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

Published: Mar, 2014 | Pages: 186 | Publisher: SNS Research
Industry: Telecommunications | Report Format: Electronic (PDF)

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.

 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 Self-Organizing Network (SON) 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
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 Backhaul Links
3.3.23 Backhaul Bandwidth Optimization
3.3.24 Backhaul 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

6 Chapter 6: Industry Roadmap & Value Chain
6.1 Industry Roadmap
6.1.1 Initial LTE FDD Rollouts with D-SON: 2010 - 2011
6.1.2 Rise of the HetNets & C-SON: 2012 - 2013
6.1.3 TD-LTE Deployments & Continued SON Proliferation: 2014 - 2016
6.1.4 “Software Centric” Networking & QoE/QoS Based SON: 2017 - 2019
6.1.5 Start of the 5G Era: 2020 & Beyond
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 Cloud RAN Solution Providers
6.4.6 Other Technology & Network Component Providers/Enablers
6.5 Mobile Backhaul Ecosystem
6.5.1 Backhaul 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 Small Cells as a Service (SCaaS) 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 AIRCOM International (Acquired by TEOCO)
7.4 AirHop Communications
7.5 Airspan Networks
7.6 Alcatel-Lucent
7.7 Amdocs
7.8 Arcadyan
7.9 Argela
7.10 Aricent
7.11 ARItel
7.12 Ascom
7.13 Astellia
7.14 ATDI
7.15 Avvasi
7.16 Broadcom
7.17 BLiNQ Networks
7.18 Cavium
7.19 CBNL (Cambridge Broadband Networks Limited)
7.20 Cellwize
7.21 Celtro
7.22 CENTRI
7.23 Cisco Systems
7.24 Citrix
7.25 Comarch
7.26 Commsquare
7.27 DTM (Datang Mobile)
7.28 ECE (European Communications Engineering)
7.29 Eden Rock Communications
7.30 Ericsson
7.31 Forsk
7.32 Freescale Semiconductor
7.33 Fujitsu
7.34 Guavus
7.35 Hitachi
7.36 Huawei
7.37 Intel
7.38 InterDigital
7.39 InfoVista
7.40 JDSU
7.41 Lemko
7.42 Lavastorm
7.43 mimoOn
7.44 NEC
7.45 NSN (Nokia Solutions & Networks)
7.46 Optulink
7.47 P.I.Works
7.48 Plano Engineering
7.49 Qualcomm
7.50 Radisys
7.51 RADCOM
7.52 Reverb Networks
7.53 Rohde & Schwarz
7.54 Rorotika
7.55 Samsung
7.56 SEDICOM
7.57 Siklu
7.58 SpiderCloud Wireless
7.59 Tarana Wireless
7.60 Tektronix Communications
7.61 Tellabs
7.62 TEOCO
7.63 Texas Instruments
7.64 Theta Networks
7.65 TTG International
7.66 Tulinx
7.67 WebRadar
7.68 Xceed Technologies
7.69 ZTE

8 Chapter 8: Market Analysis & Forecasts
8.1 SON & Mobile Network Optimization Revenue
8.2 SON Revenue
8.3 SON Revenue by Submarket
8.3.1 SON in Macrocell RAN
8.3.2 SON in HetNet/Small Cell RAN
8.3.3 SON in Mobile Core
8.3.4 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
8.5.1 2G/3G SON
8.5.2 4G 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

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 Analytics & SON
9.4 SON for NFV & SDN: The Push from Wireless Carriers
9.5 Moving Towards Mobile Core and Backhaul
9.6 Assessing the Impact of SON on Optimization & Field Engineers
9.7 SON Associated OpEx Savings: The Numbers
9.8 What SON Capabilities Will 5G Networks Entail?
9.9 The C-SON Versus D-SON Debate
9.10 Strategic Recommendations
9.10.1 SON & Conventional Mobile Network Optimization Solution Providers
9.10.2 Wireless Infrastructure OEMs
9.10.3 Wireless Carriers