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The Self-Organizing Networks (SON) Ecosystem: 2014 - 2020

Report Code : snst0133
Published Date : 17 March, 2014 | No of Pages: 186

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Self-Organizing Network (SON) technology minimizes the lifecycle cost of running a wireless carrier 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 carrier’s services, improving the OpEx to revenue ratio.

Amid growing demands for mobile broadband connectivity, wireless carriers 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 Radio Access Network (RAN) segment of wireless carrier networks, SON technology is now also utilized in the mobile core and mobile backhaul segments. Furthermore, the SON ecosystem is increasingly witnessing convergence with other technological innovations such as Big Data analytics and Deep Packet Inspection (DPI).

Despite challenges relating to implementation complexities and multi-vendor interoperability, SON revenue is expected to grow to more than $3 Billion by the end of 2016, exceeding conventional mobile network optimization revenue by over 20%.

The “Self-Organizing Networks (SON) Ecosystem: 2014 – 2020” 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 8 SON submarkets from 2014 through to 2020. Historical figures are also presented for 2010, 2011, 2012 and 2013.

The report comes with an associated Excel datasheet suite covering quantitative data from all numeric forecasts presented in the report.Key Findings:
The report has the following key findings:
- Despite challenges relating to implementation complexities and multi-vendor interoperability, SON revenue is expected to grow to more than $3 Billion by the end of 2016, exceeding conventional mobile network optimization revenue by over 20%
- Driven by large scale TD-LTE rollouts and ongoing SON deployments, the Asia Pacific region will account for nearly 40% of the global mobile network optimization market by 2016
- SNS Research estimates that SON can enable wireless carriers to save up to 35% of their electrical power consumption by dynamically by activating and deactivating RAN nodes in line with the changing traffic and user distribution profile
- SNS Research estimates that a Tier 1 wireless carrier can save as much as 32% of its overall OpEx by employing SON across the RAN, mobile core and mobile backhaul segments of the network
- Wireless carriers have reported up to a 40% reduction in dropped calls and over 20% higher data rates with SON implementation
- Infrastructure and software incumbents are aggressively eyeing on acquisitions of smaller established C-SON players to accelerate their early entry path into the C-SON market

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
- Company profiles and strategies of over 60 SON ecosystem players
- OpEx and CapEx saving analysis of SON
- Wireless network infrastructure spending and traffic projections
- Wireless network infrastructure industry roadmap and value chain
- Future roadmap of the SON ecosystem
- Convergence of SON with other technologies (such as Big Data analytics)
- Strategic recommendations for SON solution providers, wireless infrastructure OEMs and wireless carriers
- Market analysis and forecasts from 2014 till 2020


Forecast Segmentation:
Market forecasts and historical figures are provided for each of the following submarkets and their subcategories:

Mobile Network Optimization
- SON
- Conventional Mobile Network Planning & Optimization

SON Submarkets
- Macrocell RAN
- HetNet/Small Cell RAN
- Mobile Core
- Mobile Backhaul

SON Architecture Submarkets
- C-SON (Centralized SON)
- D-SON (Distributed SON)

SON Wireless Network Generation Submarkets
- 2G/3G
- 4G

SON CapEx & OpEx Savings Submarkets
- RAN
- Mobile Core
- Mobile Backhaul

Regional Submarkets
- Asia Pacific
- Eastern Europe
- Latin & Central America
- Middle East & Africa
- North America
- Western Europe

Key Questions Answered:
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?
- What is the outlook for C-SON and D-SON adoption?
- What is the outlook for SON associated OpEx savings by region?
- How will SON investments compare with those on traditional mobile network optimization?
- What opportunities exist for SON in mobile core and mobile backhaul?
- How will SON use cases evolve overtime in 3GPP releases?
- Which regions will see the highest number of SON investments?
- How much will wireless carriers invest in SON solutions?

1 Chapter 1: Introduction
1.1 Executive Summary
1.2 Topics Covered
1.3 Historical Revenue & Forecast Segmentation
1.4 Key Questions Answered
1.5 Key Findings
1.6 Methodology
1.7 Target Audience
1.8 Companies & Organizations Mentioned

2 Chapter 2: SON & Mobile Network Optimization Ecosystem
2.1 Conventional Mobile Network Optimization
2.1.1 Network Planning
2.1.2 Measurement Collection: Drive Tests, Probes and End User Data
2.1.3 Post-Processing, Optimization & Policy Enforcement
2.2 The SON (Self-Organizing Network) Concept
2.2.1 What is SON?
2.2.2 The Need for SON
2.3 Functional Areas of SON
2.3.1 Self-Configuration
2.3.2 Self-Optimization
2.3.3 Self-Healing
2.4 Market Drivers for SON Adoption
2.4.1 Continued Wireless Network Infrastructure Investments
2.4.2 Optimization in Multi-RAN & HetNet Environments
2.4.3 OpEx & CapEx Reduction: The Cost Saving Potential
2.4.4 Improving Subscriber Experience and Churn Reduction
2.4.5 Power Savings
2.4.6 Enabling Small Cell Deployments
2.4.7 Traffic Management
2.5 Market Barriers for SON Adoption
2.5.1 Complexity of Implementation
2.5.2 Reorganization & Changes to Standard Engineering Procedures
2.5.3 Lack of Trust in Automation
2.5.4 Lack of Operator Control: Proprietary SON Algorithms
2.5.5 Coordination between Distributed and Centralized SON
2.5.6 Network Security Concerns: New Interfaces and Lack of Monitoring

3 Chapter 3: SON Technology, Use Cases & Implementation Architectures
3.1 Where Does SON Sit Within a Mobile Network?
3.1.1 RAN
3.1.2 Mobile Core
3.1.3 Mobile Backhaul & Transport
3.1.4 Device-Assisted SON
3.2 SON Architecture
3.2.1 C-SON (Centralized SON)
3.2.2 D-SON (Distributed SON)
3.2.3 H-SON (Hybrid SON)
3.3 SON Use-Cases
3.3.1 Self-Configuration of Network Elements
3.3.2 Automatic Connectivity Management
3.3.3 Self-Testing of Network Elements
3.3.4 Self-Recovery of Network Elements/Software
3.3.5 Self-Healing of Board Faults
3.3.6 Automatic Inventory
3.3.7 ANR (Automatic Neighbor Relations)
3.3.8 PCI (Physical Cell ID) Configuration
3.3.9 CCO (Coverage & Capacity Optimization)
3.3.10 MRO (Mobility Robustness Optimization)
3.3.11 MLB (Mobile Load Balancing)
3.3.12 RACH (Random Access Channel) Optimization
3.3.13 ICIC (Inter-Cell Interference Coordination)
3.3.14 eICIC (Enhanced ICIC)
3.3.15 Energy Savings
3.3.16 Cell Outage Detection & Compensation
3.3.17 Self-Configuration & Optimization of Small Cells
3.3.18 Optimization of DAS (Distributed Antenna Systems)
3.3.19 RAN Aware Traffic Shaping
3.3.20 Traffic Steering in HetNets
3.3.21 Optimization of Virtualized Network Resources
3.3.22 Auto-Provisioning of Transport Links
3.3.23 Transport Network Bandwidth Optimization
3.3.24 Transport Network Interference Management
3.3.25 SON Coordination Management
3.3.26 Seamless Vendor Infrastructure Swap

4 Chapter 4: SON Standardization
4.1 NGNM (Next Generation Mobile Networks) Alliance
4.1.1 Conception of the SON Initiative
4.1.2 Functional Areas and Requirements
4.1.3 Implementation Approach
4.1.4 P-SmallCell (Project Small Cell)
4.1.5 Recommendations for Multi-Vendor SON Deployment
4.2 3GPP (Third Generation Partnership Project)
4.2.1 Release 8
4.2.2 Release 9
4.2.3 Release 10
4.2.4 Release 11
4.2.5 Release 12, 13 & Beyond
4.2.6 Implementation Approach

5 Chapter 5: SON Deployment Case Studies
5.1 AT&T Mobility
5.1.1 Vendor Selection & Contract Value
5.1.2 Implemented Use Cases
5.1.3 Results
5.2 Singtel
5.2.1 Vendor Selection & Contract Value
5.2.2 Implemented Use Cases
5.2.3 Results
5.3 TIM Brasil
5.3.1 Vendor Selection & Contract Value
5.3.2 Implemented Use Cases
5.3.3 Results
5.4 KDDI
5.4.1 Vendor Selection & Contract Value
5.4.2 Implemented Use Cases
5.4.3 Results
5.5 SK Telecom
5.5.1 Vendor Selection & Contract Value
5.5.2 Implemented Use Cases
5.5.3 Results
5.6 Globe Telecom
5.6.1 Vendor Selection & Contract Value
5.6.2 Implemented Use Cases
5.6.3 Results

6 Chapter 6: Industry Roadmap & Value Chain
6.1 Industry Roadmap
6.1.1 Large Scale Adoption of SON Technology: 2015 - 2020
6.1.2 Towards QoE/QoS Based End-to-End SON: 2020 - 2025
6.1.3 Continued Investments to Support 5G Rollouts: 2025 - 2030
6.2 Value Chain
6.3 Embedded Technology Ecosystem
6.3.1 Chipset Developers
6.3.2 Embedded Component/Software Providers
6.4 RAN Ecosystem
6.4.1 Macrocell RAN OEMs
6.4.2 Pure-Play and Specialist Small Cell OEMs
6.4.3 WiFi Access Point OEMs
6.4.4 DAS & Repeater Solution Providers
6.4.5 C-RAN Solution Providers
6.4.6 Other Technology & Network Component Providers/Enablers
6.5 Mobile Backhaul & Fronthaul Ecosystem
6.5.1 Backhaul & Fronthaul Solution Providers
6.6 Mobile Core Ecosystem
6.6.1 Core Network Infrastructure & Software Providers
6.7 Connectivity Ecosystem
6.7.1 2G, 3G & 4G Wireless Carriers
6.7.2 WiFi Connectivity Providers
6.7.3 SCaaS (Small Cells as a Service) Providers
6.8 SON & Mobile Network Optimization Ecosystem
6.8.1 SON Solution Providers
6.8.2 Mobile Network Optimization Solution Providers
6.9 SDN & NFV Ecosystem
6.9.1 SDN & NFV Providers

7 Chapter 7: Vendor Landscape
7.1 Accedian Networks
7.2 Accuver
7.3 AirHop Communications
7.4 Airspan Networks
7.5 Alcatel-Lucent
7.6 Amdocs
7.7 Anite
7.8 Arcadyan
7.9 Argela
7.10 Aricent
7.11 ARItel
7.12 Ascom
7.13 Astellia
7.14 ATDI
7.15 Avago Technologies
7.16 Avvasi
7.17 BLiNQ Networks
7.18 Cavium
7.19 CBNL (Cambridge Broadband Networks Limited)
7.20 CellMining
7.21 Cellwize
7.22 Celtro Communications
7.23 CENTRI
7.24 Cisco Systems
7.25 Citrix Systems
7.26 Comarch
7.27 CommAgility
7.28 Commsquare
7.29 Coriant
7.30 Datang Mobile
7.31 ECE (European Communications Engineering)
7.32 Ericsson
7.33 Flash Networks
7.34 Forsk
7.35 Fujitsu
7.36 Guavus
7.37 Hitachi
7.38 Huawei
7.39 InfoVista
7.40 Intel Corporation
7.41 InterDigital
7.42 ip.access
7.43 Lavastorm
7.44 Lemko Corporation
7.45 NEC Corporation
7.46 Nokia Networks
7.47 NXP Semiconductors
7.48 Optulink
7.49 P.I.Works
7.50 Plano Engineering
7.51 Qualcomm
7.52 RADCOM
7.53 Radisys Corporation
7.54 Reverb Networks
7.55 Rohde & Schwarz
7.56 Rorotika
7.57 Samsung Electronics
7.58 SEDICOM
7.59 Siklu Communication
7.60 SpiderCloud Wireless
7.61 Tarana Wireless
7.62 Tektronix Communications
7.63 TEOCO
7.64 Theta Networks
7.65 TI (Texas Instruments)
7.66 TTG International
7.67 Tulinx
7.68 Vasona Networks
7.69 Viavi Solutions
7.70 WebRadar
7.71 XCellAir
7.72 ZTE

8 Chapter 8: Market Analysis & Forecasts
8.1 SON & Mobile Network Optimization Revenue
8.2 SON Revenue
8.3 SON Revenue by Network Segment
8.3.1 SON in RAN
8.3.2 SON in Mobile Core
8.3.3 SON in Mobile Backhaul
8.4 SON Revenue by Architecture: Centralized vs. Distributed
8.4.1 C-SON
8.4.2 D-SON
8.5 SON Revenue by Wireless Network Generation: 2G/3G vs. 4G & Beyond
8.5.1 2G & 3G SON
8.5.2 4G & Beyond SON
8.6 SON Revenue by Region
8.7 Conventional Mobile Network Planning & Optimization Revenue
8.8 Conventional Mobile Network Planning & Optimization Revenue by Region
8.9 Asia Pacific
8.9.1 SON
8.9.2 Conventional Mobile Network Planning & Optimization
8.10 Eastern Europe
8.10.1 SON
8.10.2 Conventional Mobile Network Planning & Optimization
8.11 Latin & Central America
8.11.1 SON
8.11.2 Conventional Mobile Network Planning & Optimization
8.12 Middle East & Africa
8.12.1 SON
8.12.2 Conventional Mobile Network Planning & Optimization
8.13 North America
8.13.1 SON
8.13.2 Conventional Mobile Network Planning & Optimization
8.14 Western Europe
8.14.1 SON
8.14.2 Conventional Mobile Network Planning & Optimization
8.15 Top Country Markets
8.15.1 Australia
8.15.2 Brazil
8.15.3 Canada
8.15.4 China
8.15.5 France
8.15.6 Germany
8.15.7 India
8.15.8 Italy
8.15.9 Japan
8.15.10 Russia
8.15.11 South Korea
8.15.12 Spain
8.15.13 Taiwan
8.15.14 UK
8.15.15 USA

9 Chapter 9: Conclusion & Strategic Recommendations
9.1 Moving Towards QoE Based SON Platforms
9.2 Capitalizing on DPI (Deep Packet Inspection)
9.3 The Convergence of Big Data, Predictive Analytics & SON
9.4 Optimizing M2M & IoT Services
9.5 SON for NFV & SDN: The Push from Mobile Operators
9.6 Moving Towards Mobile Core and Transport Networks
9.7 Assessing the Impact of SON on Optimization & Field Engineers
9.8 SON Associated OpEx Savings: The Numbers
9.9 What SON Capabilities Will 5G Networks Entail?
9.10 The C-SON Versus D-SON Debate
9.11 Strategic Recommendations
9.11.1 SON & Conventional Mobile Network Optimization Solution Providers
9.11.2 Wireless Infrastructure OEMs
9.11.3 Mobile Operators
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