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This report covers over 120 key ecosystem players in the SON (Self-Organizing Networks) market.

SNS Research's latest report indicates that global spending on SON (Self-Organizing Network) technology will account for over $5 Billion annually by the end of 2020.

SON technology minimizes the lifecycle cost of running a mobile network by eliminating manual configuration of equipment at the time of deployment, right through to dynamically optimizing performance and troubleshooting during operation. This can significantly reduce the cost of the operator’s services, improving the OpEx to revenue ratio.

Amid growing demands for mobile broadband connectivity, mobile operators are keen to capitalize on SON to minimize rollout delays and operational expenditures associated with their ongoing LTE and small cell deployments.

Originally targeted for the RAN (Radio Access Network) segment of mobile networks, SON technology is now also utilized in the mobile core and transport network segments. In addition, Wi-Fi access point OEMs are beginning to integrate SON features such as plug-and-play deployment, autonomous performance optimization, self-healing and proactive defense against unauthorized access.

Despite challenges relating to implementation complexities and multi-vendor interoperability, SON revenue is expected to grow to more than $5 Billion by the end of 2020, exceeding conventional mobile network optimization revenue by a significant margin. Furthermore, the SON ecosystem is increasingly witnessing convergence with other technological innovations such as Big Data, predictive analytics and DPI (Deep Packet Inspection).

The “SON (Self-Organizing Networks) Ecosystem: 2016 - 2030 - Opportunities, Challenges, Strategies & Forecasts” report presents an in-depth assessment of the SON and associated mobile network optimization ecosystem including key market drivers, challenges, OpEx and CapEx savings potential, use cases, SON deployment case studies, future roadmap, value chain, vendor analysis and strategies. The report also presents revenue forecasts for both SON and conventional mobile network optimization, along with individual projections for 10 SON submarkets, 6 regions and 15 countries from 2016 through to 2030.

Topics Covered
The report covers the following topics:
- Conventional mobile network planning & optimization
- SON technology and architecture
- Key benefits and market drivers of SON
- Challenges to SON adoption
- SON use cases
- SON deployment case studies
- Future roadmap of the SON ecosystem
- Company profiles and strategies of over 120 SON ecosystem players
- OpEx and CapEx saving analysis of SON
- Wireless network infrastructure spending, traffic projections and value chain
- Convergence of SON with Big Data, predictive analytics and DPI
- Strategic recommendations for SON solution providers, wireless infrastructure OEMs and mobile operators
- Market analysis and forecasts from 2016 till 2030

The report provides answers to the following key questions:
- How big is the SON and mobile network optimization ecosystem?
- How is the ecosystem evolving by segment and region?
- What will the market size be in 2020 and at what rate will it grow?
- What trends, challenges and barriers are influencing its growth?
- Who are the key SON vendors and what are their strategies?
- What is the outlook for QoE based SON solutions?
- How can SON ease the deployment of unlicensed LTE small cells?
- What SON capabilities will 5G networks entail?
- What is the outlook for C-SON and D-SON adoption?
- How will SON investments compare with those on traditional mobile network optimization?
- What opportunities exist for SON in mobile core and transport networks?
- How will SON use cases evolve overtime in 3GPP releases?
- Which regions will see the highest number of SON investments?
- How much will mobile operators invest in SON solutions?
- What is the outlook for SON associated OpEx savings by region?

Methodology
The contents of this report have been accumulated by combining information attained from a range of primary and secondary research sources. In addition to analyzing official corporate announcements, policy documents, media reports, and industry statements, SNS Research sought opinions from leading industry players within the SON and mobile network optimization ecosystem to derive an unbiased, accurate and objective mix of market trends, forecasts and the future prospects of the industry between 2016 and 2030.

The report targets the following audience:
- SON and mobile network optimization solution providers
- Enabling technology providers and chipset manufacturers
- Wireless network infrastructure vendors
- Mobile operators
- Financial analysts and investors
- Application & software providers

Table Of Contents

The SON (Self-Organizing Networks) Ecosystem: 2016 - 2030 - Opportunities, Challenges, Strategies and Forecasts
1 Chapter 1: Introduction 16
1.1 Executive Summary 16
1.2 Topics Covered 18
1.3 Forecast Segmentation 19
1.4 Key Questions Answered 21
1.5 Key Findings.22
1.6 Methodology 23
1.7 Target Audience 24
1.8 Companies and Organizations Mentioned 25
2 Chapter 2: SON and Mobile Network Optimization Ecosystem 28
2.1 Conventional Mobile Network Optimization 28
2.1.1 Network Planning 28
2.1.2 Measurement Collection: Drive Tests, Probes and End User Data 29
2.1.3 Post-Processing, Optimization and Policy Enforcement 29
2.2 The SON (Self-Organizing Network) Concept 30
2.2.1 What is SON? 30
2.2.2 The Need for SON 30
2.3 Functional Areas of SON 31
2.3.1 Self-Configuration 32
2.3.2 Self-Optimization 32
2.3.3 Self-Healing 32
2.4 Market Drivers for SON Adoption 33
2.4.1 Continued Wireless Network Infrastructure Investments 33
2.4.2 Optimization in Multi-RAN and HetNet Environments 34
2.4.3 OpEx and CapEx Reduction: The Cost Saving Potential 36
2.4.4 Improving Subscriber Experience and Churn Reduction 36
2.4.5 Power Savings 37
2.4.6 Enabling Small Cell Deployments 37
2.4.7 Traffic Management 37
2.5 Market Barriers for SON Adoption 38
2.5.1 Complexity of Implementation 38
2.5.2 Reorganization and Changes to Standard Engineering Procedures 38
2.5.3 Lack of Trust in Automation 38
2.5.4 Lack of Operator Control: Proprietary SON Algorithms 38
2.5.5 Coordination between Distributed and Centralized SON 39
2.5.6 Network Security Concerns: New Interfaces and Lack of Monitoring 39
3 Chapter 3: SON Technology, Use Cases and Implementation Architectures 40
3.1 Where Does SON Sit Within a Mobile Network? 40
3.1.1 RAN41
3.1.2 Mobile Core 41
3.1.3 Mobile Backhaul and Fronthaul 42
3.1.4 Device-Assisted SON 43
3.2 SON Architecture 44
3.2.1 C-SON (Centralized SON) 44
3.2.2 D-SON (Distributed SON) 45
3.2.3 H-SON (Hybrid SON) 46
3.3 SON Use-Cases 47
3.3.1 Self-Configuration of Network Elements 47
3.3.2 Automatic Connectivity Management 47
3.3.3 Self-Testing of Network Elements 47
3.3.4 Self-Recovery of Network Elements/Software 48
3.3.5 Self-Healing of Board Faults 48
3.3.6 Automatic Inventory 48
3.3.7 ANR (Automatic Neighbor Relations) 48
3.3.8 PCI (Physical Cell ID) Configuration 49
3.3.9 CCO (Coverage and Capacity Optimization) 49
3.3.10 MRO (Mobility Robustness Optimization) 49
3.3.11 MLB (Mobile Load Balancing) 50
3.3.12 RACH (Random Access Channel) Optimization 50
3.3.13 ICIC (Inter-Cell Interference Coordination) 50
3.3.14 eICIC (Enhanced ICIC) 51
3.3.15 Energy Savings 51
3.3.16 Cell Outage Detection and Compensation 51
3.3.17 Self-Configuration and Optimization of Small Cells 52
3.3.18 Optimization of DAS (Distributed Antenna Systems )52
3.3.19 RAN Aware Traffic Shaping 52
3.3.20 Traffic Steering in HetNets 53
3.3.21 Optimization of Virtualized Network Resources 53
3.3.22 Auto-Provisioning of Transport Links 53
3.3.23 Transport Network Bandwidth Optimization 53
3.3.24 Transport Network Interference Management 53
3.3.25 SON Coordination Management 54
3.3.26 Seamless Vendor Infrastructure Swap 54
4 Chapter 4: SON Standardization 55
4.1 NGNM (Next Generation Mobile Networks) Alliance 55
4.1.1 Conception of the SON Initiative 55
4.1.2 Functional Areas and Requirements 56
4.1.3 Implementation Approach 57
4.1.4 P-SmallCell (Project Small Cell) 57
4.1.5 Recommendations for Multi-Vendor SON Deployment 58
4.2 3GPP (Third Generation Partnership Project) 59
4.2.1 Release 8 59
4.2.2 Release 9 60
4.2.3 Release 10 60
4.2.4 Release 11 60
4.2.5 Release 12, 13 and Beyond 61
4.2.6 Implementation Approach 62
4.3 Small Cell Forum 62
4.3.1 Release 7: Focus on SON for Small Cells 62
4.3.2 SON API 63
4.3.3 X2 Interoperability 63
4.4 WBA (Wireless Broadband Alliance) 64
4.4.1 SON Integration in Carrier Wi-Fi Guidelines 64
4.5 CableLabs 65
4.5.1 SON Parameter Exchange in Wi-Fi Gateway Management Specification 65
5 Chapter 5: SON Deployment Case Studies 66
5.1 ATandT 66
5.1.1 Vendor Selection 66
5.1.2 Implemented Use Cases 66
5.1.3 Results 67
5.2 Globe Telecom 67
5.2.1 Vendor Selection 67
5.2.2 Implemented Use Cases 67
5.2.3 Results 68
5.3 KDDI Corporation 69
5.3.1 Vendor Selection 69
5.3.2 Implemented Use Cases 69
5.3.3 Results 69
5.4 Singtel Group 70
5.4.1 Vendor Selection 70
5.4.2 Implemented Use Cases 70
5.4.3 Results 70
5.5 SK Telecom 71
5.5.1 Vendor Selection 71
5.5.2 Implemented Use Cases 71
5.5.3 Results 71
5.6 Telefónica Group 72
5.6.1 Vendor Selection 72
5.6.2 Implemented Use Cases 72
5.6.3 Results 72
5.7 TIM (Telecom Italia Mobile) 73
5.7.1 Vendor Selection 73
5.7.2 Implemented Use Cases 73
5.7.3 Results 74
5.8 Turkcell Group 74
5.8.1 Vendor Selection 74
5.8.2 Implemented Use Cases 74
5.8.3 Results 75
5.9 Vodafone Group 76
5.9.1 Vendor Selection 76
5.9.2 Implemented Use Cases 76
5.9.3 Results 76
6 Chapter 6: Industry Roadmap and Value Chain 78
6.1 Industry Roadmap 78
6.1.1 Large Scale Adoption of SON Technology: 2016 - 2020 78
6.1.2 Towards QoE/QoS Based End-to-End SON: 2020 - 2025 79
6.1.3 Continued Investments to Support 5G Rollouts: 2025 - 2030 79
6.2 Value Chain 80
6.3 Embedded Technology Ecosystem 80
6.3.1 Chipset Developer 80
6.3.2 Embedded Component/Software Providers 80
6.4 RAN Ecosystem 82
6.4.1 Macrocell RAN OEMs 82
6.4.2 Pure-Play Small Cell OEMs 82
6.4.3 Wi-Fi Access Point OEMs 82
6.4.4 DAS and Repeater Solution Providers 83
6.4.5 C-RAN Solution Providers 83
6.4.6 Other Technology Providers 83
6.5 Transport Networking Ecosystem 83
6.5.1 Backhaul and Fronthaul Solution Providers 83
6.6 Mobile Core Ecosystem 84
6.6.1 Mobile Core Solution Providers 84
6.7 Connectivity Ecosystem 84
6.7.1 Mobile Operators 84
6.7.2 Wi-Fi Connectivity Providers 84
6.7.3 SCaaS (Small Cells as a Service) Providers 85
6.8 SON Ecosystem 85
6.8.1 SON Solution Providers 85
6.9 SDN and NFV Ecosystem 85
6.9.1 SDN and NFV Providers 85
7 Chapter 7: Vendor Landscape 86
7.1 Accedian Networks 86
7.2 Accelleran 87
7.3 Accuver 88
7.4 AirHop Communications .89
7.5 Airspan Networks 90
7.6 Alvarion Technologies. 91
7.7 Altiostar Networks. 92
7.8 Amdocs 93
7.9 Arcadyan Technology Corporation 95
7.10 Argela . 96
7.11 Aricent 97
7.12 ARItel 98
7.13 Artemis Networks 99
7.14 Astellia100
7.15 ASUS (ASUSTeK Computer) 101
7.16 ATDI 102
7.17 Avvasi .103
7.18 Baicells .104
7.19 Belkin International 105
7.20 Benu Networks. 106
7.21 BLiNQ Networks. 107
7.22 Broadcom.108
7.23 Brocade Communications Systems 109
7.24 Casa Systems110
7.25 Cavium111
7.26 CBNL (Cambridge Broadband Networks Limited) 112
7.27 CCS (Cambridge Communication Systems)113
7.28 CellMining 114
7.29 Cellwize 115
7.30 Celtro116
7.31 CENTRI117
7.32 Cisco Systems.118
7.33 Citrix Systems.119
7.34 Comarch .120
7.35 CommAgility .121
7.36 CommScope .122
7.37 Commsquare123
7.38 Contela .124
7.39 Coriant125
7.40 Datang Mobile126
7.41 Dell EMC.127
7.42 Digitata.128
7.43 D-Link Corporation .129
7.44 ECE (European Communications Engineering).130
7.45 Equiendo 131
7.46 Ericsson 132
7.47 Ercom .133
7.48 EXFO.134
7.49 Flash Networks.135
7.50 Forsk.136
7.51 Fujitsu.137
7.52 Gemtek Technology Company .138
7.53 General Dynamics Mission Systems.139
7.54 GoNet Systems.140
7.55 Guavus141
7.56 GWT (Global Wireless Technologies) .142
7.57 Hitachi 143
7.58 Huawei .144
7.59 InfoVista .145
7.60 Innovile.146
7.61 Intel Corporation147
7.62 InterDigital .148
7.63 Intracom Telecom 149
7.64 ip.access.150
7.65 JRC (Japan Radio Company) .151
7.66 Juni Global152
7.67 Keysight Technologies153
7.68 Kumu Networks154
7.69 Lemko Corporation 155
7.70 Luminate Wireless156
7.71 Mojo Networks.157
7.72 NEC Corporation 158
7.73 NetScout Systems.159
7.74 New Postcom Equipment Company.160
7.75 Nokia Networks161
7.76 Nutaq162
7.77 NXP Semiconductors163
7.78 Oceus Networks.164
7.79 Opera Software 165
7.80 Optulink166
7.81 Parallel Wireless.167
7.82 P.I.Works168
7.83 Phluido .169
7.84 Plano Engineering 170
7.85 Potevio (China Potevio Company)171
7.86 Qualcomm172
7.87 Quanta Computer 174
7.88 Qucell .175
7.89 RADCOM 176
7.90 Radisys Corporation .177
7.91 RED Technologies.178
7.92 Redline Communications .179
7.93 Rohde and Schwarz .180
7.112 Tulinx .199
7.113 Vasona Networks.200
7.114 Viavi Solutions201
7.115 WebRadar202
7.116 WNC (Wistron NeWeb Corporation) .203
7.117 WPOTECH 204
7.118 XCellAir.205
7.119 Z-Com (ZDC Wireless) 206
7.120 ZTE .207
7.121 ZyXEL Communications Corporation .208
8 Chapter 8: Market Analysis and Forecasts.209
8.1 SON and Mobile Network Optimization Revenue 209
8.2 SON Revenue.210
8.3 SON Revenue by Network Segment210
8.3.1 Conventional Macrocell RAN.211
8.3.2 HetNet RAN .211
8.3.3 Mobile Core.212
8.3.4 Mobile Backhaul and Fronthaul212
8.4 SON Revenue by Architecture: Centralized vs. Distributed.213
8.4.1 C-SON 213
8.4.2 D-SON214
8.5 SON Revenue by Access Network Technology 214
8.5.1 2G and 3G .215
8.5.2 LTE .215
8.5.3 Wi-Fi 216
8.5.4 5G 216
8.6 SON Revenue by Region 217
8.7 Conventional Mobile Network Planning and Optimization Revenue .217
8.8 Conventional Mobile Network Planning and Optimization Revenue by Region 218
8.9 Asia Pacific.219
8.9.1 SON219
8.9.2 Conventional Mobile Network Planning and Optimization 219
8.10 Eastern Europe.220
8.10.1 SON220
8.10.2 Conventional Mobile Network Planning and Optimization 220
8.11 Latin and Central America .221
8.11.1 SON221
8.11.2 Conventional Mobile Network Planning and Optimization 221
8.12 Middle East and Africa.222
8.12.1 SON222
8.12.2 Conventional Mobile Network Planning and Optimization 222
8.13 North America223
8.13.1 SON223
8.13.2 Conventional Mobile Network Planning and Optimization 223
8.14 Western Europe .224
8.14.1 SON224
8.14.2 Conventional Mobile Network Planning and Optimization 224
8.15 Top Country Markets .225
8.15.1 Australia 225
8.15.2 Brazil225
8.15.3 Canada.226
8.15.4 China226
8.15.5 France227
8.15.6 Germany 227
8.15.7 India.228
8.15.8 Italy228
8.15.9 Japan229
8.15.10 Russia.229
8.15.11 South Korea .230
8.15.12 Spain230
8.15.13 Taiwan .231
8.15.14 UK 231
8.15.15 USA 232
9 Chapter 9: Key Trends, Conclusion and Strategic Recommendations.233
9.1 Moving Towards QoE Based SON Platforms .233
9.2 Capitalizing on DPI (Deep Packet Inspection)233
9.3 The Convergence of Big Data, Predictive Analytics and SON .234
9.4 Optimizing M2M and IoT Services235
9.5 SON for NFV and SDN: The Push from Mobile Operators 235
9.6 Moving Towards Mobile Core and Transport Networks.236
9.7 Assessing the Impact of SON on Optimization and Field Engineers.236
9.8 Impact of Unlicensed LTE Small Cells238
9.9 Growing Adoption of SON Capabilities for Wi-Fi.240
9.10 SON Associated OpEx Savings: The Numbers 241
9.11 What SON Capabilities Will 5G Networks Entail? 242
9.11.1 Predictive Resource Allocation242
9.11.2 Addressing D2D (Device-to-Device) Communications and New Use Cases243
9.11.3 User-Based Profiling and Optimization for Vertical 5G Applications243
9.11.4 Greater Focus on Self-Protection Capabilities.244
9.12 The C-SON Versus D-SON Debate 244
9.13 Strategic Recommendations245
9.13.1 SON and Conventional Mobile Network Optimization Solution Providers.245
9.13.2 Wireless Infrastructure OEMs .246
9.13.3 Mobile Operators247


List of Figures

Figure 1: Functional Areas of SON within the Mobile Network Lifecycle 31
Figure 2: Annual Throughput of Mobile Network Data Traffic by Region: 2016 - 2030 (Exabytes) .33
Figure 3: Global Wireless Network Infrastructure Revenue Share by Submarket (%) .34
Figure 4: Global Mobile Network Data Traffic Distribution by Access Network Form Factor: 2016 - 2030 (%) .35
Figure 5: SON Associated OpEx and CapEx Savings by Network Segment36
Figure 6: Potential Areas of SON Implementation.40
Figure 7: Mobile Backhaul and Fronthaul Segmentation by Technology 42
Figure 8: C-SON (Centralized SON) in a Mobile Operator Network .44
Figure 9: D-SON (Distributed SON) in a Mobile Operator Network .45
Figure 10: H-SON (Hybrid SON) in a Mobile Operator Network 46
Figure 11: NGNM SON Use Cases56
Figure 12: SON Industry Roadmap: 2016 - 203078
Figure 13: Wireless Network Infrastructure Value Chain 81
Figure 14: Global SON and Mobile Network Optimization Revenue: 2016 - 2030 ($ Million). 209
Figure 15: Global SON Revenue: 2016 - 2030 ($ Million). 210
Figure 16: Global SON Revenue by Network Segment: 2016 - 2030 ($ Million) 210
Figure 17: Global Macrocell RAN SON Revenue: 2016 - 2030 ($ Million) 211
Figure 18: Global HetNet RAN SON Revenue: 2016 - 2030 ($ Million) 211
Figure 19: Global Mobile Core SON Revenue: 2016 - 2030 ($ Million) 212
Figure 20: Global Mobile Backhaul and Fronthaul SON Revenue: 2016 - 2030 ($ Million) . 212
Figure 21: Global SON Revenue by Architecture: 2016 - 2030 ($ Million) . 213
Figure 22: Global C-SON Revenue: 2016 - 2030 ($ Million) . 213
Figure 23: Global D-SON Revenue: 2016 - 2030 ($ Million). 214
Figure 24: Global SON Revenue by Access Network Technology: 2016 - 2030 ($ Million) 214
Figure 25: Global 2G and 3G SON Revenue: 2016 - 2030 ($ Million) 215
Figure 26: Global LTE SON Revenue: 2016 - 2030 ($ Million) 215
Figure 27: Global Wi-Fi SON Revenue: 2016 - 2030 ($ Million) . 216
Figure 28: Global 5G SON Revenue: 2020 - 2030 ($ Million) . 216
Figure 29: SON Revenue by Region: 2016 - 2030 ($ Million) . 217
Figure 30: Global Conventional Mobile Network Planning and Optimization Revenue: 2016 - 2030 ($ Million) 217
Figure 31: Conventional Mobile Network Planning and Optimization Revenue by Region: 2016 - 2030 ($ Million) 218
Figure 32: Asia Pacific SON Revenue: 2016 - 2030 ($ Million) . 219
Figure 33: Asia Pacific Conventional Mobile Network Planning and Optimization Revenue: 2016 - 2030 ($ Million) 219
Figure 34: Eastern Europe SON Revenue: 2016 - 2030 ($ Million). 220
Figure 35: Eastern Europe Conventional Mobile Network Planning and Optimization Revenue: 2016 - 2030 ($ Million) 220
Figure 36: Latin and Central America SON Revenue: 2016 - 2030 ($ Million). 221
Figure 37: Latin and Central America Conventional Mobile Network Planning and Optimization Revenue: 2016 - 2030 ($ Million) 221
Figure 38: Middle East and Africa SON Revenue: 2016 - 2030 ($ Million) 222
Figure 39: Middle East and Africa Conventional Mobile Network Planning and Optimization Revenue: 2016 - 2030 ($ Million) 222
Figure 40: North America SON Revenue: 2016 - 2030 ($ Million) . 223
Figure 41: North America Conventional Mobile Network Planning and Optimization Revenue: 2016 - 2030 ($ Million). 223
Figure 42: Western Europe SON Revenue: 2016 - 2030 ($ Million). 224
Figure 43: Western Europe Conventional Mobile Network Planning and Optimization Revenue: 2016 - 2030 ($ Million) 224
Figure 44: Australia SON Revenue: 2016 - 2030 ($ Million). 225
Figure 45: Brazil SON Revenue: 2016 - 2030 ($ Million) 225
Figure 46: Canada SON Revenue: 2016 - 2030 ($ Million). 226
Figure 47: China SON Revenue: 2016 - 2030 ($ Million) 226
Figure 48: France SON Revenue: 2016 - 2030 ($ Million) 227
Figure 49: Germany SON Revenue: 2016 - 2030 ($ Million) 227
Figure 50: India SON Revenue: 2016 - 2030 ($ Million). 228
Figure 51: Italy SON Revenue: 2016 - 2030 ($ Million) 228
Figure 52: Japan SON Revenue: 2016 - 2030 ($ Million) 229
Figure 53: Russia SON Revenue: 2016 - 2030 ($ Million). 229
Figure 54: South Korea SON Revenue: 2016 - 2030 ($ Million) . 230
Figure 55: Spain SON Revenue: 2016 - 2030 ($ Million) 230
Figure 56: Taiwan SON Revenue: 2016 - 2030 ($ Million) . 231
Figure 57: UK SON Revenue: 2016 - 2030 ($ Million) 231
Figure 58: USA SON Revenue: 2016 - 2030 ($ Million) 232
Figure 59: Global Unlicensed LTE Small Cell Unit Shipments: 2016 - 2030 (Thousands of Units) 238
Figure 60: Global Unlicensed LTE Small Cell Unit Shipment Revenue: 2016 - 2030 ($ Million) 239
Figure 61: SON Associated OpEx Savings by Region: 2016 - 2030 ($ Million) 24

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