SON (Self-Organizing Networks) in the 5G & Open RAN Era: 2022 – 2030 – Opportunities, Challenges, Strategies & Forecasts
Report Code: KNJ1705504 No. of Pages: 443 Category: Telecom and IT
Publisher: Date of Publish:
Publisher: Date of Publish:
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. Early adopters of SON have already witnessed a multitude of benefits in the form of accelerated 5G NR and LTE RAN (Radio Access Network) 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, 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 and automate cellular RAN 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 AI (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, with the cellular industry's ongoing shift towards open interfaces, virtualization and software-driven networking, the SON ecosystem is progressively transitioning from the traditional D-SON (Distributed SON) and C-SON (Centralized SON) approach to open standards-based components supporting RAN programmability for advanced automation and intelligent control. The surging popularity of innovative Open RAN and vRAN (Virtualized RAN) architectures has reignited the traditionally niche and proprietary product-driven SON market with a host of open standards-compliant RIC (RAN Intelligent Controller), xApp and rApp offerings, which are capable of supporting both near real-time D-SON and non real-time C-SON capabilities for RAN automation and optimization needs. SNS Telecom & IT estimates that global spending on RIC platforms, xApps and rApps will reach $120 Million in 2023 as initial implementations move from field trials to production-grade deployments. With commercial maturity, the submarket is further expected to quintuple to nearly $600 Million by the end of 2025. Annual investments in the wider SON market – which includes licensing of embedded D-SON features, third party C-SON functions and associated OSS platforms, in-house SON capabilities internally developed by mobile operators, and SON-related professional services across the RAN, mobile core and transport domains – are expected to grow at a CAGR of approximately 7% during the same period. The “SON (Self-Organizing Networks) in the 5G & Open RAN Era: 2022 – 2030 – Opportunities, Challenges, Strategies & Forecasts” report presents a detailed assessment of the SON market, including the value chain, market drivers, barriers to uptake, enabling technologies, functional areas, use cases, key trends, future roadmap, standardization, case studies, ecosystem player profiles and strategies. The report also provides global and regional market size forecasts for both SON and conventional mobile network optimization from 2022 till 2030, including submarket projections for three network segments, six SON architecture categories, four access technologies and five regional submarkets. 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: - Introduction to SON - Value chain and ecosystem structure - Market drivers and challenges - SON technology, architecture and functional areas - D-SON (Distributed SON), C-SON (Centralized SON), H-SON (Hybrid SON), RIC (RAN Intelligent Controller), xApps and rApps - Review of over 40 SON use cases across the RAN, core and transport domains, ranging from ANR (Automatic Neighbor Relations) and rapid equipment configuration to advanced traffic steering, QoE-based optimization and automated anomaly detection - Key trends in next-generation 5G SON implementations, including Open RAN and vRAN (Virtualized RAN) architectures, dynamic spectrum management, network slicing, edge computing, Big Data, advanced analytics, AI (Artificial Intelligence)/ML (Machine Learning) and zero-touch automation - Case studies of 20 commercial-scale SON deployments and examination of ongoing projects covering both traditional D-SON/C-SON and RIC-x/rApp approaches - Future roadmap for the SON market - Standardization, regulatory and collaborative initiatives - Profiles and strategies of more than 230 ecosystem players - Strategic recommendations for SON solution providers and mobile operators - Market analysis and forecasts from 2022 till 2030 Forecast Segmentation Market forecasts are provided for each of the following submarkets and their subcategories: SON & Mobile Network Optimization - SON - Conventional Mobile Network Planning & Optimization SON Network Segment Submarkets - RAN (Radio Access Network) - Mobile Core - Transport (Fronthaul, Midhaul & Backhaul) RAN Segment SON Architecture Submarkets - Traditional D-SON & C-SON ○ Embedded D-SON (Distributed SON) Features ○ Third Party C-SON (Centralized SON) & OSS Platforms - Open RAN RIC, xApps & rApps ○ RIC (RAN Intelligent Controller) Platforms ○ Near Real-Time xApps ○ Non Real-Time rApps - Mobile Operators' In-House SON Tools & Systems SON Access Network Technology Submarkets - 2G & 3G - LTE - 5G NR - Wi-Fi & Others Regional Markets - North America - Asia Pacific - Europe - Middle East & Africa - Latin & Central America Key Questions Answered The report provides answers to the following key questions: - How big is the SON opportunity? - What trends, drivers and challenges are influencing its growth? - What will the market size be in 2025, and at what rate will it grow? - Which submarkets and regions will see the highest percentage of growth? - How do SON investments compare with spending on conventional 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 D-SON and C-SON adoption worldwide? - When will open standards-based RIC platforms, xApps and rApps replace the traditional SON approach? - What are the prospects of AI/ML-driven automation in the SON market? - What opportunities exist for SON capabilities in the mobile core and transport network domains? - How can SON ease the deployment of private 4G/5G networks for enterprises and vertical industries? - In what way will SON facilitate network slicing and other advanced 5G capabilities? - How does SON impact mobile network optimization engineers? - 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: - The surging popularity of innovative Open RAN and vRAN (Virtualized RAN) architectures has reignited the traditionally niche and proprietary product-driven SON market with a host of open standards-compliant RIC (RAN Intelligent Controller), xApp and rApp offerings, which are capable of supporting both near real-time D-SON and non real-time C-SON capabilities for RAN automation and optimization needs. - SNS Telecom & IT estimates that global spending on RIC platforms, xApps and rApps will reach $120 Million in 2023 as initial implementations move from field trials to production-grade deployments. With commercial maturity, the submarket is further expected to quintuple to nearly $600 Million by the end of 2025. - Annual investments in the wider SON market – which includes licensing of embedded D-SON features, third party C-SON functions and associated OSS platforms, in-house SON capabilities internally developed by mobile operators, and SON-related professional services across the RAN, mobile core and transport domains – are expected to grow at a CAGR of approximately 7% during the same period. - The third party SON vendor ecosystem is exhibiting signs of consolidation, with several prominent M&A deals such as Qualcomm's recent acquisition of C-SON specialist Cellwize – in a bid to strengthen its 5G RAN infrastructure offerings, Elisa Automate's merger with Polystar to form Elisa Polystar, and HCL's acquisition of Cisco's SON technology business. - However, on the other hand, newer suppliers are also beginning to emerge – extending from VMware, Juniper Networks and other RIC platform providers to x/rApp specialists such as Cohere Technologies, DeepSig, Groundhog Technologies, Subex, B-Yond, Net AI and RIMEDO Labs. - SON capabilities are playing a pivotal role in the ongoing proliferation of private 4G/5G networks, as evident from a growing number of cross-sector partnerships. For example, private wireless service provider Betacom is collaborating with Qualcomm to accelerate enterprise adoption of private 5G networks by combining the former's 5GaaS (5G-as-a-Service) offering with the latter's enablement ecosystem, including the Cellwize RAN automation and management platform. Similarly, Germany-based systems integrator Opticoms has entered into a partnership with SON specialist Innovile to automate and optimize Open RAN standards-compliant private 5G networks. - Over the last two years, with the steep rise of mobile data consumption in residential areas during the COVID-19 pandemic-imposed lockdowns, mobile operators – despite coping relatively well – have recognized the importance of a more dynamic and automated approach to the optimization of network assets in order to provide a consistent and seamless user experience. - The 2020-2022 period saw large-scale C-SON deployments by several operators, including but not limited to Verizon, EE (BT Group), Orange, Telefónica, Turkcell, beCloud (Belarusian Cloud Technologies), VEON, Ooredoo, Zain, BTC (Botswana Telecommunications Corporation), LTT (Libya Telecom & Technology), Telstra, Singtel, Telkomsel, Globe Telecom, Smart Communications (PLDT), and Telecom Argentina.
Table of Contents
1 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
2 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 & 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 SON Value Chain
2.4.1 SON, xApp/rApp & Automation Specialists
2.4.2 OSS & RIC Platform Providers
2.4.3 RAN, Core & Transport Network Equipment Suppliers
2.4.4 Wireless Service Providers
2.4.4.1 National Mobile Operators
2.4.4.2 Fixed-Line Service Providers
2.4.4.3 Private 4G/5G Network Operators
2.4.4.4 Neutral Hosts
2.4.5 End Users
2.4.5.1 Consumers
2.4.5.2 Enterprises & Vertical Industries
2.4.6 Other Ecosystem Players
2.5 Market Drivers
2.5.1 The 5G & Open RAN Era: Continued Infrastructure Investments
2.5.2 Optimization in Complex Multi-RAN Environments
2.5.3 OpEx & CapEx Reduction: The Cost Savings Potential
2.5.4 Improving Subscriber Experience & Churn Reduction
2.5.5 Power Savings: Towards Greener Mobile Networks
2.5.6 Alleviating Congestion With Traffic Management
2.5.7 Enabling Plug & Play Deployment of Small Cells
2.5.8 Growing Adoption of Private 4G/5G Networks
2.6 Market Barriers
2.6.1 Complexity of Implementation
2.6.2 Reorganization & Changes to Standard Engineering Procedures
2.6.3 Lack of Trust in Automation
2.6.4 Proprietary SON Algorithms
2.6.5 Coordination Between Distributed & Centralized SON
2.6.6 Network Security Concerns: New Interfaces & Lack of Monitoring
3 Chapter 3: SON Technology, Implementation Architectures & Use Cases
3.1 Where Does SON Sit Within a Mobile Network?
3.1.1 RAN
3.1.2 Mobile Core
3.1.3 Transport (Fronthaul, Midhaul & Backhaul)
3.1.4 Device-Assisted SON
3.2 Traditional SON Architecture
3.2.1 D-SON (Distributed SON)
3.2.2 C-SON (Centralized SON)
3.2.3 H-SON (Hybrid SON)
3.3 Open Standards-Compliant RIC, xApps & rApps
3.3.1 RIC (RAN Intelligent Controller)
3.3.1.1 Near-RT (Real-Time) RIC
3.3.1.2 Non-RT (Real-Time) RIC
3.3.2 xApps: Open D-SON Applications
3.3.3 rApps: Open C-SON Applications
3.4 SON Use Cases
3.4.1 RAN-Centric Use Cases
3.4.1.1 ANR (Automatic Neighbor Relations)
3.4.1.2 CNR (Centralized Neighbor Relations)
3.4.1.3 PCI (Physical Cell ID) Allocation & Conflict Resolution
3.4.1.4 CCO (Coverage & Capacity Optimization)
3.4.1.5 MRO (Mobility Robustness Optimization)
3.4.1.6 MLB (Mobility Load Balancing)
3.4.1.7 RACH (Random Access Channel) Optimization
3.4.1.8 ICIC (Inter-Cell Interference Coordination) & eICIC (Enhanced ICIC)
3.4.1.9 COD/COC (Cell Outage Detection & Compensation)
3.4.1.10 MDT (Minimization of Drive Tests)
3.4.1.11 Advanced Traffic Steering
3.4.1.12 Automated Anomaly Detection
3.4.1.13 Massive MIMO & Beamforming Optimization
3.4.1.14 4G-5G Dual Connectivity Management
3.4.1.15 RAN Slice Management
3.4.1.16 DSS (Dynamic Spectrum Sharing)
3.4.1.17 Frequency Layer Management
3.4.1.18 BBU (Baseband Unit) Resource Pooling
3.4.1.19 Radio Resource Allocation for Complex Vertical Applications
3.4.1.20 Handover Management in V2X Communications Scenarios
3.4.1.21 Rapid Plug & Play Configuration of Small Cells
3.4.1.22 DAS (Distributed Antenna System) Optimization
3.4.2 Multi-Domain, Core & Transport-Related Use Cases
3.4.2.1 Self-Configuration & Testing of Network Elements
3.4.2.2 Domain Connectivity Management
3.4.2.3 Automated Inventory Checks
3.4.2.4 AIC (Automated Inconsistency Correction)
3.4.2.5 Self-Healing of Network Faults
3.4.2.6 Signaling Storm Protection
3.4.2.7 Energy Efficiency & Savings
3.4.2.8 QoS & QoE-Based Optimization
3.4.2.9 Congestion Prediction & Management
3.4.2.10 AI-Enabled Performance Diagnostics
3.4.2.11 Industrial IoT Optimization
3.4.2.12 Core Network Automation
3.4.2.13 Network Slicing Resource Allocation
3.4.2.14 Optimization of VNFs & CNFs
3.4.2.15 Auto-Provisioning of Transport Links
3.4.2.16 Transport Network Bandwidth Optimization
3.4.2.17 Wireless Transport Interference Management
3.4.2.18 Seamless Vendor Infrastructure Swap
3.4.2.19 SON Coordination Management
3.4.2.20 Cognitive & Self-Learning Networks
4 Chapter 4: Key Trends in Next-Generation SON Implementations
4.1 Open RAN & vRAN (Virtualized RAN) Architectures
4.1.1 Enabling RAN Automation & Intelligence With RIC, xApps & rApps
4.2 Small Cells, HetNets & RAN Densification
4.2.1 Plug & Play Small Cells
4.2.2 SON-Enabled Coordination of UDNs (Ultra-Dense Networks)
4.3 Shared & Unlicensed Spectrum
4.3.1 Dynamic Management of Spectrum Using SON
4.4 MEC (Multi-Access Edge Computing)
4.4.1 Potential Synergies With SON
4.5 Network Slicing
4.5.1 SON Mechanisms for Network Slicing in 5G Networks
4.6 Big Data & Advanced Analytics
4.6.1 Maximizing the Benefits of SON With Big Data
4.6.2 The Importance of Predictive & Behavioral Analytics
4.7 AI (Artificial Intelligence) & ML (Machine Learning)
4.7.1 Towards Self-Learning SON Engines
4.7.2 Deep Learning: Enabling Zero-Touch Mobile Networks
4.8 NFV (Network Functions Virtualization)
4.8.1 Enabling SON-Driven Deployment of VNFs & CNFs
4.9 SDN (Software-Defined Networking) & Programmability
4.9.1 Using the SDN Controller as a Platform for SON in Transport Networks
4.10 Cloud Computing
4.10.1 Facilitating C-SON Scalability & Elasticity
4.11 Other Trends & Complementary Technologies
4.11.1 Private 4G/5G 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
5 Chapter 5: Standardization, Regulatory & Collaborative Initiatives
5.1 3GPP (Third Generation Partnership Project)
5.1.1 3GPP Standardization of SON Capabilities
5.1.2 LTE SON Features
5.1.2.1 Release 8
5.1.2.2 Release 9
5.1.2.3 Release 10
5.1.2.4 Release 11
5.1.2.5 Release 12
5.1.2.6 Releases 13 & 14
5.1.3 5G NR SON Features
5.1.3.1 Release 15
5.1.3.2 Release 16
5.1.3.3 Release 17
5.1.3.4 Release 18 & Beyond
5.1.4 Implementation Approach for 3GPP-Specified SON Features
5.2 O-RAN Alliance
5.2.1 Open RAN RIC Architecture Specifications
5.2.2 xApp & rApp Use Cases
5.3 OSA (OpenAirInterface Software Alliance)
5.3.1 M5G (MOSAIC5G) Project: Flexible RAN & Core Controllers
5.4 TIP (Telecom Infra Project)
5.4.1 RIA (RAN Intelligence & Automation) Project
5.5 ONF (Open Networking Foundation)
5.5.1 SD-RAN Project: Near Real-Time RIC & Exemplar xApps
5.6 Linux Foundation's ONAP (Open Network Automation Platform)
5.6.1 OOF (ONAP Optimization Framework)-SON for 5G Networks
5.6.2 Interface Support for Open RAN RIC Integration
5.7 SCF (Small Cell Forum)
5.7.1 4G/5G Small Cell SON & Orchestration
5.8 OSSii (Operations Support Systems Interoperability Initiative)
5.8.1 Enabling Multi-Vendor SON Interoperability
5.9 NGMN Alliance
5.9.1 Conception of the SON Initiative
5.9.2 Recommendations for Multi-Vendor SON Deployment
5.9.3 SON Capabilities for 5G Network Deployment, Operation & Management
5.10 Others
6 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 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 BT Group
6.4.1 Vendor Selection
6.4.2 SON Deployment Review
6.4.3 Results & Future Plans
6.5 China Mobile
6.5.1 Vendor Selection
6.5.2 SON Deployment Review
6.5.3 Results & Future Plans
6.6 Elisa
6.6.1 Vendor Selection
6.6.2 SON Deployment Review
6.6.3 Results & Future Plans
6.7 Globe Telecom
6.7.1 Vendor Selection
6.7.2 SON Deployment Review
6.7.3 Results & Future Plans
6.8 KDDI Corporation
6.8.1 Vendor Selection
6.8.2 SON Deployment Review
6.8.3 Results & Future Plans
6.9 MegaFon
6.9.1 Vendor Selection
6.9.2 SON Deployment Review
6.9.3 Results & Future Plans
6.10 NTT DoCoMo
6.10.1 Vendor Selection
6.10.2 SON Deployment Review
6.10.3 Results & Future Plans
6.11 Ooredoo
6.11.1 Vendor Selection
6.11.2 SON Deployment Review
6.11.3 Results & Future Plans
6.12 Orange
6.12.1 Vendor Selection
6.12.2 SON Deployment Review
6.12.3 Results & Future Plans
6.13 Singtel
6.13.1 Vendor Selection
6.13.2 SON Deployment Review
6.13.3 Results & Future Plans
6.14 SK Telecom
6.14.1 Vendor Selection
6.14.2 SON Deployment Review
6.14.3 Results & Future Plans
6.15 Telecom Argentina
6.15.1 Vendor Selection
6.15.2 SON Deployment Review
6.15.3 Results & Future Plans
6.16 Telefónica Group
6.16.1 Vendor Selection
6.16.2 SON Deployment Review
6.16.3 Results & Future Plans
6.17 TIM (Telecom Italia Mobile)
6.17.1 Vendor Selection
6.17.2 SON Deployment Review
6.17.3 Results & Future Plans
6.18 Turkcell
6.18.1 Vendor Selection
6.18.2 SON Deployment Review
6.18.3 Results & Future Plans
6.19 Verizon Communications
6.19.1 Vendor Selection
6.19.2 SON Deployment Review
6.19.3 Results & Future Plans
6.20 Vodafone Group
6.20.1 Vendor Selection
6.20.2 SON Deployment Review
6.20.3 Results & Future Plans
6.21 Other Recent Deployments & Ongoing Projects
6.21.1 beCloud (Belarusian Cloud Technologies): AI-Enabled Network Automation & Performance Management
6.21.2 Beeline Russia: Transforming the Mobile Experience Using C-SON Technology
6.21.3 Betacom: Accelerating Enterprise Private 5G Adoption With RAN Automation
6.21.4 BTC (Botswana Telecommunications Corporation): SON for Nationwide Network Optimization
6.21.5 Celona: Self-Organizing 5G LAN Solution for Enterprises
6.21.6 América Móvil: Accelerating 5G Rollouts Through SON-Based Automation
6.21.7 DISH Network Corporation: RIC-Based Custom RAN Programmability & Intelligence
6.21.8 DT (Deutsche Telekom): Berlin SD-RAN 4G/5G Outdoor Field Trial
6.21.9 KPN: SON-Driven Automation for Network Optimization
6.21.10 Kyivstar: Leveraging C-SON to Enhance Network Performance
6.21.11 Liberty Global: Building a Customer-First Network
6.21.12 LTT (Libya Telecom & Technology): Nationwide RAN Automation
6.21.13 NEC Corporation: Self-Learning Local 5G Networks
6.21.14 Opticoms: Optimizing Open RAN-Compliant Private 5G Networks
6.21.15 Rakuten Mobile: Embedded RIC for RAN Automation Applications
6.21.16 Smart Communications (PLDT): Enabling Multi-Vendor 4G/5G Network Automation
6.21.17 Smartfren: Facilitating Network Densification & HetNet Management With C-SON Technology
6.21.18 STC (Saudi Telecom Company): Automating Network Operations & Driving 5G Transformation
6.21.19 Telkomsel: SON-Enabled Automated Network Optimization
6.21.20 Telstra: Boosting Mobile Network Automation
6.21.21 Zain Group: SON for Performance Enhancement
7 Chapter 7: Key Ecosystem Players
7.1 Aarna Networks
7.2 Abside Networks
7.3 Accedian
7.4 Accelleran
7.5 Accuver (InnoWireless)
7.6 Actiontec Electronics
7.7 AI-LINK
7.8 AirHop Communications
7.9 Airspan Networks
7.10 AiVader
7.11 Aliniant
7.12 Allot
7.13 Alpha Networks
7.14 Altiostar (Rakuten Symphony)
7.15 Amazon/AWS (Amazon Web Services)
7.16 Amdocs
7.17 Anktion (Fujian) Technology
7.18 Anritsu
7.19 Arcadyan Technology Corporation (Compal Electronics)
7.20 Argela
7.21 Aria Networks
7.22 ArrayComm (Chengdu ArrayComm Wireless Technologies)
7.23 Artemis Networks
7.24 Artiza Networks
7.25 Arukona
7.26 Askey Computer Corporation (ASUS – ASUSTeK Computer)
7.27 ASOCS
7.28 Aspire Technology (NEC Corporation)
7.29 ASTRI (Hong Kong Applied Science and Technology Research Institute)
7.30 ATDI
7.31 Atesio
7.32 Atrinet
7.33 Aurora Insight
7.34 Aviat Networks
7.35 Azcom Technology
7.36 Baicells
7.37 BandwidthX
7.38 BLiNQ Networks (CCI – Communication Components Inc.)
7.39 Blu Wireless
7.40 Blue Danube Systems (NEC Corporation)
7.41 BTI Wireless
7.42 B-Yond
7.43 CableFree (Wireless Excellence)
7.44 Cambium Networks
7.45 Capgemini Engineering
7.46 Casa Systems
7.47 CBNG (Cambridge Broadband Networks Group)
7.48 CCS – Cambridge Communication Systems (ADTRAN)
7.49 Celfinet (Cyient)
7.50 CellOnyx
7.51 Cellwize (Qualcomm)
7.52 CelPlan Technologies
7.53 CGI
7.54 Chengdu NTS
7.55 CICT – China Information and Communication Technology Group (China Xinke Group)
7.56 Ciena Corporation
7.57 CIG (Cambridge Industries Group)
7.58 Cisco Systems
7.59 Cohere Technologies
7.60 Comarch
7.61 Comba Telecom
7.62 CommAgility (Wireless Telecom Group)
7.63 CommScope
7.64 COMSovereign
7.65 Contela
7.66 Continual
7.67 Corning
7.68 Creanord
7.69 DeepSig
7.70 Dell Technologies
7.71 DGS (Digital Global Systems)
7.72 Digitata
7.73 D-Link Corporation
7.74 DZS
7.75 ECE (European Communications Engineering)
7.76 EDX Wireless
7.77 eino
7.78 Elisa Polystar
7.79 Equiendo
7.80 Ericsson
7.81 Errigal
7.82 ETRI (Electronics & Telecommunications Research Institute, South Korea)
7.83 EXFO
7.84 Fairspectrum
7.85 Federated Wireless
7.86 Flash Networks
7.87 Forsk
7.88 Foxconn (Hon Hai Technology Group)
7.89 Fraunhofer HHI (Heinrich Hertz Institute)
7.90 Fujitsu
7.91 Gemtek Technology
7.92 GENEViSiO (QNAP Systems)
7.93 GenXComm
7.94 Gigamon
7.95 GigaTera Communications (KMW)
7.96 Google (Alphabet)
7.97 Groundhog Technologies
7.98 Guavus (Thales)
7.99 HCL Technologies
7.100 Helios (Fujian Helios Technologies)
7.101 HFR Networks
7.102 Highstreet Technologies
7.103 Hitachi
7.104 HPE (Hewlett Packard Enterprise)
7.105 HSC (Hughes Systique Corporation)
7.106 Huawei
7.107 iBwave Solutions
7.108 iConNext
7.109 Infinera
7.110 Infosys
7.111 InfoVista
7.112 Inmanta
7.113 Innovile
7.114 InnoWireless
7.115 Intel Corporation
7.116 InterDigital
7.117 Intracom Telecom
7.118 Inventec Corporation
7.119 ISCO International
7.120 IS-Wireless
7.121 ITRI (Industrial Technology Research Institute, Taiwan)
7.122 JMA Wireless
7.123 JRC (Japan Radio Company)
7.124 Juniper Networks
7.125 Key Bridge Wireless
7.126 Keysight Technologies
7.127 Kleos
7.128 KMW
7.129 Kumu Networks
7.130 Lemko Corporation
7.131 Lenovo
7.132 Lextrum (COMSovereign)
7.133 Lime Microsystems
7.134 LIONS Technology
7.135 LITE-ON Technology Corporation
7.136 LS telcom
7.137 LuxCarta
7.138 MantisNet
7.139 Marvell Technology
7.140 Mavenir
7.141 Meta Connectivity
7.142 MicroNova
7.143 Microsoft Corporation
7.144 MikroTik
7.145 MitraStar Technology (Unizyx Holding Corporation)
7.146 MYCOM OSI (Amdocs)
7.147 Nash Technologies
7.148 NEC Corporation
7.149 Net AI
7.150 Netcracker Technology (NEC Corporation)
7.151 NETSCOUT Systems
7.152 Netsia (Argela)
7.153 New H3C Technologies (Tsinghua Unigroup)
7.154 New Postcom Equipment
7.155 Nextivity
7.156 Node-H
7.157 Nokia
7.158 NuRAN Wireless
7.159 NXP Semiconductors
7.160 Oceus Networks
7.161 Omnitele
7.162 Opanga Networks
7.163 Openet (Amdocs)
7.164 P.I. Works
7.165 Parallel Wireless
7.166 Phluido
7.167 Picocom
7.168 Pivotal Commware
7.169 Polte
7.170 Potevio (CETC – China Electronics Technology Group Corporation)
7.171 Qualcomm
7.172 Quanta Computer
7.173 Qucell Networks (InnoWireless)
7.174 RADCOM
7.175 Radisys (Reliance Industries)
7.176 Rakuten Symphony
7.177 Ranplan Wireless
7.178 Red Hat (IBM)
7.179 RED Technologies
7.180 RIMEDO Labs
7.181 Rivada Networks
7.182 Rohde & Schwarz
7.183 Ruijie Networks
7.184 RunEL
7.185 SageRAN (Guangzhou SageRAN Technology)
7.186 Saguna Networks (COMSovereign)
7.187 Samji Electronics
7.188 Samsung
7.189 Sandvine
7.190 Sercomm Corporation
7.191 Signalwing
7.192 Siklu
7.193 SIRADEL
7.194 Skyvera (TelcoDR)
7.195 SOLiD
7.196 Sooktha
7.197 Spectrum Effect
7.198 SSC (Shared Spectrum Company)
7.199 Star Solutions
7.200 STL (Sterlite Technologies Ltd.)
7.201 Subex
7.202 Sunwave Communications
7.203 Systemics-PAB
7.204 T&W (Shenzhen Gongjin Electronics)
7.205 Tarana Wireless
7.206 TCS (Tata Consultancy Services)
7.207 Tech Mahindra
7.208 Tecore Networks
7.209 Telrad Networks
7.210 TEOCO
7.211 ThinkRF
7.212 TI (Texas Instruments)
7.213 TietoEVRY
7.214 Trópico (CPQD – Center for Research and Development in Telecommunications, Brazil)
7.215 TTG International
7.216 Tupl
7.217 ULAK Communication
7.218 Vavitel (Shenzhen Vavitel Technology)
7.219 VHT (Viettel High Tech)
7.220 VIAVI Solutions
7.221 VMware
7.222 VNC – Virtual NetCom (COMSovereign)
7.223 VNL – Vihaan Networks Limited (Shyam Group)
7.224 WDNA (Wireless DNA)
7.225 WebRadar
7.226 Wind River Systems
7.227 Wipro
7.228 Wiwynn (Wistron Corporation)
7.229 WNC (Wistron NeWeb Corporation)
7.230 XCOM Labs
7.231 Xingtera
7.232 ZaiNar
7.233 Z-Com
7.234 Zeetta Networks
7.235 ZTE
7.236 Zyxel (Unizyx Holding Corporation)
8 Chapter 8: Market Sizing & Forecasts
8.1 SON & Mobile Network Optimization Revenue
8.2 SON Revenue
8.3 SON Revenue by Network Segment
8.3.1 RAN
8.3.2 Mobile Core
8.3.3 Transport (Fronthaul, Midhaul & Backhaul)
8.4 RAN Segment SON Revenue by Architecture: Traditional SON vs. Open RAN RIC, xApps & rApps
8.4.1 Traditional D-SON & C-SON
8.4.1.1 Embedded D-SON Features
8.4.1.2 Third Party C-SON & OSS Platforms
8.4.2 Open RAN RIC, xApps & rApps
8.4.2.1 RIC Platforms
8.4.2.2 Near Real-Time xApps
8.4.2.3 Non Real-Time rApps
8.4.3 Mobile Operators' In-House SON Tools & Systems
8.5 SON Revenue by Access Network Technology
8.5.1 2G & 3G
8.5.2 LTE
8.5.3 5G NR
8.5.4 Wi-Fi & Others
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 North America
8.9.1 SON
8.9.2 Conventional Mobile Network Planning & Optimization
8.10 Asia Pacific
8.10.1 SON
8.10.2 Conventional Mobile Network Planning & Optimization
8.11 Europe
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 Latin & Central America
8.13.1 SON
8.13.2 Conventional Mobile Network Planning & Optimization
9 Chapter 9: Conclusion & Strategic Recommendations
9.1 Why is the Market Poised to Grow?
9.2 Future Roadmap: 2022 – 2030
9.2.1 2022 – 2025: Transition From Traditional SON to RIC Platforms, xApps & rApps
9.2.2 2026 – 2029: Commercial Maturity of Advanced AI/ML-Based SON Implementations
9.2.3 2030 & Beyond: Towards Zero-Touch 5G & 6G Network Automation
9.3 Competitive Industry Landscape: Acquisitions, Alliances & Consolidation
9.4 The C-SON Versus D-SON Debate
9.5 Evaluating the Practical Benefits of SON
9.6 Prospects of Open RAN Standards-Compliant RIC Platforms, xApps & rApps
9.7 End-to-End SON: From the RAN to the Core & Transport Domains
9.8 Growing Adoption of SON Capabilities for Wi-Fi & Non-3GPP Access Technologies
9.9 The Importance of AI & ML-Driven SON Algorithms
9.10 Improving End User Experience With QoE-Based Optimization
9.11 Enabling Network Slicing & Advanced 5G Capabilities
9.12 Greater Focus on Self-Protection
9.13 Addressing IoT Optimization
9.14 Managing Shared & Unlicensed Spectrum
9.15 Easing the Deployment of Private 4G/5G Networks
9.16 Assessing the Impact of SON on Optimization & Field Engineers
9.17 Strategic Recommendations
9.17.1 SON Solution Providers
9.17.2 Mobile Operators NEED Help?
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