003e\u003ci\u003eA.5.1. Reported Sites per Event\u003c\/i\u003e 266\u003c\/p\u003e \u003cp\u003e\u003ci\u003eA.5.2. Event Status Report\u003c\/i\u003e 268\u003c\/p\u003e \u003cp\u003e\u003ci\u003eA.5.3. Geo-Location Accuracy\u003c\/i\u003e 268\u003c\/p\u003e \u003cp\u003e\u003ci\u003eA.5.4. Impact of Using PD Measurements\u003c\/i\u003e 269\u003c\/p\u003e \u003cp\u003eA.6. Concluding Remarks 269\u003c\/p\u003e \u003cp\u003eA.7. References 271\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix B X-Map Estimation for LTE 273\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eB.1. Introduction 273\u003c\/p\u003e \u003cp\u003eB.2. X-Map Estimation Approach 274\u003c\/p\u003e \u003cp\u003eB.3. Simulation Results 275\u003c\/p\u003e \u003cp\u003eB.4. References 277\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex 279\u003c\/b\u003e\u003c\/p\u003e  \u003cb\u003eDr. Juan Ramiro\u003c\/b\u003e 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\u0026amp;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).  \u003cp\u003e\u003cb\u003eDr. Khalid Hamied\u003c\/b\u003e 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.\u003c\/p\u003e  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.  \u003cp\u003eWritten 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.\u003c\/p\u003e \u003cp\u003eKey features:\u003c\/p\u003e \u003cul type=\"disc\"\u003e \u003cli\u003eFollows 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\u003c\/li\u003e \u003cli\u003eFeatures detailed discussions about the relevant trade-offs in each Use Case\u003c\/li\u003e \u003cli\u003eIncludes field results of today’s GSM and UMTS SON implementations in live networks\u003c\/li\u003e \u003cli\u003eAddresses the calculation of ROI for Multi-Technology SON, contributing to a more complete and strategic view of the SON paradigm\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThis book will appeal to network planners, optimization engineers, technical\/strategy managers with operators and R\u0026amp;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.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47990004056293,"sku":"NP9780470973523","price":119.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470973523.jpg?v=1761786183","url":"https:\/\/k12savings.com\/es\/products\/self-organizing-networks-isbn-9780470973523","provider":"K12savings","version":"1.0","type":"link"}