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

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Edited by (Optimi Corporation, Spain), Edited by (Optimi Corporation, Spain)
  • Formāts: PDF+DRM
  • Izdošanas datums: 24-Oct-2011
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781119954217
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Self-Organizing Networks: Self-Planning, Self-Optimization and Self-Healing for GSM, UMTS and LTEPalielināt
  • Formāts: PDF+DRM
  • Izdošanas datums: 24-Oct-2011
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781119954217
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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 todays GSM and UMTS networks Features detailed discussions about the relevant trade-offs in each Use Case Includes field results of todays GSM and UMTS SON implementations in live networks Addresses the calculation of ROI for Multi-Technology SON, contributing to a more complete and strategic view of the SON paradigm

This 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.
Foreword xi
Preface xiii
Acknowledgements xv
List of Contributors
xvii
List of Abbreviations
xix
1 Operating Mobile Broadband Networks
1(20)
1.1 The Challenge of Mobile Traffic Growth
1(4)
1.1.1 Differences between Smartphones
3(2)
1.1.2 Driving Data Traffic - Streaming Media and Other Services
5(1)
1.2 Capacity and Coverage Crunch
5(1)
1.3 Meeting the Challenge - the Network Operator Toolkit
6(10)
1.3.1 Tariff Structures
6(1)
1.3.2 Advanced Radio Access Technologies
7(3)
1.3.3 Femto Cells
10(1)
1.3.4 Acquisition and Activation of New Spectrum
11(1)
1.3.5 Companion Networks, Offloading and Traffic Management
12(2)
1.3.6 Advanced Source Coding
14(2)
1.4 Self-Organizing Networks (SON)
16(1)
1.5 Summary and Book Contents
17(2)
1.6 References
19(2)
2 The Self-Organizing Networks (SON) Paradigm
21(26)
2.1 Motivation and Targets from NGMN
21(2)
2.2 SON Use Cases
23(12)
2.2.1 Use Case Categories
23(2)
2.2.2 Automatic versus Autonomous Processes
25(1)
2.2.3 Self-Planning Use Cases
25(1)
2.2.4 Self-Deployment Use Cases
26(2)
2.2.5 Self-Optimization Use Cases
28(4)
2.2.6 Self-Healing Use Cases
32(2)
2.2.7 SON Enablers
34(1)
2.3 SON versus Radio Resource Management
35(2)
2.4 SON in 3GPP
37(4)
2.4.1 3GPP Organization
37(1)
2.4.2 SON Status in 3GPP (up to Release 9)
38(2)
2.4.3 SON Objectives for 3GPP Release 10
40(1)
2.5 SON in the Research Community
41(2)
2.5.1 SOCRATES: Self-Optimization and Self-ConfiguRATion in wirelEss networkS
41(1)
2.5.2 Celtic Gandalf: Monitoring and Self-Tuning of RRM Parameters in a Multi-System Network
42(1)
2.5.3 Celtic OPERA-Net: Optimizing Power Efficiency in mobile RAdio Networks
42(1)
2.5.4 E3: End-to-End Efficiency
43(1)
2.6 References
43(4)
3 Multi-Technology SON
47(18)
3.1 Drivers for Multi-Technology SON
47(2)
3.2 Architectures for Multi-Technology SON
49(15)
3.2.1 Deployment Architectures for Self-Organizing Networks
49(1)
3.2.2 Comparison of SON Architectures
50(3)
3.2.3 Coordination of SON Functions
53(6)
3.2.4 Layered Architecture for Centralized Multi-Technology SON
59(5)
3.3 References
64(1)
4 Multi-Technology Self-Planning
65(66)
4.1 Self-Planning Requirements for 2G, 3G and LTE
65(1)
4.2 Cross-Technology Constraints for Self-Planning
66(1)
4.3 Self-Planning as an Integrated Process
66(3)
4.4 Planning versus Optimization
69(1)
4.5 Information Sources for Self-Planning
70(1)
4.5.1 Propagation Path-Loss Predictions
70(1)
4.5.2 Drive Test Measurements
71(1)
4.6 Automated Capacity Planning
71(8)
4.6.1 Main Inputs for Automated Capacity Planning
73(1)
4.6.2 Traffic and Network Load Forecast
74(1)
4.6.3 Automated Capacity Planning Process
75(3)
4.6.4 Outputs of the Process and Implementation of Capacity Upgrades in the Network
78(1)
4.7 Automated Transmission Planning
79(8)
4.7.1 Self-Organizing Protocols
80(2)
4.7.2 Additional Requirements for Automated Transmission Planning
82(1)
4.7.3 Automatic Transmission Planning Process
83(1)
4.7.4 Automatic Transmission Planning Algorithms
84(3)
4.7.5 Practical Example
87(1)
4.8 Automated Site Selection and RF Planning
87(11)
4.8.1 Solution Space
89(1)
4.8.2 RF Planning Evaluation Model
90(1)
4.8.3 RF Optimization Engine
91(1)
4.8.4 Technology-Specific Aspects of RF Planning
92(6)
4.9 Automated Neighbor Planning
98(7)
4.9.1 Technology-Specific Aspects of Neighbor Lists
99(4)
4.9.2 Principles of Automated Neighbor List Planning
103(2)
4.10 Automated Spectrum Planning for GSM/GPRS/EDGE
105(12)
4.10.1 Spectrum Planning Objectives
107(1)
4.10.2 Inputs to Spectrum Planning
108(4)
4.10.3 Automatic Frequency Planning
112(2)
4.10.4 Spectrum Self-Planning for GSM/GPRS/EDGE
114(1)
4.10.5 Trade-Offs and Spectrum Plan Evaluation
115(2)
4.11 Automated Planning of 3G Scrambling Codes
117(7)
4.11.1 Scrambling Codes in UMTS-FDD
117(2)
4.11.2 Primary Scrambling Code Planning
119(3)
4.11.3 PSC Planning and Optimization in SON
122(2)
4.12 Automated Planning of LTE Physical Cell Identifiers
124(3)
4.12.1 The LTE Physical Cell ID
124(1)
4.12.2 Planning LTE Physical Cell IDs
125(1)
4.12.3 Automated Planning of PCI in SON
126(1)
4.13 References
127(4)
5 Multi-Technology Self-Optimization
131(76)
5.1 Self-Optimization Requirements for 2G, 3G and LTE
131(1)
5.2 Cross-Technology Constraints for Self-Optimization
132(1)
5.3 Optimization Technologies
132(4)
5.3.1 Control Engineering Techniques for Optimization
132(4)
5.3.2 Technology Discussion for Optimizing Cellular Communication Systems
136(1)
5.4 Sources for Automated Optimization of Cellular Networks
136(3)
5.4.1 Propagation Predictions
137(1)
5.4.2 Drive Test Measurements
137(1)
5.4.3 Performance Counters Measured at the OSS
138(1)
5.4.4 Call Traces
138(1)
5.5 Self-Planning versus Open-Loop Self-Optimization
139(1)
5.5.1 Minimizing Human Intervention in Open-Loop Automated Optimization Systems
140(1)
5.6 Architectures for Automated and Autonomous Optimization
140(4)
5.6.1 Centralized, Open-Loop Automated Self-Optimization
140(1)
5.6.2 Centralized, Closed-Loop Autonomous Self-Optimization
141(2)
5.6.3 Distributed, Autonomous Self-Optimization
143(1)
5.7 Open-Loop, Automated Self-Optimization of Cellular Networks
144(4)
5.7.1 Antenna Settings
144(2)
5.7.2 Neighbor Lists
146(2)
5.7.3 Frequency Plans
148(1)
5.8 Closed-Loop, Autonomous Self-Optimization of 2G Networks
148(5)
5.8.1 Mobility Load Balance for Multi-Layer 2G Networks
149(2)
5.8.2 Mobility Robustness Optimization for Multi-Layer 2G Networks
151(2)
5.9 Closed-Loop, Autonomous Self-Optimization of 3G Networks
153(12)
5.9.1 UMTS Optimization Dimensions
153(2)
5.9.2 Key UMTS Optimization Parameters
155(8)
5.9.3 Field Results of UMTS RRM Self-Optimization
163(2)
5.10 Closed-Loop, Autonomous Self-Optimization of LTE Networks
165(20)
5.10.1 Automatic Neighbor Relation
166(2)
5.10.2 Mobility Load Balance
168(8)
5.10.3 Mobility Robustness Optimization
176(2)
5.10.4 Coverage and Capacity Optimization
178(1)
5.10.5 RACH Optimization
179(1)
5.10.6 Inter-Cell Interference Coordination
179(5)
5.10.7 Admission Control Optimization
184(1)
5.11 Autonomous Load Balancing for Multi-Technology Networks
185(6)
5.11.1 Load Balancing Driven by Capacity Reasons
186(3)
5.11.2 Load Balancing Driven by Coverage Reasons
189(1)
5.11.3 Load Balancing Driven by Quality Reasons
190(1)
5.11.4 Field Results
190(1)
5.12 Multi-Technology Energy Saving for Green IT
191(6)
5.12.1 Approaching Energy Saving through Different Angles
192(1)
5.12.2 Static Energy Saving
193(2)
5.12.3 Dynamic Energy Saving
195(1)
5.12.4 Operational Challenges
196(1)
5.12.5 Field Results
197(1)
5.13 Coexistence with Network Management Systems
197(5)
5.13.1 Network Management System Concept and Functions
197(4)
5.13.2 Other Management Systems
201(1)
5.13.3 Interworking between SON Optimization Functions and NMS
201(1)
5.14 Multi-Vendor Self-Optimization
202(2)
5.15 References
204(3)
6 Multi-Technology Self-Healing
207(24)
6.1 Self-Healing Requirements for 2G, 3G and LTE
207(1)
6.2 The Self-Healing Process
208(3)
6.2.1 Detection
209(1)
6.2.2 Diagnosis
210(1)
6.2.3 Cure
210(1)
6.3 Inputs for Self-Healing
211(1)
6.4 Self-Healing for Multi-Layer 2G Networks
211(3)
6.4.1 Detecting Problems
111(1)
6.4.2 Diagnosis
111(103)
6.4.3 Cure
214(1)
6.5 Self-Healing for Multi-Layer 3G Networks
214(6)
6.5.1 Detecting Problems
214(1)
6.5.2 Diagnosis
214(4)
6.5.3 Cure
218(2)
6.6 Self-Healing for Multi-Layer LTE Networks
220(7)
6.6.1 Cell Outage Compensation Concepts
222(1)
6.6.2 Cell Outage Compensation Algorithms
223(1)
6.6.3 Results for P0 Tuning
224(1)
6.6.4 Results for Antenna Tilt Optimization
224(3)
6.7 Multi-Vendor Self-Healing
227(2)
6.8 References
229(2)
7 Return on Investment (ROI) for Multi-Technology SON
231(32)
7.1 Overview of SON Benefits
231(2)
7.2 General Model for ROI Calculation
233(2)
7.3 Case Study: ROI for Self-Planning
235(14)
7.3.1 Scope of Self-Planning and ROI Components
235(2)
7.3.2 Automated Capacity Planning
237(1)
7.3.3 Modeling SON for Automated Capacity Planning
237(1)
7.3.4 Characterizing the Traffic Profile
238(3)
7.3.5 Modeling the Need for Capacity Expansions
241(2)
7.3.6 CAPEX Computations
243(1)
7.3.7 OPEX Computations
243(2)
7.3.8 Sample Scenario and ROI
245(4)
7.4 Case Study: ROI for Self-Optimization
249(11)
7.4.1 Self-Optimization and ROI Components
249(1)
7.4.2 Modeling SON for Self-Optimization
250(1)
7.4.3 Characterizing the Traffic Profile
250(1)
7.4.4 Modeling the Need for Capacity Expansions
251(1)
7.4.5 Quality, Churn and Revenue
252(2)
7.4.6 CAPEX Computations
254(1)
7.4.7 OPEX Computations
255(1)
7.4.8 Sample Scenario and ROI
255(5)
7.5 Case Study: ROI for Self-Healing
260(1)
7.5.1 OPEX Reduction through Automation
260(1)
7.5.2 Extra Revenue due to Improved Quality and Reduced Churn
260(1)
7.5.3 Sample Scenario and ROI
261(1)
7.6 References
261(2)
Appendix A Geo-Location Technology for UMTS
263(10)
A.1 Introduction
263(1)
A.2 Observed Time Differences (OTDs)
264(1)
A.3 Algorithm Description
264(2)
A.3.1 Geo-Location of Events
264(1)
A.3.2 Synchronization Recovery
265(1)
A.3.3 Filtering of Events
265(1)
A.4 Scenario and Working Assumptions
266(1)
A.5 Results
266(3)
A.5.1 Reported Sites per Event
266(2)
A.5.2 Event Status Report
268(1)
A.5.3 Geo-Location Accuracy
268(1)
A.5.4 Impact of Using PD Measurements
269(1)
A.6 Concluding Remarks
269(2)
A.7 References
271(2)
Appendix B X-Map Estimation for LTE
273(6)
B.1 Introduction
273(1)
B.2 X-Map Estimation Approach
274(1)
B.3 Simulation Results
275(2)
B.4 References
277(2)
Index 279
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 Mįlaga (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.
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