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SON (自己組織ネットワーク) のエコシステム:2015-2030年 - 市場機会、課題、戦略および予測

The SON (Self-Organizing Networks) Ecosystem: 2015 - 2030 - Opportunities, Challenges, Strategies & Forecasts

発行 Signals and Systems Telecom 商品コード 337365
出版日 ページ情報 英文 180 Pages
納期: 即日から翌営業日
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SON (自己組織ネットワーク) のエコシステム:2015-2030年 - 市場機会、課題、戦略および予測 The SON (Self-Organizing Networks) Ecosystem: 2015 - 2030 - Opportunities, Challenges, Strategies & Forecasts
出版日: 2015年08月14日 ページ情報: 英文 180 Pages
担当者のコメント
SONはモバイルネットワークの一部としてSignals and Systems Telecom社が継続的に取り組んでいるテーマです。本レポートでは、SONの各技術の導入例と実装アーキテクチャを掲載し、市場分析の章では、ネットワークセグメント、世代別、アーキテクチャ別の予測を掲載するなど詳細な内容となっています。世界の各地域、主要国別の市場予測が提供されることも特徴の一つです。数値データ、予測値をまとめたエクセルファイルと一緒にお届けいたします。
概要

実装の複雑さ、および複数ベンダーの相互運用性に関連した問題にも関わらず、SON (自己組織ネットワーク) 収益は2017年末までに40億米ドル以上へ成長し、従来型モバイルネットワーク最適化収益を約60%上回ると予測されています。

当レポートでは、SONおよび関連のモバイルネットワーク最適化エコシステムについて、主な市場成長促進因子、課題、OpEx・CapExの削減見込み、利用例、SON導入のケーススタディ、将来のロードマップ、場チューチェーン、ベンダー分析および戦略を含めて詳細に調査し、SONおよび従来型モバイルネットワーク最適化の収益予測、7つのSON2次市場、6つの地域、および主要15カ国の収益予測を提供しています。

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最適化のエコシステム

  • 従来型のモバイルネットワーク最適化
  • SON (自己組織ネットワーク) のコンセプト
  • SONの機能領域
  • SON導入の市場成長促進要因
  • SON導入の市場成長阻害要因

第3章 SON技術、利用例および実装アーキテクチャー

  • SONはモバイルネットワーク内のどこに位置するのか?
  • SONのアーキテクチャー
  • SONの利用例

第4章 SONの標準化

  • NGNM (次世代モバイルネットワーク) アライアンス
  • 3GPP (第3世代パートナーシッププロジェクト)

第5章 SON導入のケーススタディ

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

第6章 産業ロードマップ・バリューチェーン

  • 産業ロードマップ
  • バリューチェーン
  • 組込み技術のエコシステム
  • RANのエコシステム
  • モバイルバックホール & フロントホールのエコシステム
  • モバイルコアのエコシステム
  • コネクティビティのエコシステム
  • SON & モバイルネットワーク最適化のエコシステム
  • SDN & NFVのエコシステム

第7章 ベンダー情勢

第8章 市場分析・予測

  • SDN & モバイルネットワーク最適化の収益
  • SDN収益
  • SDN収益:ネットワークセグメント別
  • SDN収益、アーキテクチャー別:集中化 vs. 分散化
  • SDN収益、ワイヤレスネットワーク世代別:2G/3G vs. 4G以上
  • SON収益:地域別
  • 従来型のモバイルネットワークプランニング・最適化収益
  • 従来型のモバイルネットワークプランニング・最適化収益:地域別
  • アジア太平洋地域
  • 東欧
  • ラテンアメリカ・中米
  • 中東・アフリカ
  • 北米
  • 西欧
  • 主要国市場
    • オーストラリア
    • ブラジル
    • カナダ
    • 中国
    • フランス
    • ドイツ
    • インド
    • イタリア
    • 日本
    • ロシア
    • 韓国
    • スペイン
    • 台湾
    • 英国
    • 米国

第9章 結論・戦略的提言

  • QoE型SONプラットフォームへの移行
  • DPI (ディープパケットインスペクション) の活用
  • ビッグデータ、予測分析およびSONのコンバージェンス
  • M2M & IoTサービスの最適化
  • NFV & SDN向けSON:モバイルオペレーターからの後押し
  • モバイルコアおよびトランスポートネットワークへの移行
  • SONが最適化 & フィールドエンジニアへ及ぼす影響の評価
  • SON関連OpExの節約:数字
  • どのSON機能が5Gネットワークを必要とするか?
  • C-SON vs. D-SONの議論
  • 戦略的提言

図表リスト

目次

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 HetNet 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. Furthermore, the SON ecosystem is increasingly witnessing convergence with other technological innovations such as Big Data, predictive analytics and DPI (Deep Packet Inspection).

Despite challenges relating to implementation complexities and multi-vendor interoperability, SON revenue is expected to grow to more than $4 Billion by the end of 2017, exceeding conventional mobile network optimization revenue by nearly 60%.

The “SON (Self-Organizing Networks) Ecosystem: 2015 - 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 7 SON submarkets, 6 regions and 15 countries from 2015 through to 2030.

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

Table of Contents

Chapter 1: Introduction

  • 1.1. Executive Summary
  • 1.2. Topics Covered
  • 1.3. Historical Revenue & Forecast Segmentation
  • 1.4. Key Questions Answered
  • 1.5. Key Findings
  • 1.6. Methodology
  • 1.7. Target Audience
  • 1.8. Companies & Organizations Mentioned

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 & Transport
    • 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

Chapter 5: SON Deployment Case Studies

  • 5.1. AT&T Mobility
    • 5.1.1. Vendor Selection & Contract Value
    • 5.1.2. Implemented Use Cases
    • 5.1.3. Results
  • 5.2. Singtel
    • 5.2.1. Vendor Selection & Contract Value
    • 5.2.2. Implemented Use Cases
    • 5.2.3. Results
  • 5.3. TIM Brasil
    • 5.3.1. Vendor Selection & Contract Value
    • 5.3.2. Implemented Use Cases
    • 5.3.3. Results
  • 5.4. KDDI
    • 5.4.1. Vendor Selection & Contract Value
    • 5.4.2. Implemented Use Cases
    • 5.4.3. Results
  • 5.5. SK Telecom
    • 5.5.1. Vendor Selection & Contract Value
    • 5.5.2. Implemented Use Cases
    • 5.5.3. Results
  • 5.6. Globe Telecom
    • 5.6.1. Vendor Selection & Contract Value
    • 5.6.2. Implemented Use Cases
    • 5.6.3. Results

Chapter 6: Industry Roadmap & Value Chain

  • 6.1. Industry Roadmap
    • 6.1.1. Large Scale Adoption of SON Technology: 2015 - 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 and Specialist Small Cell OEMs
    • 6.4.3. WiFi Access Point OEMs
    • 6.4.4. DAS & Repeater Solution Providers
    • 6.4.5. C-RAN Solution Providers
    • 6.4.6. Other Technology & Network Component Providers/Enablers
  • 6.5. Mobile Backhaul & Fronthaul Ecosystem
    • 6.5.1. Backhaul & Fronthaul Solution Providers
  • 6.6. Mobile Core Ecosystem
    • 6.6.1. Core Network Infrastructure & Software Providers
  • 6.7. Connectivity Ecosystem
    • 6.7.1. 2G, 3G & 4G Wireless Carriers
    • 6.7.2. WiFi Connectivity Providers
    • 6.7.3. SCaaS (Small Cells as a Service) Providers
  • 6.8. SON & Mobile Network Optimization Ecosystem
    • 6.8.1. SON Solution Providers
    • 6.8.2. Mobile Network Optimization Solution Providers
  • 6.9. SDN & NFV Ecosystem
    • 6.9.1. SDN & NFV Providers

Chapter 7: Vendor Landscape

  • 7.1. Accedian Networks
  • 7.2. Accuver
  • 7.3. AirHop Communications
  • 7.4. Airspan Networks
  • 7.5. Alcatel-Lucent
  • 7.6. Amdocs
  • 7.7. Anite
  • 7.8. Arcadyan
  • 7.9. Argela
  • 7.10. Aricent
  • 7.11. ARItel
  • 7.12. Ascom
  • 7.13. Astellia
  • 7.14. ATDI
  • 7.15. Avago Technologies
  • 7.16. Avvasi
  • 7.17. BLiNQ Networks
  • 7.18. Cavium
  • 7.19. CBNL (Cambridge Broadband Networks Limited)
  • 7.20. CellMining
  • 7.21. Cellwize
  • 7.22. Celtro Communications
  • 7.23. CENTRI
  • 7.24. Cisco Systems
  • 7.25. Citrix Systems
  • 7.26. Comarch
  • 7.27. CommAgility
  • 7.28. Commsquare
  • 7.29. Coriant
  • 7.30. Datang Mobile
  • 7.31. ECE (European Communications Engineering)
  • 7.32. Ericsson
  • 7.33. Flash Networks
  • 7.34. Forsk
  • 7.35. Fujitsu
  • 7.36. Guavus
  • 7.37. Hitachi
  • 7.38. Huawei
  • 7.39. InfoVista
  • 7.40. Intel Corporation
  • 7.41. InterDigital
  • 7.42. ip.access
  • 7.43. Lavastorm
  • 7.44. Lemko Corporation
  • 7.45. NEC Corporation
  • 7.46. Nokia Networks
  • 7.47. NXP Semiconductors
  • 7.48. Optulink
  • 7.49. P.I.Works
  • 7.50. Plano Engineering
  • 7.51. Qualcomm
  • 7.52. RADCOM
  • 7.53. Radisys Corporation
  • 7.54. Reverb Networks
  • 7.55. Rohde & Schwarz
  • 7.56. Rorotika
  • 7.57. Samsung Electronics
  • 7.58. SEDICOM
  • 7.59. Siklu Communication
  • 7.60. SpiderCloud Wireless
  • 7.61. Tarana Wireless
  • 7.62. Tektronix Communications
  • 7.63. TEOCO
  • 7.64. Theta Networks
  • 7.65. TI (Texas Instruments)
  • 7.66. TTG International
  • 7.67. Tulinx
  • 7.68. Vasona Networks
  • 7.69. Viavi Solutions
  • 7.70. WebRadar
  • 7.71. XCellAir
  • 7.72. ZTE

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. SON in RAN
    • 8.3.2. SON in Mobile Core
    • 8.3.3. SON in Mobile Backhaul
  • 8.4. SON Revenue by Architecture: Centralized vs. Distributed
    • 8.4.1. C-SON
    • 8.4.2. D-SON
  • 8.5. SON Revenue by Wireless Network Generation: 2G/3G vs. 4G & Beyond
    • 8.5.1. 2G & 3G SON
    • 8.5.2. 4G & Beyond SON
  • 8.6. SON Revenue by Region
  • 8.7. Conventional Mobile Network Planning & Optimization Revenue
  • 8.8. Conventional Mobile Network Planning & Optimization Revenue by Region
  • 8.9. Asia Pacific
    • 8.9.1. SON
    • 8.9.2. Conventional Mobile Network Planning & Optimization
  • 8.10. Eastern Europe
    • 8.10.1. SON
    • 8.10.2. Conventional Mobile Network Planning & Optimization
  • 8.11. Latin & Central America
    • 8.11.1. SON
    • 8.11.2. Conventional Mobile Network Planning & Optimization
  • 8.12. Middle East & Africa
    • 8.12.1. SON
    • 8.12.2. Conventional Mobile Network Planning & Optimization
  • 8.13. North America
    • 8.13.1. SON
    • 8.13.2. Conventional Mobile Network Planning & Optimization
  • 8.14. Western Europe
    • 8.14.1. SON
    • 8.14.2. Conventional Mobile Network Planning & Optimization
  • 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: 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. SON Associated OpEx Savings: The Numbers
  • 9.9. What SON Capabilities Will 5G Networks Entail?
  • 9.10. The C-SON Versus D-SON Debate
  • 9.11. Strategic Recommendations
    • 9.11.1. SON & Conventional Mobile Network Optimization Solution Providers
    • 9.11.2. Wireless Infrastructure OEMs
    • 9.11.3. Mobile Operators

List of Figures

  • Figure 1: Functional Areas of SON with the Mobile Network Lifecycle
  • Figure 2: Annual Global Throughput of Mobile Network Data Traffic by Region: 2015 - 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: 2015 - 2030 (%)
  • Figure 5: SON Associated OpEx & CapEx Savings by Network Segment
  • Figure 6: Potential Areas of SON Implementation
  • Figure 7: Mobile Backhaul & Transport Segmentation by Technology
  • Figure 8: C-SON (Centralized SON) in a Wireless Carrier Network
  • Figure 9: D-SON (Distributed SON) in a Wireless Carrier Network
  • Figure 10: H-SON (Hybrid SON) in a Wireless Carrier Network
  • Figure 11: NGNM SON Use Cases
  • Figure 12: SON Industry Roadmap: 2015 - 2030
  • Figure 13: The Wireless Network Infrastructure Value Chain
  • Figure 14: List of LTE Trials & Deployments
  • Figure 15: Global SON & Mobile Network Optimization Revenue: 2015 - 2030 ($ Million)
  • Figure 16: Global SON Revenue: 2015 - 2030 ($ Million)
  • Figure 17: Global SON Revenue by Network Segment: 2015 - 2030 ($ Million)
  • Figure 18: Global RAN SON Revenue: 2015 - 2030 ($ Million)
  • Figure 19: Global Mobile Core SON Revenue: 2015 - 2030 ($ Million)
  • Figure 20: Global Mobile Backhaul & Transport SON Revenue: 2015 - 2030 ($ Million)
  • Figure 21: Global SON Revenue by Architecture: 2015 - 2030 ($ Million)
  • Figure 22: Global C-SON Revenue: 2015 - 2030 ($ Million)
  • Figure 23: Global D-SON Revenue: 2015 - 2030 ($ Million)
  • Figure 24: Global SON Revenue by Wireless Network Generation: 2015 - 2030 ($ Million)
  • Figure 25: Global 2G & 3G SON Revenue: 2015 - 2030 ($ Million)
  • Figure 26: Global 4G & Beyond SON Revenue: 2015 - 2030 ($ Million)
  • Figure 27: SON Revenue by Region: 2015 - 2030 ($ Million)
  • Figure 28: Global Conventional Mobile Network Planning & Optimization Revenue: 2015 - 2030 ($ Million)
  • Figure 29: Conventional Mobile Network Planning & Optimization Revenue by Region: 2015 - 2030 ($ Million)
  • Figure 30: Asia Pacific SON Revenue: 2015 - 2030 ($ Million)
  • Figure 31: Asia Pacific Conventional Mobile Network Planning & Optimization Revenue: 2015 - 2030 ($ Million)
  • Figure 32: Eastern Europe SON Revenue: 2015 - 2030 ($ Million)
  • Figure 33: Eastern Europe Conventional Mobile Network Planning & Optimization Revenue: 2015 - 2030 ($ Million)
  • Figure 34: Latin & Central America SON Revenue: 2015 - 2030 ($ Million)
  • Figure 35: Latin & Central America Conventional Mobile Network Planning & Optimization Revenue: 2015 - 2030 ($ Million)
  • Figure 36: Middle East & Africa SON Revenue: 2015 - 2030 ($ Million)
  • Figure 37: Middle East & Africa Conventional Mobile Network Planning & Optimization Revenue: 2015 - 2030 ($ Million)
  • Figure 38: North America SON Revenue: 2015 - 2030 ($ Million)
  • Figure 39: North America Conventional Mobile Network Planning & Optimization Revenue: 2015 - 2030 ($ Million)
  • Figure 40: Western Europe SON Revenue: 2015 - 2030 ($ Million)
  • Figure 41: Western Europe Conventional Mobile Network Planning & Optimization Revenue: 2015 - 2030 ($ Million)
  • Figure 42: Australia SON Revenue: 2015 - 2030 ($ Million)
  • Figure 43: Brazil SON Revenue: 2015 - 2030 ($ Million)
  • Figure 44: Canada SON Revenue: 2015 - 2030 ($ Million)
  • Figure 45: China SON Revenue: 2015 - 2030 ($ Million)
  • Figure 46: France SON Revenue: 2015 - 2030 ($ Million)
  • Figure 47: Germany SON Revenue: 2015 - 2030 ($ Million)
  • Figure 48: India SON Revenue: 2015 - 2030 ($ Million)
  • Figure 49: Italy SON Revenue: 2015 - 2030 ($ Million)
  • Figure 50: Japan SON Revenue: 2015 - 2030 ($ Million)
  • Figure 51: Russia SON Revenue: 2015 - 2030 ($ Million)
  • Figure 52: South Korea SON Revenue: 2015 - 2030 ($ Million)
  • Figure 53: Spain SON Revenue: 2015 - 2030 ($ Million)
  • Figure 54: Taiwan SON Revenue: 2015 - 2030 ($ Million)
  • Figure 55: UK SON Revenue: 2015 - 2030 ($ Million)
  • Figure 56: USA SON Revenue: 2015 - 2030 ($ Million)
  • Figure 57: SON Associated OpEx Savings by Region: 2015 - 2030 ($ Million)
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