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市場調查報告書

SON (自組織網路) 的生態系統:2015-2030年 - 市場機會、課題、策略及預測

The SON (Self-Organizing Networks) Ecosystem: 2016 - 2030 - Opportunities, Challenges, Strategies & Forecasts

出版商 Signals and Systems Telecom 商品編碼 337365
出版日期 內容資訊 英文 247 Pages
商品交期: 最快1-2個工作天內
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SON (自組織網路) 的生態系統:2015-2030年 - 市場機會、課題、策略及預測 The SON (Self-Organizing Networks) Ecosystem: 2016 - 2030 - Opportunities, Challenges, Strategies & Forecasts
出版日期: 2016年10月07日 內容資訊: 英文 247 Pages
簡介

雖然實行複雜,及複數供應商的互通性相關問題,SON (自組織網路) 收益預計至2017年末成長到40億美元以上,傳統行動網路最佳化收益超過約60%。

本報告提供SON及相關的行動網路最佳化生態系統相關資料、主要市場促進成長要素、課題、OpEx、CapEx的削減預定、利用案例、包含SON引進的案例研究、未來發展藍圖、供應鏈、供應商分析及策略詳細調查、SON及傳統行動網路最佳化的收益預測、7個SON次市場、6個地區,及主要15個國家的收益預測。

第1章 簡介

第2章 SON & 行動網路最佳化的生態系統

  • 傳統的行動網路最佳化
  • SON (自組織網路) 的概念
  • SON的功能領域
  • SON引進的推動市場成長要素
  • SON引進的阻礙市場成長要素

第3章 SON技術、利用案例及實行架構

  • SON位於行動網路內的什麼位置?
  • SON的架構
  • SON的利用案例

第4章 SON的標準化

  • NGNM (下一代行動網路) 聯盟
  • 3GPP (第3代夥伴關係計劃)

第5章 SON引進的案例研究

  • AT&T Mobility
  • Singtel
  • TIM Brasil
  • KDDI
  • SK Telecom
  • Globe Telecom

第6章 產業藍圖、價值鏈

  • 產業藍圖
  • 價值鏈
  • 內建式技術的生態系統
  • RAN的生態系統
  • 行動回程網路 & Fronthaul的生態系統
  • 行動核心的生態系統
  • 連接性的生態系統
  • SON & 行動網路最佳化的生態系統
  • SDN & NFV的生態系統

第7章 業者情勢

第8章 市場分析、預測

  • SDN & 行動網路最佳化的收益
  • SDN收益
  • SDN收益:各網路市場區隔
  • SDN收益,架構:集中化 vs. 分權化
  • SDN收益,無線網路世代:2G/3G vs. 4G以上
  • SON收益:各地區
  • 傳統的行動網路規劃、最佳化收益
  • 傳統的行動網路規劃、最佳化收益:各地區
  • 亞太地區
  • 東歐
  • 南美、中美
  • 中東、非洲
  • 北美
  • 西歐
  • 主要國家市場
    • 澳洲
    • 巴西
    • 加拿大
    • 中國
    • 法國
    • 德國
    • 印度
    • 義大利
    • 日本
    • 俄羅斯
    • 韓國
    • 西班牙
    • 台灣
    • 英國
    • 美國

第9章 結論、策略性建議

  • QoE型SON平台的轉變
  • DPI (深層封包檢測) 的有效利用
  • 巨量資料、預測分析及SON的匯流
  • M2M & IoT服務的最佳化
  • 來自面向NFV & SDNSON:手機業者的推動
  • 行動核心及傳輸網路的轉變
  • SON影響最佳化 & 現場工程師的評估
  • SON相關OpEx的節約:數字
  • 哪個SON功能需要5G網路?
  • C-SON vs. D-SON的討論
  • 策略性建議

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目錄

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

Amid growing demands for mobile broadband connectivity, mobile operators 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 RAN (Radio Access Network) segment of mobile networks, SON technology is now also utilized in the mobile core and transport network segments. In addition, Wi-Fi access point OEMs are beginning to integrate SON features such as plug-and-play deployment, autonomous performance optimization, self-healing and proactive defense against unauthorized access.

Despite challenges relating to implementation complexities and multi-vendor interoperability, SON revenue is expected to grow to more than $5 Billion by the end of 2020, exceeding conventional mobile network optimization revenue by a significant margin. Furthermore, the SON ecosystem is increasingly witnessing convergence with other technological innovations such as Big Data, predictive analytics and DPI (Deep Packet Inspection).

The “SON (Self-Organizing Networks) Ecosystem: 2016 - 2030 - Opportunities, Challenges, Strategies & Forecasts” 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 10 SON submarkets, 6 regions and 15 countries from 2016 through to 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:

  • 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
  • Future roadmap of the SON ecosystem
  • Company profiles and strategies of over 120 SON ecosystem players
  • OpEx and CapEx saving analysis of SON
  • Wireless network infrastructure spending, traffic projections and value chain
  • Convergence of SON with Big Data, predictive analytics and DPI
  • Strategic recommendations for SON solution providers, wireless infrastructure OEMs and mobile operators
  • Market analysis and forecasts from 2016 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

  • Macrocell RAN
  • HetNet RAN
  • Mobile Core
  • Mobile Backhaul & Fronthaul

SON Architecture Submarkets

  • C-SON (Centralized SON)
  • D-SON (Distributed SON)

SON Access Network Technology Submarkets

  • 2G & 3G
  • LTE
  • Wi-Fi
  • 5G

Regional Markets

  • Asia Pacific
  • Eastern Europe
  • Latin & Central America
  • Middle East & Africa
  • North America
  • Western Europe

Country Markets

  • Australia
  • Brazil
  • Canada
  • China
  • France
  • Germany
  • India
  • Italy
  • Japan
  • Russia
  • South Korea
  • Spain
  • Taiwan
  • UK
  • USA

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?
  • How can SON ease the deployment of unlicensed LTE small cells?
  • What SON capabilities will 5G networks entail?
  • What is the outlook for C-SON and D-SON adoption?
  • How will SON investments compare with those on traditional mobile network optimization?
  • What opportunities exist for SON in mobile core and transport networks?
  • How will SON use cases evolve overtime in 3GPP releases?
  • Which regions will see the highest number of SON investments?
  • How much will mobile operators invest in SON solutions?
  • What is the outlook for SON associated OpEx savings by region?

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 $5 Billion by the end of 2020, exceeding conventional mobile network optimization revenue by a significant margin.
  • Mobile operators have reported up to a 50% reduction in dropped calls and over 20% higher data rates with SON implementation. Besides common network optimization use cases, operators are also capitalizing on SON platforms to address critical business objectives such as refarming 2G/3G spectrum for LTE networks.
  • In a bid to differentiate their products, Wi-Fi access point OEMs are beginning to integrate SON features such as plug-and-play deployment, autonomous performance optimization, self-healing and proactive defense against unauthorized access.
  • SON platforms are moving from reactive systems to more advanced implementations that incorporate predictive analytics technology to make necessary changes to a network before any degradation occurs.
  • Infrastructure and software incumbents are continuing to acquire smaller established C-SON players to accelerate their entry path into the C-SON market.

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
  • 2.4. 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

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 & 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 (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

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
  • 4.3. Small Cell Forum
    • 4.3.1. Release 7: Focus on SON for Small Cells
    • 4.3.2. SON API
    • 4.3.3. X2 Interoperability
  • 4.4. WBA (Wireless Broadband Alliance)
    • 4.4.1. SON Integration in Carrier Wi-Fi Guidelines
  • 4.5. CableLabs
    • 4.5.1. SON Parameter Exchange in Wi-Fi Gateway Management Specification

Chapter 5: SON Deployment Case Studies

  • 5.1. AT&T
    • 5.1.1. Vendor Selection
    • 5.1.2. Implemented Use Cases
    • 5.1.3. Results
  • 5.2. Globe Telecom
    • 5.2.1. Vendor Selection
    • 5.2.2. Implemented Use Cases
    • 5.2.3. Results
  • 5.3. KDDI Corporation
    • 5.3.1. Vendor Selection
    • 5.3.2. Implemented Use Cases
    • 5.3.3. Results
  • 5.4. Singtel Group
    • 5.4.1. Vendor Selection
    • 5.4.2. Implemented Use Cases
    • 5.4.3. Results
  • 5.5. SK Telecom
    • 5.5.1. Vendor Selection
    • 5.5.2. Implemented Use Cases
    • 5.5.3. Results
  • 5.6. Telefónica Group
    • 5.6.1. Vendor Selection
    • 5.6.2. Implemented Use Cases
    • 5.6.3. Results
  • 5.7. TIM (Telecom Italia Mobile)
    • 5.7.1. Vendor Selection
    • 5.7.2. Implemented Use Cases
    • 5.7.3. Results
  • 5.8. Turkcell Group
    • 5.8.1. Vendor Selection
    • 5.8.2. Implemented Use Cases
    • 5.8.3. Results
  • 5.9. Vodafone Group
    • 5.9.1. Vendor Selection
    • 5.9.2. Implemented Use Cases
    • 5.9.3. Results

Chapter 6: Industry Roadmap & Value Chain

  • 6.1. Industry Roadmap
    • 6.1.1. Large Scale Adoption of SON Technology: 2016 - 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 Small Cell OEMs
    • 6.4.3. Wi-Fi Access Point OEMs
    • 6.4.4. DAS & Repeater Solution Providers
    • 6.4.5. C-RAN Solution Providers
    • 6.4.6. Other Technology Providers
  • 6.5. Transport Networking Ecosystem
    • 6.5.1. Backhaul & Fronthaul Solution Providers
  • 6.6. Mobile Core Ecosystem
    • 6.6.1. Mobile Core Solution Providers
  • 6.7. Connectivity Ecosystem
    • 6.7.1. Mobile Operators
    • 6.7.2. Wi-Fi Connectivity Providers
    • 6.7.3. SCaaS (Small Cells as a Service) Providers
  • 6.8. SON Ecosystem
    • 6.8.1. SON Solution Providers
  • 6.9. SDN & NFV Ecosystem
    • 6.9.1. SDN & NFV Providers

Chapter 7: Vendor Landscape

  • 7.1. Accedian Networks
  • 7.2. Accelleran
  • 7.3. Accuver
  • 7.4. AirHop Communications
  • 7.5. Airspan Networks
  • 7.6. Alvarion Technologies
  • 7.7. Altiostar Networks
  • 7.8. Amdocs
  • 7.9. Arcadyan Technology Corporation
  • 7.10. Argela
  • 7.11. Aricent
  • 7.12. ARItel
  • 7.13. Artemis Networks
  • 7.14. Astellia
  • 7.15. ASUS (ASUSTeK Computer)
  • 7.16. ATDI
  • 7.17. Avvasi
  • 7.18. Baicells
  • 7.19. Belkin International
  • 7.20. Benu Networks
  • 7.21. BLiNQ Networks
  • 7.22. Broadcom
  • 7.23. Brocade Communications Systems
  • 7.24. Casa Systems
  • 7.25. Cavium
  • 7.26. CBNL (Cambridge Broadband Networks Limited)
  • 7.27. CCS (Cambridge Communication Systems)
  • 7.28. CellMining
  • 7.29. Cellwize
  • 7.30. Celtro
  • 7.31. CENTRI
  • 7.32. Cisco Systems
  • 7.33. Citrix Systems
  • 7.34. Comarch
  • 7.35. CommAgility
  • 7.36. CommScope
  • 7.37. Commsquare
  • 7.38. Contela
  • 7.39. Coriant
  • 7.40. Datang Mobile
  • 7.41. Dell EMC
  • 7.42. Digitata
  • 7.43. D-Link Corporation
  • 7.44. ECE (European Communications Engineering)
  • 7.45. Equiendo
  • 7.46. Ericsson
  • 7.47. Ercom
  • 7.48. EXFO
  • 7.49. Flash Networks
  • 7.50. Forsk
  • 7.51. Fujitsu
  • 7.52. Gemtek Technology Company
  • 7.53. General Dynamics Mission Systems
  • 7.54. GoNet Systems
  • 7.55. Guavus
  • 7.56. GWT (Global Wireless Technologies)
  • 7.57. Hitachi
  • 7.58. Huawei
  • 7.59. InfoVista
  • 7.60. Innovile
  • 7.61. Intel Corporation
  • 7.62. InterDigital
  • 7.63. Intracom Telecom
  • 7.64. ip.access
  • 7.65. JRC (Japan Radio Company)
  • 7.66. Juni Global
  • 7.67. Keysight Technologies
  • 7.68. Kumu Networks
  • 7.69. Lemko Corporation
  • 7.70. Luminate Wireless
  • 7.71. Mojo Networks
  • 7.72. NEC Corporation
  • 7.73. NetScout Systems
  • 7.74. New Postcom Equipment Company
  • 7.75. Nokia Networks
  • 7.76. Nutaq
  • 7.77. NXP Semiconductors
  • 7.78. Oceus Networks
  • 7.79. Opera Software
  • 7.80. Optulink
  • 7.81. Parallel Wireless
  • 7.82. P.I.Works
  • 7.83. Phluido
  • 7.84. Plano Engineering
  • 7.85. Potevio (China Potevio Company)
  • 7.86. Qualcomm
  • 7.87. Quanta Computer
  • 7.88. Qucell
  • 7.89. RADCOM
  • 7.90. Radisys Corporation
  • 7.91. RED Technologies
  • 7.92. Redline Communications
  • 7.93. Rohde & Schwarz
  • 7.94. Samji Electronics Company
  • 7.95. Samsung Electronics
  • 7.96. SEDICOM
  • 7.97. SerComm Corporation
  • 7.98. Seven Networks
  • 7.99. Siklu Communication
  • 7.100. SK Telesys
  • 7.101. SpiderCloud Wireless
  • 7.102. Star Solutions
  • 7.103. Tarana Wireless
  • 7.104. Tecore
  • 7.105. TEKTELIC Communications
  • 7.106. Telrad Networks
  • 7.107. Telum
  • 7.108. TEOCO
  • 7.109. TI (Texas Instruments)
  • 7.110. TP-Link Technologies
  • 7.111. TTG International
  • 7.112. Tulinx
  • 7.113. Vasona Networks
  • 7.114. Viavi Solutions
  • 7.115. WebRadar
  • 7.116. WNC (Wistron NeWeb Corporation)
  • 7.117. WPOTECH
  • 7.118. XCellAir
  • 7.119. Z-Com (ZDC Wireless)
  • 7.120. ZTE
  • 7.121. ZyXEL Communications Corporation

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. Conventional Macrocell RAN
    • 8.3.2. HetNet RAN
    • 8.3.3. Mobile Core
    • 8.3.4. Mobile Backhaul & Fronthaul
  • 8.4. SON Revenue by Architecture: Centralized vs. Distributed
    • 8.4.1. C-SON
    • 8.4.2. D-SON
  • 8.5. SON Revenue by Access Network Technology
    • 8.5.1. 2G & 3G
    • 8.5.2. LTE
    • 8.5.3. Wi-Fi
    • 8.5.4. 5G
  • 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

Chapter 9: Key Trends, 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. Impact of Unlicensed LTE Small Cells
  • 9.9. Growing Adoption of SON Capabilities for Wi-Fi
  • 9.10. SON Associated OpEx Savings: The Numbers
  • 9.11. What SON Capabilities Will 5G Networks Entail?
    • 9.11.1. Predictive Resource Allocation
    • 9.11.2. Addressing D2D (Device-to-Device) Communications & New Use Cases
    • 9.11.3. User-Based Profiling & Optimization for Vertical 5G Applications
    • 9.11.4. Greater Focus on Self-Protection Capabilities
  • 9.12. The C-SON Versus D-SON Debate
  • 9.13. Strategic Recommendations
    • 9.13.1. SON & Conventional Mobile Network Optimization Solution Providers
    • 9.13.2. Wireless Infrastructure OEMs
    • 9.13.3. 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: 2016 - 2030 (Exabytes)
  • Figure 3: Global Wireless Network Infrastructure Revenue Share by Submarket (%)
  • Figure 4: Global Mobile Network Data Traffic Distribution by Access Network Form Factor: 2016 - 2030 (%)
  • Figure 5: SON Associated OpEx & CapEx Savings by Network Segment
  • Figure 6: Potential Areas of SON Implementation
  • Figure 7: Mobile Backhaul & Fronthaul Segmentation by Technology
  • Figure 8: C-SON (Centralized SON) in a Mobile Operator Network
  • Figure 9: D-SON (Distributed SON) in a Mobile Operator Network
  • Figure 10: H-SON (Hybrid SON) in a Mobile Operator Network
  • Figure 11: NGNM SON Use Cases
  • Figure 12: SON Industry Roadmap: 2016 - 2030
  • Figure 13: Wireless Network Infrastructure Value Chain
  • Figure 14: Global SON & Mobile Network Optimization Revenue: 2016 - 2030 ($ Million)
  • Figure 15: Global SON Revenue: 2016 - 2030 ($ Million)
  • Figure 16: Global SON Revenue by Network Segment: 2016 - 2030 ($ Million)
  • Figure 17: Global Macrocell RAN SON Revenue: 2016 - 2030 ($ Million)
  • Figure 18: Global HetNet RAN SON Revenue: 2016 - 2030 ($ Million)
  • Figure 19: Global Mobile Core SON Revenue: 2016 - 2030 ($ Million)
  • Figure 20: Global Mobile Backhaul & Fronthaul SON Revenue: 2016 - 2030 ($ Million)
  • Figure 21: Global SON Revenue by Architecture: 2016 - 2030 ($ Million)
  • Figure 22: Global C-SON Revenue: 2016 - 2030 ($ Million)
  • Figure 23: Global D-SON Revenue: 2016 - 2030 ($ Million)
  • Figure 24: Global SON Revenue by Access Network Technology: 2016 - 2030 ($ Million)
  • Figure 25: Global 2G & 3G SON Revenue: 2016 - 2030 ($ Million)
  • Figure 26: Global LTE SON Revenue: 2016 - 2030 ($ Million)
  • Figure 27: Global Wi-Fi SON Revenue: 2016 - 2030 ($ Million)
  • Figure 28: Global 5G SON Revenue: 2020 - 2030 ($ Million)
  • Figure 29: SON Revenue by Region: 2016 - 2030 ($ Million)
  • Figure 30: Global Conventional Mobile Network Planning & Optimization Revenue: 2016 - 2030 ($ Million)
  • Figure 31: Conventional Mobile Network Planning & Optimization Revenue by Region: 2016 - 2030 ($ Million)
  • Figure 32: Asia Pacific SON Revenue: 2016 - 2030 ($ Million)
  • Figure 33: Asia Pacific Conventional Mobile Network Planning & Optimization Revenue: 2016 - 2030 ($ Million)
  • Figure 34: Eastern Europe SON Revenue: 2016 - 2030 ($ Million)
  • Figure 35: Eastern Europe Conventional Mobile Network Planning & Optimization Revenue: 2016 - 2030 ($ Million)
  • Figure 36: Latin & Central America SON Revenue: 2016 - 2030 ($ Million)
  • Figure 37: Latin & Central America Conventional Mobile Network Planning & Optimization Revenue: 2016 - 2030 ($ Million)
  • Figure 38: Middle East & Africa SON Revenue: 2016 - 2030 ($ Million)
  • Figure 39: Middle East & Africa Conventional Mobile Network Planning & Optimization Revenue: 2016 - 2030 ($ Million)
  • Figure 40: North America SON Revenue: 2016 - 2030 ($ Million)
  • Figure 41: North America Conventional Mobile Network Planning & Optimization Revenue: 2016 - 2030 ($ Million)
  • Figure 42: Western Europe SON Revenue: 2016 - 2030 ($ Million)
  • Figure 43: Western Europe Conventional Mobile Network Planning & Optimization Revenue: 2016 - 2030 ($ Million)
  • Figure 44: Australia SON Revenue: 2016 - 2030 ($ Million)
  • Figure 45: Brazil SON Revenue: 2016 - 2030 ($ Million)
  • Figure 46: Canada SON Revenue: 2016 - 2030 ($ Million)
  • Figure 47: China SON Revenue: 2016 - 2030 ($ Million)
  • Figure 48: France SON Revenue: 2016 - 2030 ($ Million)
  • Figure 49: Germany SON Revenue: 2016 - 2030 ($ Million)
  • Figure 50: India SON Revenue: 2016 - 2030 ($ Million)
  • Figure 51: Italy SON Revenue: 2016 - 2030 ($ Million)
  • Figure 52: Japan SON Revenue: 2016 - 2030 ($ Million)
  • Figure 53: Russia SON Revenue: 2016 - 2030 ($ Million)
  • Figure 54: South Korea SON Revenue: 2016 - 2030 ($ Million)
  • Figure 55: Spain SON Revenue: 2016 - 2030 ($ Million)
  • Figure 56: Taiwan SON Revenue: 2016 - 2030 ($ Million)
  • Figure 57: UK SON Revenue: 2016 - 2030 ($ Million)
  • Figure 58: USA SON Revenue: 2016 - 2030 ($ Million)
  • Figure 59: Global Unlicensed LTE Small Cell Unit Shipments: 2016 - 2030 (Thousands of Units)
  • Figure 60: Global Unlicensed LTE Small Cell Unit Shipment Revenue: 2016 - 2030 ($ Million)
  • Figure 61: SON Associated OpEx Savings by Region: 2016 - 2030 ($ Million)
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