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Exclusive Forecast Study: SON (Self-Organizing Networks) In The 5G Era: 2019 – 2030 – Opportunities, Challenges, Strategies & Forecasts

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. By the end of 2022, SNS Telecom & IT estimates that SON will account for a market worth $5.5 Billion.

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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.

The report comes with an associated Excel datasheet suite covering quantitative data from all numeric forecasts presented in the report.

Topics Covered

The report covers the following topics:

  •  SON ecosystem
  •  Market drivers and barriers
  •  Conventional mobile network planning & optimization
  •  Mobile network infrastructure spending, traffic projections and value chain
  •  SON technology, architecture & functional areas
  •  Review of over 30 SON use cases – ranging from automated neighbor relations and parameter optimization to self-protection and cognitive networks
  •  Case studies of 15 commercial SON deployments by mobile operators
  •  Complementary technologies including Big Data, advanced analytics, artificial intelligence and machine learning
  •  Key trends in next-generation LTE and 5G SON implementations including network slicing, dynamic spectrum management, edge computing, virtualization and zero-touch automation
  •  Regulatory landscape, collaborative initiatives and standardization
  •  SON future roadmap: 2019 – 2030
  •  Profiles and strategies of more than 160 leading ecosystem players including wireless network infrastructure OEMs, SON solution providers and mobile operators
  •  Strategic recommendations for SON solution providers and mobile operators
  •  Market analysis and forecasts from 2019 till 2030

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Forecast Segmentation

Market forecasts are provided for each of the following submarkets and their subcategories:

Mobile Network Optimization

  •  SON
  •  Conventional Mobile Network Planning & Optimization

SON Network Segment Submarkets

  •  RAN (Radio Access Network)
  •  Mobile Core
  •  Transport (Backhaul & Fronthaul)

SON Architecture Submarkets

  •  C-SON (Centralized SON)
  •  D-SON (Distributed SON)
  •  SON Access Network Technology Submarkets
  •  2G & 3G
  •  LTE
  •  5G
  •  Wi-Fi & Others

Regional Markets

  •  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 opportunity?
  •  What trends, challenges and barriers are influencing its growth?
  •  How is the ecosystem evolving by segment and region?
  •  What will the market size be in 2022, and at what rate will it grow?
  •  Which regions and countries will see the highest percentage of growth?
  •  How do SON investments compare with spending on traditional mobile network optimization?
  •  What are the practical, quantifiable benefits of SON – based on live, commercial deployments?
  •  How can mobile operators capitalize on SON to ensure optimal network performance, improve customer experience, reduce costs, and drive revenue growth?
  •  What is the status of C-SON and D-SON adoption worldwide?
  •  What are the prospects of artificial intelligence in SON and mobile network automation?
  •  What opportunities exist for SON in mobile core and transport networks?
  •  How can SON ease the deployment of unlicensed and private LTE/5G-ready networks?
  •  What SON capabilities will 5G networks entail?
  •  How does SON impact mobile network optimization engineers?
  •  What is the global and regional outlook for SON associated OpEx savings?
  •  Who are the key ecosystem players, and what are their strategies?
  •  What strategies should SON solution providers and mobile operators adopt to remain competitive?

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Key Findings

The report has the following 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, SNS Telecom & IT 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.

List of Companies Mentioned

  • 3GPP (Third Generation Partnership Project)
  • 5G PPP (5G Infrastructure Public Private Partnership)
  • Accedian Networks
  • Accelleran
  • Accuver
  • Actix
  • AIRCOM International
  • AirHop Communications
  • Airspan Networks
  • Allot Communications
  • Alpha Networks
  • Alphabet
  • Altiostar Networks
  • Altran
  • Alvarion Technologies
  • Amdocs
  • Anritsu Corporation
  • Arcadyan Technology Corporation
  • Argela
  • ARIB (Association of Radio Industries and Businesses, Japan)
  • Aricent
  • Arista Networks
  • ARRIS International
  • Artemis Networks
  • Artiza Networks
  • ASOCS
  • Astellia
  • ASUS (ASUSTeK Computer)
  • AT&T
  • ATDI
  • ATIS (Alliance for Telecommunications Industry Solutions, United States)
  • Baicells Technologies
  • BCE (Bell Canada)
  • Benu Networks
  • Bharti Airtel
  • BLiNQ Networks
  • BoostEdge
  • Broadcom
  • CableLabs
  • Casa Systems
  • Cavium
  • CBNL (Cambridge Broadband Networks Limited)
  • CCI (Communication Components, Inc.)
  • CCS (Cambridge Communication Systems)
  • CCSA (China Communications Standards Association)
  • Celcite
  • CellOnyx
  • Cellwize
  • CelPlan Technologies
  • Celtro
  • Cisco Systems
  • Citrix Systems
  • Collision Communications
  • Comarch
  • CommAgility
  • CommProve
  • CommScope
  • Commsquare
  • Comsearch
  • Contela
  • Continual
  • Coriant
  • Corning
  • Datang Mobile
  • Dell Technologies
  • Digi Communications
  • Digitata
  • D-Link Corporation
  • ECE (European Communications Engineering)
  • EDX Wireless
  • Elisa
  • Elisa Automate
  • Empirix
  • Equiendo
  • Ercom
  • Ericsson
  • ETRI (Electronics & Telecommunications Research Institute, South Korea)
  • ETSI (European Telecommunications Standards Institute)
  • EXFO
  • Facebook
  • Fairspectrum
  • Federated Wireless
  • Flash Networks
  • Fon
  • Fontech
  • Forsk
  • Fujian Sunnada Network Technology
  • Fujitsu
  • Galgus
  • Gemtek Technology
  • General Dynamics Mission Systems
  • GenXComm
  • Globe Telecom
  • GoNet Systems
  • Google
  • Guavus
  • GWT (Global Wireless Technologies)
  • HCL Technologies
  • Hitachi
  • Hitachi Vantara
  • Huawei
  • iBwave Solutions
  • InfoVista
  • Innovile
  • InnoWireless
  • Intel Corporation
  • InterDigital
  • Intracom Telecom
  • ip.access
  • ITRI (Industrial Technology Research Institute, Taiwan)
  • Ixia
  • JRC (Japan Radio Company)
  • Juni Global
  • Juniper Networks
  • KDDI Corporation
  • Keima
  • Key Bridge
  • Keysight Technologies
  • KKTCell (Kuzey K?br?s Turkcell)
  • Kleos
  • Koonsys Radiocommunications
  • Kumu Networks
  • Lemko Corporation
  • life:) Belarus
  • lifecell Ukraine
  • Linksys
  • Linux Foundation
  • LS telcom
  • Luminate Wireless
  • LuxCarta
  • Marvell Technology Group
  • Mavenir Systems
  • MegaFon
  • Mimosa Networks
  • MitraStar Technology Corporation
  • Mojo Networks
  • Mosaik
  • Nash Technologies
  • NEC Corporation
  • NetQPro
  • NetScout Systems
  • Netsia
  • New Postcom Equipment Company
  • Nexus Telecom
  • NGMN Alliance
  • Node-H
  • Nokia Networks
  • Nomor Research
  • NuRAN Wireless
  • Nutaq Innovation
  • NXP Semiconductors
  • Oceus Networks
  • Optus
  • Orange
  • P.I.Works
  • Parallel Wireless
  • Persistent Systems
  • PHAZR
  • Phluido
  • Polystar
  • Potevio
  • PreClarity
  • Qualcomm
  • Quanta Computer
  • Qucell
  • RADCOM
  • Radisys Corporation
  • Ranplan Wireless Network Design
  • RCS & RDS
  • Rearden
  • Red Hat
  • RED Technologies
  • Redline Communications
  • Reliance Industries
  • Rivada Networks
  • Rohde & Schwarz
  • Ruckus Wireless
  • Saguna Networks
  • Samji Electronics Company
  • Samsung
  • Schema
  • SEDICOM
  • SerComm Corporation
  • Seven Networks
  • Siklu Communication
  • Singtel
  • SIRADEL
  • SITRONICS
  • SK Telecom
  • SK Telesys
  • Small Cell Forum
  • Spectrum Effect
  • SpiderCloud Wireless
  • Star Solutions
  • SuperCom
  • Systemics Group
  • Tarana Wireless
  • Tech Mahindra
  • Tecore Networks
  • TEKTELIC Communications
  • Telefónica Group
  • Telrad Networks
  • TEOCO Corporation
  • Teragence
  • Thales
  • TI (Texas Instruments)
  • TIM (Telecom Italia Mobile)
  • TIM Brasil
  • TP-Link Technologies
  • TSDSI (Telecommunications Standards Development Society, India)
  • TTA (Telecommunications Technology Association, South Korea)
  • TTC (Telecommunication Technology Committee, Japan)
  • TTG International
  • Tulinx
  • Turkcell
  • Vasona Networks
  • Verizon Communications
  • VHA (Vodafone Hutchison Australia)
  • Viavi Solutions
  • VMWare
  • Vodafone Germany
  • Vodafone Group
  • Vodafone Ireland
  • Vodafone Spain
  • Vodafone UK
  • WBA (Wireless Broadband Alliance)
  • WebRadar
  • Wireless DNA
  • WNC (Wistron NeWeb Corporation)
  • WPOTECH
  • XCellAir
  • Z-Com
  • ZTE
  • Zyxel Communications Corporation

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Table of Contents

1 Chapter 1: Introduction 19
1.1 Executive Summary 19
1.2 Topics Covered 21
1.3 Forecast Segmentation 22
1.4 Key Questions Answered 23
1.5 Key Findings 24
1.6 Methodology 26
1.7 Target Audience 27
1.8 Companies & Organizations Mentioned 28

2 Chapter 2: SON & Mobile Network Optimization Ecosystem 31
2.1 Conventional Mobile Network Optimization 31
2.1.1 Network Planning 31
2.1.2 Measurement Collection: Drive Tests, Probes and End User Data 32
2.1.3 Post-Processing, Optimization & Policy Enforcement 32
2.2 The SON (Self-Organizing Network) Concept 33
2.2.1 What is SON? 33
2.2.2 The Need for SON 33
2.3 Functional Areas of SON 34
2.3.1 Self-Configuration 35
2.3.2 Self-Optimization 35
2.3.3 Self-Healing 35
2.3.4 Self-Protection 36
2.3.5 Self-Learning 36
2.4 Market Drivers for SON Adoption 37
2.4.1 The 5G Era: Continued Mobile Network Infrastructure Investments 37
2.4.2 Optimization in Multi-RAN & HetNet Environments 39
2.4.3 OpEx & CapEx Reduction: The Cost Savings Potential 39
2.4.4 Improving Subscriber Experience and Churn Reduction 40
2.4.5 Power Savings: Towards Green Mobile Networks 40
2.4.6 Alleviating Congestion with Traffic Management 41
2.4.7 Enabling Large-Scale Small Cell Rollouts 41
2.4.8 Growing Adoption of Private LTE & 5G-Ready Networks 41
2.5 Market Barriers for SON Adoption 42
2.5.1 Complexity of Implementation 42
2.5.2 Reorganization & Changes to Standard Engineering Procedures 42
2.5.3 Lack of Trust in Automation 42
2.5.4 Proprietary SON Algorithms 42
2.5.5 Coordination Between Distributed and Centralized SON 43
2.5.6 Network Security Concerns: New Interfaces and Lack of Monitoring 43

3 Chapter 3: SON Technology, Use Cases & Implementation Architectures 44
3.1 Where Does SON Sit Within a Mobile Network? 44
3.1.1 RAN 45
3.1.2 Mobile Core 45
3.1.3 Transport (Backhaul & Fronthaul) 46
3.1.4 Device-Assisted SON 47
3.2 SON Architecture 48
3.2.1 C-SON (Centralized SON) 48
3.2.2 D-SON (Distributed SON) 49
3.2.3 H-SON (Hybrid SON) 50
3.3 SON Use-Cases 51
3.3.1 Self-Configuration of Network Elements 51
3.3.2 Automatic Connectivity Management 51
3.3.3 Self-Testing of Network Elements 51
3.3.4 Self-Recovery of Network Elements/Software 51
3.3.5 Self-Healing of Board Faults 52
3.3.6 Automatic Inventory 52
3.3.7 ANR (Automatic Neighbor Relations) 52
3.3.8 PCI (Physical Cell ID) Configuration 52
3.3.9 CCO (Coverage & Capacity Optimization) 53
3.3.10 MRO (Mobility Robustness Optimization) 53
3.3.11 MLB (Mobility Load Balancing) 53
3.3.12 RACH (Random Access Channel) Optimization 54
3.3.13 ICIC (Inter-Cell Interference Coordination) 54
3.3.14 eICIC (Enhanced ICIC) 55
3.3.15 Energy Savings 55
3.3.16 COD/COC (Cell Outage Detection & Compensation) 55
3.3.17 MDT (Minimization of Drive Tests) 56
3.3.18 AAS (Adaptive Antenna Systems) & Massive MIMO 56
3.3.19 Millimeter Wave Links in 5G NR (New Radio) Networks 56
3.3.20 Self-Configuration & Optimization of Small Cells 56
3.3.21 Optimization of DAS (Distributed Antenna Systems) 57
3.3.22 RAN Aware Traffic Shaping 57
3.3.23 Traffic Steering in HetNets 57
3.3.24 Optimization of NFV-Based Networking 57
3.3.25 Auto-Provisioning of Transport Links 58
3.3.26 Transport Network Bandwidth Optimization 58
3.3.27 Transport Network Interference Management 58
3.3.28 Self-Protection 59
3.3.29 SON Coordination Management 59
3.3.30 Seamless Vendor Infrastructure Swap 59
3.3.31 Dynamic Spectrum Management & Allocation 59
3.3.32 Network Slice Optimization 59
3.3.33 Cognitive & Self-Learning Networks 60

4 Chapter 4: Key Trends in Next-Generation LTE & 5G SON Implementations 61
4.1 Big Data & Advanced Analytics 61
4.1.1 Maximizing the Benefits of SON with Big Data 61
4.1.2 The Importance of Predictive & Behavioral Analytics 62
4.2 Artificial Intelligence & Machine Learning 62
4.2.1 Towards Self-Learning SON Engines with Machine Learning 63
4.2.2 Deep Learning: Enabling “Zero-Touch” Mobile Networks 63
4.3 NFV (Network Functions Virtualization) 64
4.3.1 Enabling the SON-Driven Deployment of VNFs (Virtualized Network Functions) 65
4.4 SDN (Software Defined Networking) & Programmability 66
4.4.1 Using the SDN Controller as a Platform for SON in Transport Networks 66
4.5 Cloud Computing 67
4.5.1 Facilitating C-SON Scalability & Elasticity 67
4.6 Small Cells, HetNets & RAN Densification 67
4.6.1 Plug & Play Small Cells 68
4.6.2 Coordinating UDNs (Ultra Dense Networks) with SON 68
4.7 C-RAN (Centralized RAN) & Cloud RAN 69
4.7.1 Efficient Resource Utilization in C-RAN Deployments with SON 70
4.8 Unlicensed & Shared Spectrum Usage 71
4.8.1 Dynamic Management of Spectrum with SON 72
4.9 MEC (Multi-Access Edge Computing) 72
4.9.1 Potential Synergies with SON 73
4.10 Network Slicing 73
4.10.1 Use of SON Mechanisms for Network Slicing in 5G Networks 74
4.11 Other Trends & Complementary Technologies 75
4.11.1 Alternative Carrier/Private LTE & 5G-Ready Networks 75
4.11.2 FWA (Fixed Wireless Access) 76
4.11.3 DPI (Deep Packet Inspection) 76
4.11.4 Digital Security for Self-Protection 77
4.11.5 SON Capabilities for IoT Applications 78
4.11.6 User-Based Profiling & Optimization for Vertical 5G Applications 78
4.11.7 Addressing D2D (Device-to-Device) Communications & New Use Cases 79

Continue…

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