Self-Organizing Networks

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

Inbunden, Engelska, 2011

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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 networksFeatures detailed discussions about the relevant trade-offs in each Use CaseIncludes field results of today’s GSM and UMTS SON implementations in live networksAddresses the calculation of ROI for Multi-Technology SON, contributing to a more complete and strategic view of the SON paradigmThis 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.

Produktinformation

Utgivningsdatum2011-11-11
Mått175 x 252 x 24 mm
Vikt680 g
FormatInbunden
SpråkEngelska
Antal sidor320
FörlagJohn Wiley & Sons Inc
ISBN9780470973523

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Elektronik och kommunikationer inom Naturvetenskap och teknik

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 xiiiAcknowledgements xvList of Contributors xviiList of Abbreviations xix1 Operating Mobile Broadband Networks 11.1. The Challenge of Mobile Traffic Growth 11.1.1. Differences between Smartphones 31.1.2. Driving Data Traffic – Streaming Media and Other Services 51.2. Capacity and Coverage Crunch 51.3. Meeting the Challenge – the Network Operator Toolkit 61.3.1. Tariff Structures 61.3.2. Advanced Radio Access Technologies 71.3.3. Femto Cells 101.3.4. Acquisition and Activation of New Spectrum 111.3.5. Companion Networks, Offloading and Traffic Management 121.3.6. Advanced Source Coding 141.4. Self-Organizing Networks (SON) 161.5. Summary and Book Contents 171.6. References 192 The Self-Organizing Networks (SON) Paradigm 212.1. Motivation and Targets from NGMN 212.2. SON Use Cases 232.2.1. Use Case Categories 232.2.2. Automatic versus Autonomous Processes 252.2.3. Self-Planning Use Cases 252.2.4. Self-Deployment Use Cases 262.2.5. Self-Optimization Use Cases 282.2.6. Self-Healing Use Cases 322.2.7. SON Enablers 342.3. SON versus Radio Resource Management 352.4. SON in 3GPP 372.4.1. 3GPP Organization 372.4.2. SON Status in 3GPP (up to Release 9) 382.4.3. SON Objectives for 3GPP Release 10 402.5. SON in the Research Community 412.5.1. SOCRATES: Self-Optimization and Self-ConfiguRATion in wirelEss networkS 412.5.2. Celtic Gandalf: Monitoring and Self-Tuning of RRM Parameters in a Multi-System Network 422.5.3. Celtic OPERA-Net: Optimizing Power Efficiency in mobile RAdio Networks 422.5.4. E3: End-to-End Efficiency 432.6. References 433 Multi-Technology SON 473.1. Drivers for Multi-Technology SON 473.2. Architectures for Multi-Technology SON 493.2.1. Deployment Architectures for Self-Organizing Networks 493.2.2. Comparison of SON Architectures 503.2.3. Coordination of SON Functions 533.2.4. Layered Architecture for Centralized Multi-Technology SON 593.3. References 644 Multi-Technology Self-Planning 654.1. Self-Planning Requirements for 2G, 3G and LTE 654.2. Cross-Technology Constraints for Self-Planning 664.3. Self-Planning as an Integrated Process 664.4. Planning versus Optimization 694.5. Information Sources for Self-Planning 704.5.1. Propagation Path-Loss Predictions 704.5.2. Drive Test Measurements 714.6. Automated Capacity Planning 714.6.1. Main Inputs for Automated Capacity Planning 734.6.2. Traffic and Network Load Forecast 744.6.3. Automated Capacity Planning Process 754.6.4. Outputs of the Process and Implementation of Capacity Upgrades in the Network 784.7. Automated Transmission Planning 794.7.1. Self-Organizing Protocols 804.7.2. Additional Requirements for Automated Transmission Planning 824.7.3. Automatic Transmission Planning Process 834.7.4. Automatic Transmission Planning Algorithms 844.7.5. Practical Example 874.8. Automated Site Selection and RF Planning 874.8.1. Solution Space 894.8.2. RF Planning Evaluation Model 904.8.3. RF Optimization Engine 914.8.4. Technology-Specific Aspects of RF Planning 924.9. Automated Neighbor Planning 984.9.1. Technology-Specific Aspects of Neighbor Lists 994.9.2. Principles of Automated Neighbor List Planning 1034.10. Automated Spectrum Planning for GSM/GPRS/EDGE 1054.10.1. Spectrum Planning Objectives 1074.10.2. Inputs to Spectrum Planning 1084.10.3. Automatic Frequency Planning 1124.10.4. Spectrum Self-Planning for GSM/GPRS/EDGE 1144.10.5. Trade-Offs and Spectrum Plan Evaluation 1154.11. Automated Planning of 3G Scrambling Codes 1174.11.1. Scrambling Codes in UMTS-FDD 1174.11.2. Primary Scrambling Code Planning 1194.11.3. PSC Planning and Optimization in SON 1224.12. Automated Planning of LTE Physical Cell Identifiers 1244.12.1. The LTE Physical Cell ID 1244.12.2. Planning LTE Physical Cell IDs 1254.12.3. Automated Planning of PCI in SON 1264.13. References 1275 Multi-Technology Self-Optimization 1315.1. Self-Optimization Requirements for 2G, 3G and LTE 1315.2. Cross-Technology Constraints for Self-Optimization 1325.3. Optimization Technologies 1325.3.1. Control Engineering Techniques for Optimization 1325.3.2. Technology Discussion for Optimizing Cellular Communication Systems 1365.4. Sources for Automated Optimization of Cellular Networks 1365.4.1. Propagation Predictions 1375.4.2. Drive Test Measurements 1375.4.3. Performance Counters Measured at the OSS 1385.4.4. Call Traces 1385.5. Self-Planning versus Open-Loop Self-Optimization 1395.5.1. Minimizing Human Intervention in Open-Loop Automated Optimization Systems 1405.6. Architectures for Automated and Autonomous Optimization 1405.6.1. Centralized, Open-Loop Automated Self-Optimization 1405.6.2. Centralized, Closed-Loop Autonomous Self-Optimization 1415.6.3. Distributed, Autonomous Self-Optimization 1435.7. Open-Loop, Automated Self-Optimization of Cellular Networks 1445.7.1. Antenna Settings 1445.7.2. Neighbor Lists 1465.7.3. Frequency Plans 1485.8. Closed-Loop, Autonomous Self-Optimization of 2G Networks 1485.8.1. Mobility Load Balance for Multi-Layer 2G Networks 1495.8.2. Mobility Robustness Optimization for Multi-Layer 2G Networks 1515.9. Closed-Loop, Autonomous Self-Optimization of 3G Networks 1535.9.1. UMTS Optimization Dimensions 1535.9.2. Key UMTS Optimization Parameters 1555.9.3. Field Results of UMTS RRM Self-Optimization 1635.10. Closed-Loop, Autonomous Self-Optimization of LTE Networks 1655.10.1. Automatic Neighbor Relation 1665.10.2. Mobility Load Balance 1685.10.3. Mobility Robustness Optimization 1765.10.4. Coverage and Capacity Optimization 1785.10.5. RACH Optimization 1795.10.6. Inter-Cell Interference Coordination 1795.10.7. Admission Control Optimization 1845.11. Autonomous Load Balancing for Multi-Technology Networks 1855.11.1. Load Balancing Driven by Capacity Reasons 1865.11.2. Load Balancing Driven by Coverage Reasons 1895.11.3. Load Balancing Driven by Quality Reasons 1905.11.4. Field Results 1905.12. Multi-Technology Energy Saving for Green IT 1915.12.1. Approaching Energy Saving through Different Angles 1925.12.2. Static Energy Saving 1935.12.3. Dynamic Energy Saving 1955.12.4. Operational Challenges 1965.12.5. Field Results 1975.13. Coexistence with Network Management Systems 1975.13.1. Network Management System Concept and Functions 1975.13.2. Other Management Systems 2015.13.3. Interworking between SON Optimization Functions and NMS 2015.14. Multi-Vendor Self-Optimization 2025.15. References 2046 Multi-Technology Self-Healing 2076.1. Self-Healing Requirements for 2G, 3G and LTE 2076.2. The Self-Healing Process 2086.2.1. Detection 2096.2.2. Diagnosis 2106.2.3. Cure 2106.3. Inputs for Self-Healing 2116.4. Self-Healing for Multi-Layer 2G Networks 2116.4.1. Detecting Problems 2116.4.2. Diagnosis 2116.4.3. Cure 2146.5. Self-Healing for Multi-Layer 3G Networks 2146.5.1. Detecting Problems 2146.5.2. Diagnosis 2146.5.3. Cure 2186.6. Self-Healing for Multi-Layer LTE Networks 2206.6.1. Cell Outage Compensation Concepts 2226.6.2. Cell Outage Compensation Algorithms 2236.6.3. Results for P0 Tuning 2246.6.4. Results for Antenna Tilt Optimization 2246.7. Multi-Vendor Self-Healing 2276.8. References 2297 Return on Investment (ROI) for Multi-Technology SON 2317.1. Overview of SON Benefits 2317.2. General Model for ROI Calculation 2337.3. Case Study: ROI for Self-Planning 2357.3.1. Scope of Self-Planning and ROI Components 2357.3.2. Automated Capacity Planning 2377.3.3. Modeling SON for Automated Capacity Planning 2377.3.4. Characterizing the Traffic Profile 2387.3.5. Modeling the Need for Capacity Expansions 2417.3.6. CAPEX Computations 2437.3.7. OPEX Computations 2437.3.8. Sample Scenario and ROI 2457.4. Case Study: ROI for Self-Optimization 2497.4.1. Self-Optimization and ROI Components 2497.4.2. Modeling SON for Self-Optimization 2507.4.3. Characterizing the Traffic Profile 2507.4.4. Modeling the Need for Capacity Expansions 2517.4.5. Quality, Churn and Revenue 2527.4.6. CAPEX Computations 2547.4.7. OPEX Computations 2557.4.8. Sample Scenario and ROI 2557.5. Case Study: ROI for Self-Healing 2607.5.1. OPEX Reduction through Automation 2607.5.2. Extra Revenue due to Improved Quality and Reduced Churn 2607.5.3. Sample Scenario and ROI 2617.6. References 261Appendix A Geo-Location Technology for UMTS 263A.1. Introduction 263A.2. Observed Time Differences (OTDs) 264A.3. Algorithm Description 264A.3.1. Geo-Location of Events 264A.3.2. Synchronization Recovery 265A.3.3. Filtering of Events 265A.4. Scenario and Working Assumptions 266A.5. Results 266A.5.1. Reported Sites per Event 266A.5.2. Event Status Report 268A.5.3. Geo-Location Accuracy 268A.5.4. Impact of Using PD Measurements 269A.6. Concluding Remarks 269A.7. References 271Appendix B X-Map Estimation for LTE 273B.1. Introduction 273B.2. X-Map Estimation Approach 274B.3. Simulation Results 275B.4. References 277Index 279