DUBLIN, Sept. 21, 2018 /PRNewswire/ —

The “SON (Self-Organizing Networks) in the 5G Era: 2019 – 2030 – Opportunities, Challenges, Strategies & Forecasts” report from SNS Telecom & IT has been added to ResearchAndMarkets.com’s offering.

By the end of 2022, the research estimates that SON will account for a market worth $5.5 Billion.

The SON (Self-Organizing Networks) in the 5G Era: 2019 – 2030 – Opportunities, Challenges, Strategies & Forecasts report presents an in-depth assessment of the SON and associated mobile network optimization ecosystem, including market drivers, challenges, enabling technologies, functional areas, use cases, key trends, standardization, regulatory landscape, mobile operator case studies, opportunities, future roadmap, value chain, ecosystem player profiles and strategies. The report also presents revenue forecasts for both SON and conventional mobile network optimization, along with individual projections for 10 SON submarkets, and 6 regions from 2019 till 2030.

SON (Self-Organizing Network) technology minimizes the lifecycle cost of running a mobile network by eliminating manual configuration of network elements at the time of deployment, right through to dynamic optimization and troubleshooting during operation. Besides improving network performance and customer experience, SON can significantly reduce the cost of mobile operator services, improving the OpEx-to-revenue ratio and deferring avoidable CapEx.

To support their LTE and HetNet deployments, early adopters of SON have already witnessed a spate of benefits – in the form of accelerated rollout times, simplified network upgrades, fewer dropped calls, improved call setup success rates, higher end-user throughput, alleviation of congestion during special events, increased subscriber satisfaction, and loyalty, and operational efficiencies – such as energy and cost savings, and freeing up radio engineers from repetitive manual tasks.

Although SON was originally developed as an operational approach to streamline cellular RAN (Radio Access Network) deployment and optimization, mobile operators and vendors are increasingly focusing on integrating new capabilities such as self-protection against digital security threats, and self-learning through artificial intelligence techniques, as well as extending the scope of SON beyond the RAN to include both mobile core and transport network segments – which will be critical to address 5G requirements such as end-to-end network slicing. In addition, dedicated SON solutions for Wi-Fi and other access technologies have also emerged, to simplify wireless networking in home and enterprise environments.

Largely driven by the increasing complexity of today’s multi-RAN mobile networks – including network densification and spectrum heterogeneity, as well as 5G NR (New Radio) infrastructure rollouts, global investments in SON technology are expected to grow at a CAGR of approximately 11% between 2019 and 2022.

Key Findings

• Largely driven by the increasing complexity of today’s multi-RAN mobile networks – including network densification and spectrum heterogeneity, as well as 5G NR (New Radio) infrastructure rollouts, global investments in SON technology are expected to grow at a CAGR of approximately 11% between 2019 and 2022. By the end of 2022, the research estimates that SON will account for a market worth $5.5 Billion.
• Based on feedback from mobile operators worldwide, the growing adoption of SON technology has brought about a host of practical benefits for early adopters – ranging from more than a 50% decline in dropped calls and reduction in network congestion during special events by a staggering 80% to OpEx savings of more than 30% and an increase in service revenue by 5-10%.
• In addition, SON mechanisms are playing a pivotal role in accelerating the adoption of 5G networks – through the enablement of advanced capabilities such as network slicing, dynamic spectrum management, predictive resource allocation, and the automated of deployment of virtualized 5G network functions.
• To better address network performance challenges amidst increasing complexity, C-SON platforms are leveraging an array of complementary technologies – from artificial intelligence and machine learning algorithms to Big Data technologies and the use of alternative data such as information extracted from crowd-sourcing tools.
• In addition to infrastructure vendor and third-party offerings, mobile operator developed SON solutions are also beginning to emerge. For example, Elisa has developed a SON platform based on closed-loop automation and customizable algorithms for dynamic network optimization. Through a dedicated business unit, the Finnish operator offers its in-house SON implementation as a commercial product to other mobile operators.

Key Topics Covered:

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

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
2.3.4 Self-Protection
2.3.5 Self-Learning
2.4 Market Drivers for SON Adoption
2.4.1 The 5G Era: Continued Mobile Network Infrastructure Investments
2.4.2 Optimization in Multi-RAN & HetNet Environments
2.4.3 OpEx & CapEx Reduction: The Cost Savings Potential
2.4.4 Improving Subscriber Experience and Churn Reduction
2.4.5 Power Savings: Towards Green Mobile Networks
2.4.6 Alleviating Congestion with Traffic Management
2.4.7 Enabling Large-Scale Small Cell Rollouts
2.4.8 Growing Adoption of Private LTE & 5G-Ready Networks
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 Proprietary SON Algorithms
2.5.5 Coordination Between Distributed and Centralized SON
2.5.6 Network Security Concerns: New Interfaces and Lack of Monitoring

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 Transport (Backhaul & Fronthaul)
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 (Mobility 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 COD/COC (Cell Outage Detection & Compensation)
3.3.17 MDT (Minimization of Drive Tests)
3.3.18 AAS (Adaptive Antenna Systems) & Massive MIMO
3.3.19 Millimeter Wave Links in 5G NR (New Radio) Networks
3.3.20 Self-Configuration & Optimization of Small Cells
3.3.21 Optimization of DAS (Distributed Antenna Systems)
3.3.22 RAN Aware Traffic Shaping
3.3.23 Traffic Steering in HetNets
3.3.24 Optimization of NFV-Based Networking
3.3.25 Auto-Provisioning of Transport Links
3.3.26 Transport Network Bandwidth Optimization
3.3.27 Transport Network Interference Management
3.3.28 Self-Protection
3.3.29 SON Coordination Management
3.3.30 Seamless Vendor Infrastructure Swap
3.3.31 Dynamic Spectrum Management & Allocation
3.3.32 Network Slice Optimization
3.3.33 Cognitive & Self-Learning Networks

Chapter 4: Key Trends in Next-Generation LTE & 5G SON Implementations
4.1 Big Data & Advanced Analytics
4.1.1 Maximizing the Benefits of SON with Big Data
4.1.2 The Importance of Predictive & Behavioral Analytics
4.2 Artificial Intelligence & Machine Learning
4.2.1 Towards Self-Learning SON Engines with Machine Learning
4.2.2 Deep Learning: Enabling “Zero-Touch” Mobile Networks
4.3 NFV (Network Functions Virtualization)
4.3.1 Enabling the SON-Driven Deployment of VNFs (Virtualized Network Functions)
4.4 SDN (Software Defined Networking) & Programmability
4.4.1 Using the SDN Controller as a Platform for SON in Transport Networks
4.5 Cloud Computing
4.5.1 Facilitating C-SON Scalability & Elasticity
4.6 Small Cells, HetNets & RAN Densification
4.6.1 Plug & Play Small Cells
4.6.2 Coordinating UDNs (Ultra Dense Networks) with SON
4.7 C-RAN (Centralized RAN) & Cloud RAN
4.7.1 Efficient Resource Utilization in C-RAN Deployments with SON
4.8 Unlicensed & Shared Spectrum Usage
4.8.1 Dynamic Management of Spectrum with SON
4.9 MEC (Multi-Access Edge Computing)
4.9.1 Potential Synergies with SON
4.10 Network Slicing
4.10.1 Use of SON Mechanisms for Network Slicing in 5G Networks
4.11 Other Trends & Complementary Technologies
4.11.1 Alternative Carrier/Private LTE & 5G-Ready Networks
4.11.2 FWA (Fixed Wireless Access)
4.11.3 DPI (Deep Packet Inspection)
4.11.4 Digital Security for Self-Protection
4.11.5 SON Capabilities for IoT Applications
4.11.6 User-Based Profiling & Optimization for Vertical 5G Applications
4.11.7 Addressing D2D (Device-to-Device) Communications & New Use Cases

Chapter 5: Standardization, Regulatory & Collaborative Initiatives
5.1 3GPP (Third Generation Partnership Project)
5.1.1 Standardization of SON Capabilities for 3GPP Networks
5.1.2 Release 8
5.1.3 Release 9
5.1.4 Release 10
5.1.5 Release 11
5.1.6 Release 12
5.1.7 Releases 13 & 14
5.1.8 Releases 15, 16 & Beyond
5.1.9 Implementation Approach for 3GPP-Specified SON Features
5.2 NGMN Alliance
5.2.1 Conception of the SON Initiative
5.2.2 Functional Areas and Requirements
5.2.3 Implementation Approach: Focus on H-SON
5.2.4 Recommendations for Multi-Vendor SON Deployment
5.2.5 SON Capabilities for 5G Network Deployment, Operation & Management
5.3 ETSI (European Telecommunications Standards Institute)
5.3.1 ENI ISG (Experiential Networked Intelligence Industry Specification Group)
5.4 Linux Foundation’s ONAP (Open Network Automation Platform)
5.4.1 ONAP Support for SON in 5G Networks
5.5 OSSii (Operations Support Systems Interoperability Initiative)
5.5.1 Enabling Multi-Vendor SON Interoperability
5.6 Small Cell Forum
5.6.1 Release 7: Focus on SON for Small Cells
5.6.2 SON API
5.6.3 X2 Interoperability
5.7 WBA (Wireless Broadband Alliance)
5.7.1 SON Integration in Carrier Wi-Fi Guidelines
5.8 CableLabs
5.8.1 Wi-Fi RRM (Radio Resource Management)/SON
5.9 5G PPP (5G Infrastructure Public Private Partnership) & European Union Projects
5.9.1 SELFNET (Framework for Self-Organized Network Management in Virtualized and Software Defined Networks)
5.9.2 SEMAFOUR (Self-Management for Unified Heterogeneous Radio Access Networks)
5.9.3 SOCRATES (Self-Optimization and Self-Configuration in Wireless Networks)
5.9.4 COGNET (Building an Intelligent System of Insights and Action for 5G Network Management)

Chapter 6: SON Deployment Case Studies
6.1 AT&T
6.2 BCE (Bell Canada)
6.3 Bharti Airtel
6.4 Elisa
6.5 Globe Telecom
6.6 KDDI Corporation
6.7 MegaFon
6.8 Orange
6.9 Singtel
6.10 SK Telecom
6.11 Telefnica Group
6.12 TIM (Telecom Italia Mobile)
6.13 Turkcell
6.14 Verizon Communications
6.15 Vodafone Group

Chapter 7: Future Roadmap & Value Chain
7.1 Future Roadmap
7.1.1 Pre-2020: Addressing Customer QoE, Network Densification & Early 5G Rollouts
7.1.2 2020 – 2025: Towards Advanced Machine Learning Based SON Implementations
7.1.3 2025 – 2030: Enabling Near Zero-Touch & Automated 5G Networks
7.2 Value Chain
7.3 Embedded Technology Ecosystem
7.3.1 Chipset Developers
7.3.2 Embedded Component/Software Providers
7.4 RAN Ecosystem
7.4.1 Macrocell RAN OEMs
7.4.2 Pure-Play Small Cell OEMs
7.4.3 Wi-Fi Access Point OEMs
7.4.4 DAS & Repeater Solution Providers
7.4.5 C-RAN Solution Providers
7.4.6 Other Technology Providers
7.5 Transport Networking Ecosystem
7.5.1 Backhaul & Fronthaul Solution Providers
7.6 Mobile Core Ecosystem
7.6.1 Mobile Core Solution Providers
7.7 Connectivity Ecosystem
7.7.1 Mobile Operators
7.7.2 Wi-Fi Connectivity Providers
7.7.3 SCaaS (Small-Cells-as-a-Service) Providers
7.8 SON Ecosystem
7.8.1 SON Solution Providers
7.9 SDN & NFV Ecosystem
7.9.1 SDN & NFV Providers
7.10 MEC Ecosystem
7.10.1 MEC Specialists

Chapter 8: Key Ecosystem Players
8.1 Accedian Networks
8.2 Accelleran
8.3 AirHop Communications
8.4 Airspan Networks
8.5 Allot Communications
8.6 Alpha Networks
8.7 Altiostar Networks
8.8 Altran/Aricent
8.9 Alvarion Technologies/SuperCom
8.10 Amdocs
8.11 Anritsu Corporation
8.12 Arcadyan Technology Corporation
8.13 Argela/Netsia
8.14 Artemis Networks
8.15 Artiza Networks
8.16 ASOCS
8.17 ASUS (ASUSTeK Computer)
8.18 ATDI
8.19 Baicells Technologies
8.20 Benu Networks
8.21 BoostEdge
8.22 Broadcom
8.23 Casa Systems
8.24 CBNL (Cambridge Broadband Networks Limited)
8.25 CCI (Communication Components, Inc.)/BLiNQ Networks
8.26 CCS (Cambridge Communication Systems)
8.27 CellOnyx
8.28 Cellwize
8.29 CelPlan Technologies
8.30 Celtro
8.31 Cisco Systems
8.32 Citrix Systems
8.33 Collision Communications
8.34 Comarch
8.35 CommAgility
8.36 CommScope
8.37 CommProve
8.38 Contela
8.39 Continual
8.40 Coriant
8.41 Corning/SpiderCloud Wireless
8.42 Datang Mobile
8.43 Dell Technologies
8.44 Digitata
8.45 D-Link Corporation
8.46 ECE (European Communications Engineering)
8.47 EDX Wireless
8.48 Elisa Automate
8.49 Empirix
8.50 Equiendo
8.51 Ercom
8.52 Ericsson
8.53 ETRI (Electronics & Telecommunications Research Institute, South Korea)
8.54 EXFO/Astellia
8.55 Facebook
8.56 Fairspectrum
8.57 Federated Wireless
8.58 Flash Networks
8.59 Forsk
8.60 Fujian Sunnada Network Technology
8.61 Fujitsu
8.62 Galgus
8.63 Gemtek Technology
8.64 General Dynamics Mission Systems
8.65 GenXComm
8.66 GoNet Systems
8.67 Google/Alphabet
8.68 Guavus/Thales
8.69 GWT (Global Wireless Technologies)
8.70 HCL Technologies
8.71 Hitachi
8.72 Huawei
8.73 iBwave Solutions
8.74 InfoVista
8.75 Innovile
8.76 InnoWireless/Qucell/Accuver
8.77 Intel Corporation
8.78 InterDigital
8.79 Intracom Telecom
8.80 ip.access
8.81 ITRI (Industrial Technology Research Institute, Taiwan)
8.82 JRC (Japan Radio Company)
8.83 Juni Global
8.84 Juniper Networks
8.85 Keima
8.86 Key Bridge
8.87 Keysight Technologies/Ixia
8.88 Kleos
8.89 Koonsys Radiocommunications
8.90 Kumu Networks
8.91 Lemko Corporation
8.92 Linksys
8.93 LS telcom
8.94 Luminate Wireless
8.95 LuxCarta
8.96 Marvell Technology Group/Cavium
8.97 Mavenir Systems
8.98 Mimosa Networks
8.99 MitraStar Technology Corporation
8.100 Mojo Networks/Arista Networks
8.101 Mosaik
8.102 Nash Technologies
8.103 NEC Corporation
8.104 NetScout Systems
8.105 New Postcom Equipment Company
8.106 Node-H
8.107 Nokia Networks
8.108 Nomor Research
8.109 NuRAN Wireless/Nutaq Innovation
8.110 NXP Semiconductors
8.111 Oceus Networks
8.112 P.I.Works
8.113 Parallel Wireless
8.114 Persistent Systems
8.115 PHAZR
8.116 Phluido
8.117 Polystar
8.118 Potevio
8.119 Qualcomm
8.120 Quanta Computer
8.121 RADCOM
8.122 Radisys Corporation/Reliance Industries
8.123 Ranplan Wireless Network Design
8.124 RED Technologies
8.125 Redline Communications
8.126 Rivada Networks
8.127 Rohde & Schwarz
8.128 Ruckus Wireless/ARRIS International
8.129 Saguna Networks
8.130 Samji Electronics Company
8.131 Samsung
8.132 SEDICOM
8.133 SerComm Corporation
8.134 Seven Networks
8.135 Siklu Communication
8.136 SIRADEL
8.137 SITRONICS
8.138 SK Telesys
8.139 Spectrum Effect
8.140 Star Solutions
8.141 Systemics Group
8.142 Tarana Wireless
8.143 Tech Mahindra
8.144 Tecore Networks
8.145 TEKTELIC Communications
8.146 Telrad Networks
8.147 TEOCO Corporation
8.148 Teragence
8.149 TI (Texas Instruments)
8.150 TP-Link Technologies
8.151 TTG International
8.152 Tulinx
8.153 Vasona Networks
8.154 Viavi Solutions
8.155 VMWare
8.156 WebRadar
8.157 Wireless DNA
8.158 WNC (Wistron NeWeb Corporation)
8.159 WPOTECH
8.160 XCellAir/Fontech
8.161 Z-Com
8.162 ZTE
8.163 Zyxel Communications Corporation

Chapter 9: Market Sizing & Forecasts
9.1 SON & Mobile Network Optimization Revenue
9.2 SON Revenue
9.3 SON Revenue by Network Segment
9.3.1 RAN
9.3.2 Mobile Core
9.3.3 Transport (Backhaul & Fronthaul)
9.4 SON Revenue by Architecture: Centralized vs. Distributed
9.4.1 C-SON
9.4.2 D-SON
9.5 SON Revenue by Access Network Technology
9.5.1 2G & 3G
9.5.2 LTE
9.5.3 5G
9.5.4 Wi-Fi
9.6 SON Revenue by Region
9.7 Conventional Mobile Network Planning & Optimization Revenue
9.8 Conventional Mobile Network Planning & Optimization Revenue by Region

Chapter 10: Conclusion & Strategic Recommendations
10.1 Why is the Market Poised to Grow?
10.2 Competitive Industry Landscape: Acquisitions, Alliances & Consolidation
10.3 Evaluating the Practical Benefits of SON
10.4 End-to-End SON: Moving Towards Mobile Core and Transport Networks
10.5 Growing Adoption of SON Capabilities for Wi-Fi
10.6 The Importance of Artificial Intelligence & Machine Learning
10.7 QoE-Based SON Platforms: Optimizing End User Experience
10.8 Enabling Network Slicing & Advanced Capabilities for 5G Networks
10.9 Greater Focus on Self-Protection Capabilities
10.10 Addressing IoT Optimization
10.11 Managing Unlicensed & Shared Spectrum
10.12 Easing the Deployment of Private & Enterprise LTE/5G-Ready Networks
10.13 Assessing the Impact of SON on Optimization & Field Engineers
10.14 SON Associated OpEx Savings: The Numbers
10.15 The C-SON Versus D-SON Debate
10.16 Strategic Recommendations
10.16.1 SON Solution Providers
10.16.2 Mobile Operators

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