表紙:5G時代におけるSON (Self-Organizing Network):市場機会・課題・戦略・成長予測 - 韓国特別版
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5G時代におけるSON (Self-Organizing Network):市場機会・課題・戦略・成長予測 - 韓国特別版

SON (Self-Organizing Networks) in the 5G Era: 2019 - 2030 - Opportunities, Challenges, Strategies & Forecasts - South Korea Special Edition

出版日: | 発行: SNS Telecom & IT | ページ情報: 英文 367 Pages | 納期: 即日から翌営業日

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5G時代におけるSON (Self-Organizing Network):市場機会・課題・戦略・成長予測 - 韓国特別版
出版日: 2018年09月19日
発行: SNS Telecom & IT
ページ情報: 英文 367 Pages
納期: 即日から翌営業日
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  • 概要
  • 目次
概要

世界のSON (Self-Organizing Network) 技術への投資額は、ネットワークの高密度化や帯域の異種性などを含む今日のマルチRANモバイルネットワークの複雑化や5G NR (New Radio) インフラの展開などが主な推進因子となり、予測期間中、約11%のCAGR (年間複合成長率) で推移し、2022年末には55億ドルの市場規模に達すると予測されています。

当レポートでは、5G時代におけるSON (Self-Organizing Network) 技術の市場を調査し、SONおよびモバイルネットワーク最適化のエコシステム、SONの技術概要・利用事例・実装アーキテクチャ、次世代LTEと5G SONの実装に関する主要動向、関連規格・法規制・提携イニチアチブ、主要事業者によるSON展開のケーススタディ、将来のロードマップとバリューチェーン、ネットワーク・アーキテクチャ・アクセス技術・地域別の市場規模の推移と予測、将来の展望・各種提言などをまとめています。

韓国特別版には、韓国でのSON投資予測を詳細に示したEXCELデータシートが特別に添付されています。

第1章 イントロダクション

第2章 SON & モバイルネットワークの最適化のエコシステム

  • 従来のモバイルネットワークの最適化
  • SON (Self-Organizing Network) のコンセプト
  • SONの機能領域
  • SON導入の推進因子
  • SON導入の障壁

第3章 SON技術・利用事例・実装アーキテクチャ

  • モバイルネットワークにおける居所
  • SONアーキテクチャ
  • 利用事例
    • ネットワークエレメントのセルフコンフィギュレーション
    • 自動接続管理
    • ネットワークエレメントの自己検査
    • ネットワークエレメント/ソフトウェアのセルフリカバリー
    • ボード故障の自己修理
    • 自動インベントリー
    • ANR (Automatic Neighbor Relations)
    • PCI (Physical Cell ID) コンフィギュレーション
    • CCO (Coverage & Capacity Optimization)
    • MRO (Mobility Robustness Optimization)
    • MLB (Mobility Load Balancing)
    • RACH (Random Access Channel) 最適化
    • ICIC (Inter-Cell Interference Coordination)
    • eICIC (Enhanced ICIC)
    • 省エネ
    • COD/COC (Cell Outage Detection & Compensation)
    • MDT (Minimization of Drive Tests)
    • AAS (Adaptive Antenna Systems) & Massive MIMO
    • 5G NR (New Radio) ネットワークのミリ波リンク
    • スモールセルのセルフコンフィギュレーション & 最適化
    • DAS (Distributed Antenna Systems)の最適化、など

第4章 次世代LTE & 5G SON実装における主要動向

  • ビッグデータ & 先進アナリティクス
  • AI & 機械学習
  • NFV
  • SDN & プログラマビリティ
  • クラウドコンピューティング
  • スモールセル・HetNet・RANの高密度化
  • C-RAN & クラウドRAN
  • 免許不要帯域・共有帯域の利用
  • MEC (Multi-Access Edge Computing)
  • ネットワークスライシング
  • その他の動向・補足技術

第5章 標準規格・法規制・提携イニチアチブ

  • 3GPP
  • NGMN Alliance
  • ETSI
  • Linux Foundation ONAP
  • OSSii
  • Small Cell Forum
  • WBA
  • CableLabs
  • 5G PPP & European Union Projects

第6章 SON展開のケーススタディ

  • AT&T
  • BCE (Bell Canada)
  • Bharti Airtel
  • Elisa
  • Globe Telecom
  • MegaFon
  • Orange
  • Singtel
  • SK Telecom
  • Telefonica Group
  • TIM (Telecom Italia Mobile)
  • Turkcell
  • Verizon Communications
  • Vodafone Group

第7章 将来のロードマップ・バリューチェーン

  • 将来のロードマップ
  • バリューチェーン
  • エコシステム:組み込み技術
  • エコシステム:RAN
  • エコシステム:トランスポートネットワーキング
  • エコシステム:モバイルコア
  • エコシステム:コネクティビティ
  • エコシステム:SON
  • エコシステム:SDN & NFV
  • エコシステム:MEC

第8章 主な参入事業者

第9章 市場規模・成長予測

  • 収益:SON & モバイルネットワークの最適化
  • 収益:SON
  • SON収益:ネットワーク別
    • RAN
    • モバイルコア
    • トランスポート (バックホール & フロントホール)
  • SON収益:アーキテクチャ別
    • C-SON
    • D-SON
  • SON収益:アクセスネットワーク技術別
    • 2G & 3G
    • LTE
    • 5G
    • Wi-Fi
  • SON・従来型モバイルネットワークプランニング&最適化の収益:地域別
    • アジア太平洋
    • 東欧
    • 中南米
    • 中東・アフリカ
    • 北米
    • 西欧

第10章 総論・戦略的提言

目次

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.

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.

This special edition of the report also features an additional Excel datasheet detailing additional forecasts for SON investments in South Korea

Additional Details

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

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?

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 Kibris 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

Countries Covered:

  • Afghanistan
  • Albania
  • Algeria
  • Andorra
  • Angola
  • Anguilla
  • Antigua & Barbuda
  • Argentina
  • Armenia
  • Aruba
  • Australia
  • Austria
  • Azerbaijan
  • Bahamas
  • Bahrain
  • Bangladesh
  • Barbados
  • Belarus
  • Belgium
  • Belize
  • Benin
  • Bermuda
  • Bhutan
  • Bolivia
  • Bosnia Herzegovina
  • Botswana
  • Brazil
  • British Virgin Islands
  • Brunei
  • Bulgaria
  • Burkina Faso
  • Burundi
  • Cambodia
  • Cameroon
  • Canada
  • Cape Verde
  • Cayman Islands
  • Central African Republic
  • Chad
  • Chile
  • China
  • Cocos Islands
  • Colombia
  • Comoros Islands
  • Congo
  • Cook Islands
  • Costa Rica
  • Côte d'Ivoire
  • Croatia
  • Cuba
  • Cyprus
  • Czech Republic
  • Democratic Rep of Congo (ex-Zaire)
  • Denmark
  • Djibouti
  • Dominica
  • Dominican Republic
  • East Timor
  • Ecuador
  • Egypt
  • El Salvador
  • Equatorial Guinea
  • Eritrea
  • Estonia
  • Ethiopia
  • Faroe Islands
  • Federated States of Micronesia
  • Fiji
  • Finland
  • France
  • French Guiana
  • French Polynesia (ex-Tahiti)
  • French West Indies
  • Gabon
  • Gambia
  • Georgia
  • Germany
  • Ghana
  • Gibraltar
  • Greece
  • Greenland
  • Grenada
  • Guam
  • Guatemala
  • Guernsey
  • Guinea Republic
  • Guinea-Bissau
  • Guyana
  • Haiti
  • Honduras
  • Hong Kong
  • Hungary
  • Iceland
  • India
  • Indonesia
  • Iran
  • Iraq
  • Ireland
  • Isle of Man
  • Israel
  • Italy
  • Jamaica
  • Japan
  • Jersey
  • Jordan
  • Kazakhstan
  • Kenya
  • Kirghizstan
  • Kiribati
  • Korea
  • Kosovo
  • Kuwait
  • Laos
  • Latvia
  • Lebanon
  • Lesotho
  • Liberia
  • Libya
  • Liechtenstein
  • Lithuania
  • Luxembourg
  • Macau
  • Macedonia
  • Madagascar
  • Malawi
  • Malaysia
  • Maldives
  • Mali
  • Malta
  • Marshall Islands
  • Mauritania
  • Mauritius
  • Mayotte
  • Mexico
  • Moldova
  • Monaco
  • Mongolia
  • Montenegro
  • Montserrat
  • Morocco
  • Mozambique
  • Myanmar
  • Namibia
  • Nepal
  • Netherlands
  • Netherlands Antilles
  • New Caledonia
  • New Zealand
  • Nicaragua
  • Niger
  • Nigeria
  • Niue
  • North Korea
  • Northern Marianas
  • Norway
  • Oman
  • Pakistan
  • Palau
  • Palestine
  • Panama
  • Papua New Guinea
  • Paraguay
  • Peru
  • Philippines
  • Poland
  • Portugal
  • Puerto Rico
  • Qatar
  • Réunion
  • Romania
  • Russia
  • Rwanda
  • Samoa
  • Samoa (American)
  • Sao Tomé & Principe
  • Saudi Arabia
  • Senegal
  • Serbia
  • Seychelles
  • Sierra Leone
  • Singapore
  • Slovak Republic
  • Slovenia
  • Solomon Islands
  • Somalia
  • South Africa
  • Spain
  • Sri Lanka
  • St Kitts & Nevis
  • St Lucia
  • St Vincent & The Grenadines
  • Sudan
  • Suriname
  • Swaziland
  • Sweden
  • Switzerland
  • Syria
  • Tajikistan
  • Taiwan
  • Tanzania
  • Thailand
  • Togo
  • Tonga
  • Trinidad & Tobago
  • Tunisia
  • Turkey
  • Turkmenistan
  • Turks & Caicos Islands
  • UAE
  • Uganda
  • UK
  • Ukraine
  • Uruguay
  • US Virgin Islands
  • USA
  • Uzbekistan
  • Vanuatu
  • Venezuela
  • Vietnam
  • Yemen
  • Zambia
  • Zimbabwe

Table of Contents

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
    • 6.1.1. Vendor Selection
    • 6.1.2. SON Deployment Review
    • 6.1.3. Results & Future Plans
  • 6.2. BCE (Bell Canada)
    • 6.2.1. Vendor Selection
    • 6.2.2. SON Deployment Review
    • 6.2.3. Results & Future Plans
  • 6.3. Bharti Airtel
    • 6.3.1. Vendor Selection
    • 6.3.2. SON Deployment Review
    • 6.3.3. Results & Future Plans
  • 6.4. Elisa
    • 6.4.1. Vendor Selection
    • 6.4.2. SON Deployment Review
    • 6.4.3. Results & Future Plans
  • 6.5. Globe Telecom
    • 6.5.1. Vendor Selection
    • 6.5.2. SON Deployment Review
    • 6.5.3. Results & Future Plans
  • 6.6. KDDI Corporation
    • 6.6.1. Vendor Selection
    • 6.6.2. SON Deployment Review
    • 6.6.3. Results & Future Plans
  • 6.7. MegaFon
    • 6.7.1. Vendor Selection
    • 6.7.2. SON Deployment Review
    • 6.7.3. Results & Future Plans
  • 6.8. Orange
    • 6.8.1. Vendor Selection
    • 6.8.2. SON Deployment Review
    • 6.8.3. Results & Future Plans
  • 6.9. Singtel
    • 6.9.1. Vendor Selection
    • 6.9.2. SON Deployment Review
    • 6.9.3. Results & Future Plans
  • 6.10. SK Telecom
    • 6.10.1. Vendor Selection
    • 6.10.2. SON Deployment Review
    • 6.10.3. Results & Future Plans
  • 6.11. Telefónica Group
    • 6.11.1. Vendor Selection
    • 6.11.2. SON Deployment Review
    • 6.11.3. Results & Future Plans
  • 6.12. TIM (Telecom Italia Mobile)
    • 6.12.1. Vendor Selection
    • 6.12.2. SON Deployment Review
    • 6.12.3. Results & Future Plans
  • 6.13. Turkcell
    • 6.13.1. Vendor Selection
    • 6.13.2. SON Deployment Review
    • 6.13.3. Results & Future Plans
  • 6.14. Verizon Communications
    • 6.14.1. Vendor Selection
    • 6.14.2. SON Deployment Review
    • 6.14.3. Results & Future Plans
  • 6.15. Vodafone Group
    • 6.15.1. Vendor Selection
    • 6.15.2. SON Deployment Review
    • 6.15.3. Results & Future Plans

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
  • 9.9. Asia Pacific
    • 9.9.1. SON
    • 9.9.2. Conventional Mobile Network Planning & Optimization
  • 9.10. Eastern Europe
    • 9.10.1. SON
    • 9.10.2. Conventional Mobile Network Planning & Optimization
  • 9.11. Latin & Central America
    • 9.11.1. SON
    • 9.11.2. Conventional Mobile Network Planning & Optimization
  • 9.12. Middle East & Africa
    • 9.12.1. SON
    • 9.12.2. Conventional Mobile Network Planning & Optimization
  • 9.13. North America
    • 9.13.1. SON
    • 9.13.2. Conventional Mobile Network Planning & Optimization
  • 9.14. Western Europe
    • 9.14.1. SON
    • 9.14.2. Conventional Mobile Network Planning & Optimization

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

List of Figures

  • Figure 1: Functional Areas of SON within the Mobile Network Lifecycle
  • Figure 2: Annual Throughput of Mobile Network Data Traffic by Region: 2019 - 2030 (Exabytes)
  • Figure 3: Global Wireless Network Infrastructure Revenue Share by Submarket (%)
  • Figure 4: SON Associated OpEx & CapEx Savings by Network Segment (%)
  • Figure 5: Potential Areas of SON Implementation
  • Figure 6: Mobile Backhaul & Fronthaul Technologies
  • Figure 7: C-SON (Centralized SON) in a Mobile Operator Network
  • Figure 8: D-SON (Distributed SON) in a Mobile Operator Network
  • Figure 9: H-SON (Hybrid SON) in a Mobile Operator Network
  • Figure 10: NFV Concept
  • Figure 11: Transition to UDNs (Ultra-Dense Networks)
  • Figure 12: C-RAN Architecture
  • Figure 13: Conceptual Architecture for End-to-End Network Slicing in Mobile Networks
  • Figure 14: Comparison Between DPI & Shallow Packet Inspection
  • Figure 15: NGNM SON Use Cases
  • Figure 16: SELFNET's SON Implementation Framework
  • Figure 17: AT&T's SON Implementation
  • Figure 18: Elisa's In-House SON Solution
  • Figure 19: KDDI's Artificial Intelligence-Assisted Automated Network Operation System
  • Figure 20: Orange's Vision for Cognitive PBSM (Policy Based SON Management)
  • Figure 21: SK Telecom's Fast Data Platform for QoE-Based Automatic Network Optimization
  • Figure 22: Telefónica's SON Deployment Roadmap From 4G To 5G Rollouts
  • Figure 23: TIM's Open SON Architecture
  • Figure 24: SON Future Roadmap: 2019 - 2030
  • Figure 25: Wireless Network Infrastructure Value Chain
  • Figure 26: Global SON & Mobile Network Optimization Revenue: 2019 - 2030 ($ Million)
  • Figure 27: Global SON Revenue: 2019 - 2030 ($ Million)
  • Figure 28: Global SON Revenue by Network Segment: 2019 - 2030 ($ Million)
  • Figure 29: Global SON Revenue in the RAN Segment: 2019 - 2030 ($ Million)
  • Figure 30: Global SON Revenue in the Mobile Core Segment: 2019 - 2030 ($ Million)
  • Figure 31: Global SON Revenue in the Transport (Backhaul & Fronthaul) Segment: 2019 - 2030 ($ Million)
  • Figure 32: Global SON Revenue by Architecture: 2019 - 2030 ($ Million)
  • Figure 33: Global C-SON Revenue: 2019 - 2030 ($ Million)
  • Figure 34: Global D-SON Revenue: 2019 - 2030 ($ Million)
  • Figure 35: Global SON Revenue by Access Network Technology: 2019 - 2030 ($ Million)
  • Figure 36: Global 2G & 3G SON Revenue: 2019 - 2030 ($ Million)
  • Figure 37: Global LTE SON Revenue: 2019 - 2030 ($ Million)
  • Figure 38: Global 5G SON Revenue: 2020 - 2030 ($ Million)
  • Figure 39: Global Wi-Fi & Other Access Technology SON Revenue: 2019 - 2030 ($ Million)
  • Figure 40: SON Revenue by Region: 2019 - 2030 ($ Million)
  • Figure 41: Global Conventional Mobile Network Planning & Optimization Revenue: 2019 - 2030 ($ Million)
  • Figure 42: Conventional Mobile Network Planning & Optimization Revenue by Region: 2019 - 2030 ($ Million)
  • Figure 43: Asia Pacific SON Revenue: 2019 - 2030 ($ Million)
  • Figure 44: Asia Pacific Conventional Mobile Network Planning & Optimization Revenue: 2019 - 2030 ($ Million)
  • Figure 45: Eastern Europe SON Revenue: 2019 - 2030 ($ Million)
  • Figure 46: Eastern Europe Conventional Mobile Network Planning & Optimization Revenue: 2019 - 2030 ($ Million)
  • Figure 47: Latin & Central America SON Revenue: 2019 - 2030 ($ Million)
  • Figure 48: Latin & Central America Conventional Mobile Network Planning & Optimization Revenue: 2019 - 2030 ($ Million)
  • Figure 49: Middle East & Africa SON Revenue: 2019 - 2030 ($ Million)
  • Figure 50: Middle East & Africa Conventional Mobile Network Planning & Optimization Revenue: 2019 - 2030 ($ Million)
  • Figure 51: North America SON Revenue: 2019 - 2030 ($ Million)
  • Figure 52: North America Conventional Mobile Network Planning & Optimization Revenue: 2019 - 2030 ($ Million)
  • Figure 53: Western Europe SON Revenue: 2019 - 2030 ($ Million)
  • Figure 54: Western Europe Conventional Mobile Network Planning & Optimization Revenue: 2019 - 2030 ($ Million)
  • Figure 55: SON Associated OpEx Savings by Region: 2019 - 2030 ($ Million)