RAN Automation, SON, RIC, xApps & rApps in the 5G Era: 2024

RAN Automation, SON, RIC, xApps & rApps in the 5G Era: 2024 – 2030 – Opportunities, Challenges, Strategies & Forecasts

Release Date: September 2024

Number of Pages: 580

Number of Tables and Figures: 47

Synopsis

Automation of the RAN (Radio Access Network) – the most expensive, technically complex and power-intensive part of cellular infrastructure – is a key aspect of mobile operators' digital transformation strategies aimed at reducing their TCO (Total Cost of Ownership), improving network quality and achieving revenue generation targets. In conjunction with AI (Artificial Intelligence) and ML (Machine Learning), RAN automation has the potential to significantly transform mobile network economics by reducing the OpEx (Operating Expenditure)-to-revenue ratio, minimizing energy consumption, lowering CO2 (Carbon Dioxide) emissions, deferring avoidable CapEx (Captial Expenditure), optimizing performance, improving user experience and enabling new services.

The RAN automation market traces its origins to the beginning of the LTE era when SON (Self-Organizing Network) technology was introduced to reduce cellular network complexity through self-configuration, self-optimization and self-healing. While embedded D-SON (Distributed SON) capabilities such as ANR (Automatic Neighbor Relations) have become a standard feature in RAN products, C-SON (Centralized SON) solutions that abstract control from edge nodes for network-wide actions have been adopted by less than a third of world's approximately 800 national mobile operators due to constraints associated with multi-vendor interoperability, scalability and latency.

These shortcomings, together with the cellular industry's shift towards open interfaces, common information models, virtualization and software-driven networking, are driving a transition from the traditional D-SON and C-SON approach to Open RAN automation with standards-based components – specifically the Near-RT (Real-Time) and Non-RT RICs (RAN Intelligent Controllers), SMO (Service Management & Orchestration) framework, xApps (Extended Applications) and rApps (RAN Applications) – that enable greater levels of RAN programmability and automation.

Along with the ongoing SON to RIC transition, RAN automation use cases have also evolved over the last decade. For example, relatively basic MLB (Mobility Load Balancing) capabilities have metamorphosed into more sophisticated policy-guided traffic steering applications that utilize AI/ML-driven optimization algorithms to efficiently adapt to peaks and troughs in network load and service usage by dynamically managing and redistributing traffic across radio resources and frequency layers.

Due to the much higher density of radios and cell sites in the 5G era, energy efficiency has emerged as one of the most prioritized use cases of RAN automation as forward-thinking mobile operators push ahead with sustainability initiatives to reduce energy consumption, carbon emissions and operating costs without degrading network quality. Some of the other use cases that have garnered considerable interest from the operator community include network slicing enablement, application-aware optimization and anomaly detection.

While the benefits of SON-based RAN automation in live networks are well-known, expectations are even higher with the RIC, SMO and x/rApps approach. For example, Japanese brownfield operator NTT DoCoMo expects to lower its TCO by up to 30% and decrease power consumption at base stations by as much as 50% using Open RAN automation. It is worth highlighting that domestic rival Rakuten Mobile has already achieved approximately 17% energy savings per cell in its live network using RIC-hosted RAN automation applications. Following successful lab trials, the greenfield operator aims to increase savings to 25% with more sophisticated AI/ML models.

Although Open RAN automation efforts seemingly lost momentum beyond the field trial phase for the past couple of years, several commercial engagements have emerged since then, with much of the initial focus on the SMO, Non-RT RIC and rApps for automated management and optimization across Open RAN, purpose-built and hybrid RAN environments. Within the framework of its five-year $14 Billion Open RAN infrastructure contract with Ericsson, AT&T is adopting the Swedish telecommunications giant's SMO and Non-RT RIC solution to replace two legacy C-SON systems. In neighboring Canada, Telus has also initiated the implementation of an SMO and RIC platform along with its multi-vendor Open RAN deployment to transform up to 50% of its RAN footprint and swap out Huawei equipment from its 4G/5G network.

Similar efforts are also underway in other regions. For example, in Europe, Swisscom is deploying an SMO and Non-RT RIC platform to provide multi-technology network management and automation capabilities as part of a wider effort to future-proof its brownfield mobile network, while Deutsche Telekom is progressing with plans to develop its own vendor-independent SMO framework. Open RAN automation is also expected to be introduced as part of Vodafone Group's global tender for refreshing 170,000 cell sites.

Deployments of newer generations of proprietary SON-based RAN automation solutions have not stalled either. In its pursuit of achieving L4 (Highly Autonomous Network) operations, China Mobile has recently initiated the implementation of a hierarchical RAN automation platform and an associated digital twin system, starting with China's Henan province. Among other interesting examples, SoftBank is implementing a closed loop automation solution for cluster-wide RAN optimization in stadiums, event venues, and other strategic locations across Japan, which supports data collection and parameter tuning in 1-5 minute intervals as opposed to the 15-minute control cycle of traditional C-SON systems. It should be noted that the Japanese operator eventually plans to adopt RIC-hosted centralized RAN optimization applications in the future.

In addition, with the support of several mobile operators, including SoftBank, Vodafone, Bell Canada and Viettel, the idea of hosting third party applications for real-time intelligent control and optimization – also referred to as dApps (Distributed Applications) – directly within RAN baseband platforms is beginning to gain traction. As a counterbalance to this approach, Ericsson, Nokia, Huawei and other established RAN vendors are making considerable progress with a stepwise approach towards embedding AI and ML functionalities deeper into their DU (Distributed Unit) and CU (Centralized Unit) products in line with the 3GPP's long-term vision of an AI/ML-based air interface in the 6G era.

SNS Telecom & IT estimates that global spending on RIC, SMO and x/rApps will grow at a CAGR of more than 125% between 2024 and 2027 alongside the second wave of Open RAN infrastructure rollouts by brownfield operators. The Open RAN automation market will eventually account for nearly $700 Million in annual investments by the end of 2027 as standardization gaps and technical challenges in terms of the SMO-to-Non-RT RIC interface, application portability across RIC platforms and conflict mitigation between x/rApps are ironed out. The wider RAN automation software and services market – which includes Open RAN automation, RAN vendor SON solutions, third party C-SON platforms, baseband-integrated intelligent RAN applications, RAN planning and optimization software, and test/measurement solutions – is expected to grow at a CAGR of approximately 8% during the same period.

The “RAN Automation, SON, RIC, xApps & rApps in the 5G Era: 2024 – 2030 – Opportunities, Challenges, Strategies & Forecasts” report presents an in-depth assessment of the RAN automation 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 RAN and end-to-end mobile network automation from 2024 to 2030. The forecasts cover three network domains, nine functional areas, three access technologies and five regional markets.

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

Pricing

The report is available for the following price:

Single User License: USD 2,500
Company Wide License: USD 3,500

Purchase/Sample Request

To request a sample or to purchase the report, please contact info@snstelecom.com

Key Findings

The report has the following key findings:

SNS Telecom & IT estimates that global spending on RIC, SMO and x/rApps will grow at a CAGR of more than 125% between 2024 and 2027 alongside the second wave of Open RAN infrastructure rollouts by brownfield operators. The Open RAN automation market will eventually account for nearly $700 Million in annual investments by the end of 2027 as standardization gaps and technical challenges in terms of the SMO-to-Non-RT RIC interface, application portability across RIC platforms and conflict mitigation between x/rApps are ironed out.
The wider market for RAN automation software and services – which includes Open RAN automation, RAN vendor SON solutions, third party C-SON platforms, baseband-integrated intelligent RAN applications, RAN planning and optimization software, and test/measurement solutions – is expected to grow at a CAGR of approximately 8% during the same period.
The shortcomings of the traditional D-SON and C-SON approach, together with the cellular industry's shift towards open interfaces, common information models, virtualization and software-driven networking, are driving a transition to Open RAN automation with standards-based components that enable greater levels of RAN programmability and automation.
The Open RAN automation movement is stimulating innovation from a diversified community of application developers. In addition to well over a dozen providers of SMO, Non-RT RIC and Near-RT RIC products, more than 50 companies are actively engaged in the development of xApps and rApps.
Some mobile operators have established dedicated business units to commoditize their RAN automation expertise. NTT DoCoMo's OREX brand and Rakuten Mobile's sister company Rakuten Symphony are two well-known cases in point. In the coming years, we also expect to see more spinoffs of academic institutes with commercial-grade Open RAN automation offerings, such as Northeastern University's zTouch Networks and TU Ilmenau's AiVader.
The SMO and RIC ecosystem is exhibiting early signs of consolidation with Broadcom's takeover of VMware and HPE's planned acquisition of Juniper Networks, although both deals have much wider ranging implications for the AI infrastructure and networking industries. Depending on the commercial success of third party RAN automation platforms, we anticipate seeing further M&A (Mergers & Acquisition) activity reminiscent of the SON boom in the previous decade.
While the benefits of SON-based RAN automation in live networks are well-known, expectations are even higher with the RIC, SMO and x/rApps approach. For example, Japanese brownfield operator NTT DoCoMo expects to lower its TCO by up to 30% and decrease power consumption at base stations by as much as 50% using Open RAN automation.
It is worth highlighting that domestic rival Rakuten Mobile has already achieved approximately 17% energy savings per cell in its live network using RIC-hosted RAN automation applications. Following successful lab trials, the greenfield operator aims to increase savings to 25% with more sophisticated AI/ML models.
Outside of public mobile operator networks, interest is also growing in vertical industries and the private wireless segment. The U.S. DOD (Department of Defense) is actively exploring the potential of RIC-hosted x/rApps to enhance the ability to detect, analyze, and mitigate a wide range of security threats in Open RAN networks for both commercial and warfighter communication scenarios. Among other examples, Taiwanese electronics manufacturer Inventec has incorporated rApps for indoor positioning and traffic steering as part of its private 5G network solution for smart factories.
Although Open RAN automation efforts seemingly lost momentum beyond the field trial phase for the past couple of years, several commercial engagements have emerged since then, with much of the initial focus on the SMO, Non-RT RIC and rApps for automated management and optimization across Open RAN, purpose-built and hybrid RAN environments.
Within the framework of its five-year $14 Billion Open RAN infrastructure contract with Ericsson, AT&T is adopting the Swedish telecommunications giant's SMO and Non-RT RIC solution to replace two legacy C-SON systems. In neighboring Canada, Telus has also initiated the implementation of an SMO and RIC platform along with its multi-vendor Open RAN deployment to transform up to 50% of its RAN footprint and swap out Huawei equipment from its 4G/5G network.
Similar efforts are also underway in other regions. For example, in Europe, Swisscom is deploying an SMO and Non-RT RIC platform to provide multi-technology network management and automation capabilities as part of a wider effort to future-proof its brownfield mobile network, while Deutsche Telekom is progressing with plans to develop its own vendor-independent SMO framework. Open RAN automation is also expected to be introduced as part of Vodafone Group's global tender for refreshing 170,000 cell sites.
Deployments of newer generations of proprietary SON-based RAN automation solutions have not stalled either. In its pursuit of achieving L4 automation, China Mobile has recently initiated the implementation of a hierarchical RAN automation platform and an associated digital twin system, starting with China's Henan province.
Among other interesting examples, SoftBank is implementing a closed loop automation solution for cluster-wide RAN optimization in stadiums, event venues, and other strategic locations across Japan, which supports data collection and parameter tuning in 1-5 minute intervals as opposed to the 15-minute control cycle of traditional C-SON systems. It should be noted that the Japanese operator eventually plans to adopt RIC-hosted centralized RAN optimization applications in the future.
In addition, with the support of several mobile operators, including SoftBank, Vodafone, Bell Canada and Viettel, the idea of hosting third party applications for real-time intelligent control and optimization – also referred to as dApps – directly within RAN baseband platforms is beginning to gain traction.
As a counterbalance to this approach, Ericsson, Nokia, Huawei and other established RAN vendors are making considerable progress with a stepwise approach towards embedding AI and ML functionalities deeper into their DU and CU products in line with the 3GPP's long-term vision of an AI/ML-based air interface in the 6G era.
Beyond AI-driven RAN performance and efficiency improvements, mobile operators, technology suppliers and other stakeholders are also setting their sights on TCO benefits and new revenue opportunities enabled by the convergence of AI and RAN, including co-hosting vRAN and AI workloads on the same underlying infrastructure to maximize asset utilization and leveraging the RAN as a platform for edge AI services.

Topics Covered

The report covers the following topics:

Introduction to RAN automation
Value chain and ecosystem structure
Market drivers and challenges
Functional areas of RAN automation
RAN automation technology and architecture, including D-SON, C-SON, H-SON, Near-RT/Non-RT RICs, SMO, x/rApps, baseband-integrated intelligent RAN applications, RAN planning and optimization software, and test & measurement solutions
Review of over 70 RAN automation use cases, ranging from ANR, PCI and RACH optimization to advanced traffic steering, QoE-based resource allocation, energy savings, network slicing, private 5G automation, anomaly detection and dynamic RAN security
Key trends in intelligent RAN implementations, including the SON-to-RIC transition, closed loop automation, intent-driven management, operational AI/ML, Gen AI, data analytics and application awareness
Cross-domain mobile network automation enablers and application scenarios across the RAN, core and xHaul transport segments of cellular infrastructure
Detailed case studies of 20 production-grade RAN automation deployments and examination of ongoing projects covering both traditional SON and Open RAN automation approaches
Future roadmap of RAN automation
Standardization and collaborative initiatives
Profiles and strategies of more than 280 ecosystem players, including RAN infrastructure vendors, SON, RIC and SMO platform providers, x/rApp developers, AI/ML technology specialists, RAN planning and optimization software suppliers, and test/measurement solution providers
Exclusive interview transcripts from 10 companies across the RAN automation value chain: AirHop Communications, Amdocs, Groundhog Technologies, Innovile, Net AI, Nokia, P.I. Works, Qualcomm, Rakuten Mobile and RIMEDO Labs
Strategic recommendations for RAN automation solution providers and mobile operators
Market analysis and forecasts from 2024 to 2030

Forecast Segmentation

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

Mobile Network Automation Submarkets
- RAN
- Mobile Core
- xHaul (Fronthaul, Midhaul & Backhaul) Transport
RAN Automation Functional Areas
- SON-Based Automation
  - RAN Vendor SON Solutions
  - Third Party C-SON Platforms
- Open RAN Automation
  - Non-RT RIC & SMO
  - Near-RT RIC
  - rApps
  - xApps
- Baseband-Integrated Intelligent RAN Applications
- RAN Planning & Optimization Software
- Test & Measurement Solutions
Access Technology Generation Submarkets
- LTE
- 5G NR
- 6G
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 RAN automation opportunity?
What trends, drivers and challenges are influencing its growth?
What will the market size be in 2027, and at what rate will it grow?
Which submarkets and regions will see the highest percentage of growth?
What are the practical and quantifiable benefits of RAN automation based on live commercial deployments?
What is the TCO reduction and cost savings potential of RAN automation?
What is the adoption status of traditional SON solutions and Open RAN specifications-compliant Near-RT RIC, Non-RT RIC, SMO, xApps and rApps?
How can brownfield operators capitalize on Open RAN automation to simplify the management and optimization of hybrid RAN environments?
In what way will automation and AI/ML facilitate network slicing, MIMO, beamforming, lower-layer optimization and other advanced RAN capabilities in the 5G era?
What are the application scenarios of operational AI/ML and Gen AI in the RAN automation market?
What opportunities exist for automation in the mobile core and xHaul transport domains?
How does RAN automation ease the deployment and operation of private 5G networks?
In what way does intelligent automation impact the role of RAN engineers?
Who are the key ecosystem players, and what are their strategies?
Which RAN automation platform and application vendors are leading the market?
What strategies should RAN automation solution providers and mobile operators adopt to remain competitive?

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

2 Chapter 2: An Overview of RAN Automation

2.1 What is RAN Automation?

2.1.1 Automating Repetitive Manual Tasks

2.1.2 RAN Analytics & Data-Driven Decision Making

2.1.3 AI (Artificial Intelligence) & ML (Machine Learning) Integration

2.1.4 SMO (Service Management & Orchestration) Frameworks

2.2 Levels of Automation in Intelligent RAN Implementations

2.2.1 L0 – Manual Operation

2.2.2 L1 – Assisted Management

2.2.3 L2 – Partial Autonomous Network

2.2.4 L3 – Conditional Autonomous Network

2.2.5 L4 – Highly Autonomous Network

2.2.6 L5 – Fully Autonomous Network

2.3 Functional Areas of RAN Automation

2.3.1 The SON (Self-Organizing Network) Concept

2.3.2 RIC (RAN Intelligent Controller), xApps & rApps

2.3.3 Native AI Capabilities in RAN Infrastructure

2.3.4 Automation-Assisted RAN Planning & Optimization

2.3.5 RAN Test & Measurement Solutions

2.4 RAN Automation Value Chain

2.4.1 Semiconductor & Enabling AI/ML Technology Specialists

2.4.2 RAN Infrastructure Vendors

2.4.3 SON, xApp/rApp & Automation Application Developers

2.4.4 RIC, SMO & OSS Platform Providers

2.4.5 RAN Planning & Optimization Software Suppliers

2.4.6 Test & Measurement Solution Providers

2.4.7 Wireless Service Providers

2.4.7.1 National Mobile Operators

2.4.7.2 Fixed-Line Service Providers

2.4.7.3 Private 5G Network Operators

2.4.7.4 Neutral Hosts

2.4.8 End Users

2.4.8.1 Consumers

2.4.8.2 Enterprises & Vertical Industries

2.5 Market Drivers

2.5.1 Growing Complexity of RAN in the 5G Era

2.5.2 Open RAN & vRAN (Virtualized RAN) Adoption

2.5.3 TCO (Total Cost of Ownership) Reduction

2.5.4 Energy Savings, Sustainability & Environmental Conservation

2.5.5 Popularity of Both Operational & Generative AI Technologies

2.5.6 Subscriber Experience & Network Performance Benefits

2.5.7 Network Slicing & New Revenue-Generating Opportunities

2.5.8 Proliferation of Shared Spectrum, Private 5G & Neutral Host Networks

2.6 Market Barriers

2.6.1 Service Provider Revenue Stagnation & Cost-Cutting Measures

2.6.2 Slow Pace of Brownfield RAN Reinvestment Cycles

2.6.3 Implementation-Related Technical Challenges

2.6.4 Standardization Gaps & Multi-Vendor Interoperability

2.6.5 Conflict Mitigation Between x/rApps

2.6.6 Dominance of Incumbent RAN Vendors

2.6.7 Conservatism & Trust in Automation

2.6.8 Network Security & Privacy Concerns

3 Chapter 3: RAN Automation Technology, Architecture & Use Cases

3.1 Traditional SON Solutions

3.1.1 Application Areas

3.1.1.1 Self-Configuration

3.1.1.2 Self-Optimization

3.1.1.3 Self-Healing

3.1.1.4 Self-Protection

3.1.1.5 Self-Learning

3.1.2 SON Architecture

3.1.2.1 D-SON (Distributed SON)

3.1.2.2 C-SON (Centralized SON)

3.1.2.3 H-SON (Hybrid SON)

3.2 Open Specifications-Based RIC, SMO, xApps & rApps

3.2.1 Architectural Elements

3.2.1.1 Near-RT (Real-Time) RIC

3.2.1.2 Non-RT RIC

3.2.1.3 SMO Framework

3.2.1.4 xApps (Extended Applications)

3.2.1.5 rApps (RAN Applications)

3.2.2 Open Interfaces

3.2.2.1 A1 Interface Between Non-RT RIC & Near-RT RIC

3.2.2.2 E2 Interface Between Near RT-RIC & RAN Nodes

3.2.2.3 O1 Interface for OAM (Operations, Administration & Maintenance)

3.2.2.4 O2 Interface for Cloud Infrastructure Management

3.2.2.5 R1 Interface for rApp Portability Across RIC Platforms

3.2.2.6 xApp APIs (Application Programming Interfaces)

3.2.2.7 Potential Decoupling of the SMO & Non-RT RIC

3.2.2.8 Open Fronthaul M-Plane Interface

3.2.2.9 Y1 Interface for RAN Analytics Exposure

3.3 AI-Native RAN Infrastructure

3.3.1 AI/ML-Based Air Interface for 6G Networks

3.3.2 Microsecond-Level Intelligent RAN Control & Optimization

3.3.3 Synergies With the dApps (Distributed Applications) Concept

3.3.4 AI-RAN Workload Sharing & RAN as a Platform for Edge AI Services

3.4 RAN Planning & Optimization

3.4.1 RAN Planning & Optimization Software Platforms

3.4.2 Specialized Products for In-Building Wireless Network Design

3.4.3 Other Categories of RAN Operations Support & Optimization Tools

3.5 Test & Measurement Solutions

3.5.1 Testing of RIC Platforms & Other RAN Automation Products

3.5.2 Automation & AI/ML Features in Test & Measurement Solutions

3.6 Automation & Intelligence Beyond the RAN

3.6.1 Mobile Core Networks

3.6.2 xHaul (Fronthaul, Midhaul & Backhaul) Transport

3.6.3 Device-Driven Intelligence & Optimization

3.7 Network Automation Use Cases

3.7.1 Neighbor Relations, PCI & RACH Optimization

3.7.1.1 ANR (Automatic Neighbor Relations)

3.7.1.2 CNR (Centralized Neighbor Relations)

3.7.1.3 PCI (Physical Cell ID) Conflict Detection & Resolution

3.7.1.4 RACH (Random Access Channel)/RSI (Root Sequence Index) Optimization

3.7.2 Mobility & Handover Management

3.7.2.1 MRO/bMRO (Cell & Beam-Based Mobility Robustness Optimization)

3.7.2.2 QoS-Based Adaptive & Intelligent Handover Optimization

3.7.2.3 CHO (Conditional Handover) Management

3.7.2.4 DAPS (Dual Active Protocol Stack) Handover Management

3.7.2.5 Handover Management for V2X, UAV & Railway Communications

3.7.3 RAN Resource Optimization

3.7.3.1 CCO (Coverage & Capacity Optimization)

3.7.3.2 AI/ML-Assisted Dynamic Cell Shaping

3.7.3.3 MLB (Mobility Load Balancing)/LBO (Load Balancing Optimization)

3.7.3.4 Advanced Traffic Steering for Efficient Load Distribution

3.7.3.5 QoS & QoE-Based Dynamic Resource Allocation

3.7.3.6 Policy-Guided QoS/QoE Nudging

3.7.3.7 Application-Aware RAN Optimization

3.7.3.8 Special Event Management

3.7.3.9 Intelligent Control in RAN Sharing Arrangements

3.7.3.10 Dynamic Reallocation of Idle RAN Compute Resources

3.7.4 Energy Efficiency & Sustainability

3.7.4.1 Energy Savings in the RAN

3.7.4.2 Dynamic Transmit Power Adaptation

3.7.4.3 Carrier & Cell On/Off Switching

3.7.4.4 RF Channel Reconfiguration: Massive MIMO Muting

3.7.4.5 Advanced Sleep Mode Control in RUs (Radio Units)

3.7.4.6 DU/CU (Distributed & Centralized Unit) Pooling & Power Management

3.7.4.7 Carbon Footprint Awareness & Emission Control

3.7.4.8 RAN-Driven Optimization of UE Energy Consumption

3.7.5 Spectrum Management & Multi-RAT Connectivity

3.7.5.1 Frequency Layer Management

3.7.5.2 Sector Carrier Orchestration

3.7.5.3 CA (Carrier Aggregation) Optimization

3.7.5.4 MCIM/ICIM (Multi/Inter-Cell Interference Management)

3.7.5.5 Atmospheric Ducting Interference Mitigation

3.7.5.6 Shared & Unlicensed Spectrum Coordination

3.7.5.7 DSS (Dynamic Spectrum Sharing)

3.7.5.8 4G-5G DC (Dual Connectivity) Control

3.7.5.9 JCAS (Joint Communication & Sensing)

3.7.6 Network Healing & Protection

3.7.6.1 AD (Anomaly Detection) & Remediation

3.7.6.2 COD/COC (Cell Outage Detection & Compensation)

3.7.6.3 SCDR (Sleeping Cell Detection & Recovery)

3.7.6.4 RET (Remote Electrical Tilt) Adjustment in Disaster Scenarios

3.7.6.5 CPM (Congestion Prediction & Management)

3.7.6.6 RF Jamming Detection

3.7.6.7 Signaling Storm Protection

3.7.6.8 Closed Loop RAN Security

3.7.7 Massive MIMO, Beamforming & Lower-Layer Optimization

3.7.7.1 GoB (Grid-of-Beams) Beamforming Optimization

3.7.7.2 Non-GoB (Reciprocity-Based) Beamforming Optimization

3.7.7.3 AI/ML-Assisted Beam Selection & Management

3.7.7.4 Initial Access Optimization in Massive MIMO Systems

3.7.7.5 MU (Multi-User)-MIMO Pairing Enhancement

3.7.7.6 Massive MIMO Grouping Optimization

3.7.7.7 Channel Estimation, Interpolation & Equalization

3.7.7.8 Link Adaptation & Other L1 (PHY)/MAC Algorithms

3.7.8 Network Slicing, Private 5G, NTN & Vertical Applications

3.7.8.1 RAN Slice Resource Allocation Optimization

3.7.8.2 RAN Slice SLA (Service Level Agreement) Assurance

3.7.8.3 Multi-Vendor Slice Management

3.7.8.4 Private 5G & Neutral Host Network Automation

3.7.8.5 IIoT (Industrial IoT) & Enterprise RAN Customization

3.7.8.6 NTN (Non-Terrestrial Network) Resource Orchestration

3.7.9 Network Planning & Evolution

3.7.9.1 RF Design

3.7.9.2 Site Selection

3.7.9.3 Capacity Planning

3.7.9.4 Canary Release

3.7.9.5 Network Digital Twin

3.7.9.6 Legacy Network Shutdown

3.7.10 Automation & AI Enablement

3.7.10.1 Conflict Management & Governance

3.7.10.2 RAN Geolocation Intelligence

3.7.10.3 UE Positioning & Trajectory Prediction

3.7.10.4 KPI (Key Performance Indicator) Monitoring

3.7.10.5 MDT (Minimization of Drive Tests) & RAN Data Collection

3.7.10.6 Integration of Datasets External to the RAN

3.7.10.7 AI/ML-Enabled Network Insights & Diagnostics

3.7.10.8 Traffic Forecasting & QoS/QoE Prediction

3.7.11 Multi-Domain, Core & Transport-Related Use Cases

3.7.11.1 Automated Configuration & Testing

3.7.11.2 Dynamic Autoscaling of Network Resources

3.7.11.3 Service Assurance, Fault Management & Cybersecurity

3.7.11.4 AI/ML-Driven Intelligence for End-to-End Network Slicing

3.7.11.5 Core Network Automation & Intelligent Orchestration

3.7.11.6 NWDAF (Network Data Analytics Function) for Core Network Analytics

3.7.11.7 MDAF (Management Data Analytics Function) for Management Plane Analytics

3.7.11.8 SDN (Software-Defined Networking)-Based xHaul Transport Automation

3.7.11.9 Interference Management in Microwave & mmWave (Millimeter Wave) Transport Links

3.7.11.10 Interworking Between RAN SMO, NWDAF, MDAF & Transport Domain SDN Controllers

4 Chapter 4: Key Trends in Intelligent RAN Implementations

4.1 Transition From SON to Open RAN-Based RIC, SMO, xApps & rApps

4.1.1 AI/ML Integration From the Outset

4.1.2 Granular Insights & Faster Control Loops

4.1.3 Multi-Vendor Interoperability & Scalability

4.1.4 Diversified Ecosystem of RAN Application Developers

4.1.5 SDKs (Software Development Kits) for Accelerated Development

4.2 Moving Closer to Higher Levels of Automation

4.2.1 Building Confidence in Closed Loop Automation

4.2.2 Service-Centric Automated Network Optimization

4.2.3 Intent-Driven Network & Service Management

4.2.4 Long-Term Vision of Zero-Touch Operations

4.3 Operational AI & ML

4.3.1 Replacement of Classic Rule-Based Solutions With AI Algorithms

4.3.2 ML Models for Network Operations Automation

4.3.3 Supervised & Unsupervised Learning

4.3.4 RL (Reinforcement Learning)

4.3.5 Federated Learning

4.3.6 Deep Learning

4.4 Gen AI (Generative AI)

4.4.1 Differences From Conventional AI/ML

4.4.2 GANs (Generative Adversarial Networks)

4.4.3 VAEs (Variational Autoencoders)

4.4.4 Transformer Architecture

4.4.5 LLMs (Large Language Models)

4.4.6 Natural Language Interface for RAN Operations

4.5 Network Data Analytics

4.5.1 Descriptive Analytics

4.5.2 Diagnostic Analytics

4.5.3 Predictive Analytics

4.5.4 Prescriptive Analytics

4.6 Observability of Network Operations

4.6.1 Deeper Visibility Into RAN Telemetry

4.6.2 Integrating Supplementary Data Sources

4.6.3 End-to-End Network Observability Control

4.7 Cloud-Native & Software-Centric Networking

4.7.1 Cloud-Native Technologies

4.7.2 Microservices & SBA (Service-Based Architecture)

4.7.3 Network Virtualization & Containerization

4.7.4 SDN for Network Programmability

4.7.5 DevOps & CI/CD (Continuous Integration & Delivery)

4.8 Other Trends & Developments

4.8.1 RAN Densification & Multi-Layer Coordination

4.8.2 Plug & Play Small Cells in Industrial, Enterprise & Public Venues

4.8.3 RAN Automation for Private 5G Network Management

4.8.4 Support for Vertical Industry-Specific Use Cases

4.8.5 FWA (Fixed Wireless Access) Deployments

4.8.6 Shared & Unlicensed Spectrum

4.8.7 Network Slicing Enablement

4.8.8 AI-RAN & Edge Computing

4.8.9 Application Awareness

4.8.10 Dynamic Security

5 Chapter 5: Standardization & Collaborative Initiatives

5.1 3GPP (Third Generation Partnership Project)

5.1.1 Releases 8-14: LTE SON Features

5.1.2 Release 15: 5G ANR, NWDAF & MDAF

5.1.3 Release 16: 5G SON, MDT & L2 Measurement Support

5.1.4 Release 17: Expansion of 5G Network Intelligence & Automation

5.1.5 Release 18: Laying the AI/ML Foundation for 5G Advanced Systems

5.1.6 Releases 19, 20, 21 & Beyond: Succession From 5G Advanced to AI-Native 6G Networks

5.2 AI-RAN Alliance

5.2.1 AI for RAN

5.2.2 AI & RAN

5.2.3 AI on RAN

5.3 ETSI (European Telecommunications Standards Institute)

5.3.1 OCG AI (Operational Co-ordination Group on AI)

5.3.2 Specific ISGs (Industry Specification Groups) & TCs (Technical Committees)

5.3.2.1 ENI (Experiential Networked Intelligence) ISG

5.3.2.2 ZSM (Zero-Touch Network & Service Management) ISG

5.3.2.3 TC INT (TC on Core Network & Interoperability Testing)

5.3.2.4 TC SAI (TC on Securing Artificial Intelligence)

5.3.2.5 Other ISGs & TCs

5.4 GSMA (GSM Association)

5.4.1 Efforts Related to AI & Network Automation

5.5 GTAA (Global Telco AI Alliance)

5.5.1 Accelerating Telco AI Transformation

5.5.2 Multi-Lingual LLM for Telco Operations

5.6 IETF (Internet Engineering Task Force)

5.6.1 Standardization for Automated Network Management

5.7 ITU (International Telecommunication Union)

5.7.1 ITU-R (ITU Radiocommunication Sector)

5.7.1.1 Work Related to AI-Native Air Interface & RAN

5.7.2 ITU-T (ITU Telecommunication Standardization Sector)

5.7.2.1 SG13 (Study Group 13): Future Networks & Emerging Technologies

5.7.2.2 FG-AN (Focus Group on Autonomous Networks)

5.7.2.3 FG-ML5G (Focus Group on ML for 5G & Future Networks)

5.8 Linux Foundation

5.8.1 ONAP (Open Network Automation Platform)

5.8.2 Other AI & Network Automation-Related Projects

5.9 NGMN Alliance

5.9.1 SON Definition & Recommendations

5.9.2 Network Automation & Autonomy Based on AI

5.9.3 Green Future Networks for Energy Efficiency & Sustainability

5.10 ONF (Open Networking Foundation)

5.10.1 SMaRT-5G (Sustainable Mobile & RAN Transformation 5G)

5.10.2 SD-RAN (Software-Defined RAN): Near-RT RIC & Exemplar xApps

5.10.3 RRAIL (RAN RIC & Applications Interoperability Lab)

5.11 O-RAN Alliance

5.11.1 RIC Architecture Specifications

5.11.2 xApp & rApp Use Cases

5.11.3 O-RAN SC (Software Community)

5.11.4 Testing & Integration Support

5.12 OSA (OpenAirInterface Software Alliance)

5.12.1 M5G (MOSAIC5G): Flexible RAN & Core Controllers

5.12.2 FlexRIC (Flexible RAN Intelligent Controller) & xApp SDK Framework

5.13 OSSii (Operations Support Systems Interoperability Initiative)

5.13.1 Enabling Multi-Vendor OSS Interoperability

5.14 SCF (Small Cell Forum)

5.14.1 Small Cell SON & RAN Orchestration

5.15 TIP (Telecom Infra Project)

5.15.1 OpenRAN Project Group

5.15.1.1 RIA (RAN Intelligence & Automation) Subgroup

5.15.1.2 ROMA (RAN Orchestration & Lifecycle Management Automation) Subgroup

5.15.2 TelcoAI Project Group

5.16 TM Forum

5.16.1 Addressing Higher-Level Aspects of Autonomous Networks

5.17 Other Initiatives & Academic Research

6 Chapter 6: RAN Automation Case Studies

6.1 AT&T

6.1.1 Vendor Selection

6.1.2 Deployment Review

6.1.3 Results & Future Plans

6.2 Bell Canada

6.2.1 Vendor Selection

6.2.2 Deployment Review

6.2.3 Results & Future Plans

6.3 Bharti Airtel

6.3.1 Vendor Selection

6.3.2 Deployment Review

6.3.3 Results & Future Plans

6.4 BT Group

6.4.1 Vendor Selection

6.4.2 Deployment Review

6.4.3 Results & Future Plans

6.5 DT (Deutsche Telekom)

6.5.1 Vendor Selection

6.5.2 Deployment Review

6.5.3 Results & Future Plans

6.6 Elisa

6.6.1 Vendor Selection

6.6.2 Deployment Review

6.6.3 Results & Future Plans

6.7 Globe Telecom

6.7.1 Vendor Selection

6.7.2 Deployment Review

6.7.3 Results & Future Plans

6.8 NTT DoCoMo

6.8.1 Vendor Selection

6.8.2 Deployment Review

6.8.3 Results & Future Plans

6.9 Ooredoo

6.9.1 Vendor Selection

6.9.2 Deployment Review

6.9.3 Results & Future Plans

6.10 Orange

6.10.1 Vendor Selection

6.10.2 Deployment Review

6.10.3 Results & Future Plans

6.11 Rakuten Mobile

6.11.1 Vendor Selection

6.11.2 Deployment Review

6.11.3 Results & Future Plans

6.12 Singtel

6.12.1 Vendor Selection

6.12.2 Deployment Review

6.12.3 Results & Future Plans

6.13 SK Telecom

6.13.1 Vendor Selection

6.13.2 Deployment Review

6.13.3 Results & Future Plans

6.14 STC (Saudi Telecom Company)

6.14.1 Vendor Selection

6.14.2 Deployment Review

6.14.3 Results & Future Plans

6.15 Telecom Argentina

6.15.1 Vendor Selection

6.15.2 Deployment Review

6.15.3 Results & Future Plans

6.16 Telefónica Group

6.16.1 Vendor Selection

6.16.2 Deployment Review

6.16.3 Results & Future Plans

6.17 TIM (Telecom Italia Mobile)

6.17.1 Vendor Selection

6.17.2 Deployment Review

6.17.3 Results & Future Plans

6.18 Turkcell

6.18.1 Vendor Selection

6.18.2 Deployment Review

6.18.3 Results & Future Plans

6.19 Verizon Communications

6.19.1 Vendor Selection

6.19.2 Deployment Review

6.19.3 Results & Future Plans

6.20 Vodafone Group

6.20.1 Vendor Selection

6.20.2 Deployment Review

6.20.3 Results & Future Plans

6.21 Other Recent Deployments & Ongoing Projects

6.21.1 1&1: Highly Automated Control of Europe's First Greenfield Open RAN Network

6.21.2 4iG Group: Closed Loop Network Management & Customer Experience Monitoring

6.21.3 América Móvil: SON-Based RAN Automation for 5G Network Rollout & Optimization

6.21.4 Andorra Telecom: Doubling Throughput With Automated RF Interference Mitigation

6.21.5 Axiata Group: Autonomous Network Initiative for Streamlining Operations

6.21.6 Batelco: AI-Powered Energy Savings & Carbon Footprint Reduction

6.21.7 beCloud (Belarusian Cloud Technologies): AI-Enabled Network Management

6.21.8 Beeline Russia (VimpelCom): Transforming the Mobile Experience Using C-SON

6.21.9 BTC (Botswana Telecommunications Corporation): Nationwide Network Optimization

6.21.10 C Spire: SON-Enabled Automation of Regional Wireless Network

6.21.11 Cellfie Mobile: Intelligent RAN Monitoring & Management

6.21.12 CETIN Group: Multi-Domain Automated Network Optimization

6.21.13 China Mobile: Aiming for AI/ML-Assisted L4 Automation by 2025

6.21.14 China Telecom: Co-Governance of Shared 5G Network Infrastructure

6.21.15 China Unicom: CUBE-RAN Intelligent Open Platform

6.21.16 CK Hutchison: Accelerating the Journey Towards Fully Automated RAN Operations

6.21.17 DIGI Communications: Laying the Groundwork for Zero-Touch Automation

6.21.18 DISH Network Corporation: RIC-Based RAN Programmability & Intelligence

6.21.19 Djezzy: Harnessing C-SON for Automated RAN Optimization & Management

6.21.20 Etisalat Group (e&): AI/ML-Enabled Intelligent Network Management Platform

6.21.21 FET (Far EasTone Telecommunications): Advancing Sustainability Goals With ML-Driven RAN Automation

6.21.22 KDDI: Moving Towards RIC-Based Automation for Network Slicing Enablement

6.21.23 KPN: Replacing Labor-Intensive RAN Optimization Tools With SON-Based Automation

6.21.24 KT Corporation: Embracing Intelligent Control of RAN Resources & Operations

6.21.25 LG Uplus: Evaluating the RIC Approach for Vendor-Independent RAN Automation

6.21.26 Liberty Global: Building a Customer-First 5G Network Using Autonomous Optimization Decisions

6.21.27 LTT (Libya Telecom & Technology): Nationwide RAN Automation for Enhanced Network Quality

6.21.28 MÁSMÓVIL: Improving Customer Experience During Peak Hours With ML-Assisted Optimization

6.21.29 MegaFon: Delivering an Exemplary Subscriber Experience Through SON Technology

6.21.30 MEO (Altice Portugal): Automated RAN Optimization & Service Assurance

6.21.31 MTN Group: Pioneering Autonomous Mobile Networks in Africa

6.21.32 MTS (Mobile TeleSystems): Self-Adjusting Intelligent Network

6.21.33 Odido: AI-Driven Cell Site Energy Management Solution

6.21.34 Reliance Jio Infocomm: Improving Customer Experience With C-SON

6.21.35 Rogers Communications: Cross-Domain Service Orchestration & Automation

6.21.36 Smart Communications (PLDT): Planning the SON-to-RIC Transition

6.21.37 Smartfren: Automating Heterogenous Network Management

6.21.38 SoftBank Group: Spearheading AI/ML-Driven Advancements in the RAN

6.21.39 Swisscom: Future-Proofing Brownfield Mobile Network With SMO & Non-RT RIC

6.21.40 TDC NET: Inching Towards Net Zero Goals With RAN Automation

6.21.41 Telia Company: Setting the Foundation for Zero-Touch Mobile Networks

6.21.42 Telkomsel: Autonomous Network Program for Operational Efficiency

6.21.43 Telstra: Advancing Mobile Network Automation Capabilities

6.21.44 Telus: SMO & RIC-Based RAN Network Intelligence Platform

6.21.45 TPG Telecom: Managing Peak Traffic Congestion With C-SON

6.21.46 Türk Telekom: Driving Efficiency Through Network Automation

6.21.47 Ucom (Armenia): AI Functionalities for Mobile Network Modernization

6.21.48 VEON: Leveraging C-SON to Enhance Network Performance

6.21.49 Viettel Group: AI/ML-Enabled Physical Layer Signal Processing

6.21.50 Zain Group: Targeting L4 Automation for Efficient 5G Network Operations

7 Chapter 7: Key Ecosystem Players

7.1 A10 Networks

7.2 A5G Networks

7.3 Aalyria

7.4 Aarna Networks

7.5 Abside Networks

7.6 Accedian

7.7 Accelleran

7.8 Accuver (InnoWireless)

7.9 Acentury

7.10 Actiontec Electronics

7.11 Adtran

7.12 Aglocell

7.13 AI-LINK

7.14 Aira Technologies

7.15 AirHop Communications

7.16 Airspan Networks

7.17 AiVader

7.18 Aliniant

7.19 Allot

7.20 Alpha Networks

7.21 Amazon/AWS (Amazon Web Services)

7.22 AMD (Advanced Micro Devices)

7.23 Amdocs

7.24 Anktion (Fujian) Technology

7.25 Anritsu

7.26 Antevia Networks

7.27 Arcadyan Technology Corporation (Compal Electronics)

7.28 Argela

7.29 Arm

7.30 ArrayComm (Chengdu ArrayComm Wireless Technologies)

7.31 Arrcus

7.32 Artemis Networks

7.33 Artiza Networks

7.34 Arukona

7.35 AsiaInfo Technologies

7.36 Askey Computer Corporation (ASUS – ASUSTeK Computer)

7.37 ASOCS

7.38 Aspire Technology (NEC Corporation)

7.39 ASTRI (Hong Kong Applied Science and Technology Research Institute)

7.40 Ataya

7.41 ATDI

7.42 Atesio

7.43 Atrinet (ServiceNow)

7.44 Auray Technology (Auden Techno)

7.45 Aviat Networks

7.46 Azcom Technology

7.47 Baicells

7.48 Betacom

7.49 BLiNQ Networks (CCI – Communication Components Inc.)

7.50 Blu Wireless

7.51 Booz Allen Hamilton

7.52 BravoCom

7.53 Broadcom

7.54 BTI Wireless

7.55 BubbleRAN

7.56 B-Yond/Reailize

7.57 C3Spectra

7.58 CableFree (Wireless Excellence)

7.59 Cambium Networks

7.60 Capgemini Engineering

7.61 CBNG (Cambridge Broadband Networks Group)

7.62 Celfinet (Cyient)

7.63 Celona

7.64 CelPlan Technologies

7.65 Ceragon Networks

7.66 CGI

7.67 Chengdu NTS

7.68 CICT – China Information and Communication Technology Group (China Xinke Group)

7.69 Ciena Corporation

7.70 CIG (Cambridge Industries Group)

7.71 Cisco Systems

7.72 Clavister

7.73 Cohere Technologies

7.74 Comarch

7.75 Comba Telecom

7.76 CommAgility (E-Space)

7.77 CommScope

7.78 Compal Electronics

7.79 COMSovereign

7.80 Contela

7.81 Corning

7.82 Creanord

7.83 Cyient

7.84 DeepSig

7.85 Dell Technologies

7.86 DGS (Digital Global Systems)

7.87 Digis Squared

7.88 Digitata

7.89 D-Link Corporation

7.90 Druid Software

7.91 DZS

7.92 ECE (European Communications Engineering)

7.93 EDX Wireless

7.94 eino

7.95 Elisa Polystar

7.96 Encora

7.97 Equiendo

7.98 Ericsson

7.99 Errigal

7.100 ETRI (Electronics & Telecommunications Research Institute, South Korea)

7.101 EXFO

7.102 F5

7.103 Fairspectrum

7.104 Federated Wireless

7.105 Firecell

7.106 Flash Networks

7.107 Forsk

7.108 Fortinet

7.109 Foxconn (Hon Hai Technology Group)

7.110 Fraunhofer HHI (Heinrich Hertz Institute)

7.111 Fujitsu

7.112 FullRays (LDAS – LocationDAS)

7.113 Future Connections

7.114 FYRA

7.115 G REIGNS (HTC Corporation)

7.116 Gemtek Technology

7.117 GENEViSiO (QNAP Systems)

7.118 Gigamon

7.119 GigaTera Communications (KMW)

7.120 GlobalLogic (Hitachi)

7.121 Globalstar

7.122 Google (Alphabet)

7.123 Groundhog Technologies

7.124 Guavus (Thales)

7.125 GXC (Formerly GenXComm)

7.126 HCLTech (HCL Technologies)

7.127 Helios (Fujian Helios Technologies)

7.128 HFR Networks

7.129 Highstreet Technologies

7.130 Hitachi

7.131 HPE (Hewlett Packard Enterprise)

7.132 HSC (Hughes Systique Corporation)

7.133 Huawei

7.134 IBM

7.135 iBwave Solutions

7.136 iConNext

7.137 Infinera

7.138 Infosys

7.139 Infovista

7.140 Inmanta

7.141 Innovile

7.142 InnoWireless

7.143 Intel Corporation

7.144 InterDigital

7.145 Intracom Telecom

7.146 Inventec Corporation

7.147 ISCO International

7.148 IS-Wireless

7.149 Itential

7.150 ITRI (Industrial Technology Research Institute, Taiwan)

7.151 JMA Wireless

7.152 JRC (Japan Radio Company)

7.153 Juniper Networks (HPE – Hewlett Packard Enterprise)

7.154 Key Bridge Wireless

7.155 Keysight Technologies

7.156 Kleos

7.157 KMW

7.158 Kumu Networks

7.159 Lemko Corporation

7.160 Lenovo

7.161 Lime Microsystems

7.162 LIONS Technology

7.163 LITE-ON Technology Corporation

7.164 LitePoint (Teradyne)

7.165 LS telcom

7.166 LuxCarta

7.167 MantisNet

7.168 Marvell Technology

7.169 Mavenir

7.170 Maxar Technologies

7.171 Meta

7.172 MicroNova

7.173 Microsoft Corporation

7.174 MikroTik

7.175 MitraStar Technology (Unizyx Holding Corporation)

7.176 Mobileum

7.177 MosoLabs (Sercomm Corporation)

7.178 MYCOM OSI

7.179 Nash Technologies

7.180 NEC Corporation

7.181 Net AI

7.182 Netcracker Technology (NEC Corporation)

7.183 NETSCOUT Systems

7.184 Netsia (Argela)

7.185 Neutroon Technologies

7.186 New H3C Technologies (Tsinghua Unigroup)

7.187 New Postcom Equipment

7.188 Nextivity

7.189 Node-H

7.190 Nokia

7.191 Novowi

7.192 NuRAN Wireless

7.193 NVIDIA Corporation

7.194 NXP Semiconductors

7.195 Oceus Networks

7.196 Omnitele

7.197 OneLayer

7.198 Ookla

7.199 Opanga Networks

7.200 OREX (NTT DoCoMo)

7.201 P.I. Works

7.202 Palo Alto Networks

7.203 Parallel Wireless

7.204 Pente Networks

7.205 Phluido

7.206 Picocom

7.207 Pivotal Commware

7.208 Potevio (CETC – China Electronics Technology Group Corporation)

7.209 QCT (Quanta Cloud Technology)

7.210 Qualcomm

7.211 Quanta Computer

7.212 Qucell Networks (InnoWireless)

7.213 RADCOM

7.214 Radisys (Reliance Industries)

7.215 Radware

7.216 Rakuten Symphony

7.217 Ranlytics

7.218 Ranplan Wireless

7.219 Rebaca Technologies

7.220 Red Hat (IBM)

7.221 RED Technologies

7.222 REPLY

7.223 RIMEDO Labs

7.224 Rivada Networks

7.225 Rohde & Schwarz

7.226 Ruijie Networks

7.227 RunEL

7.228 SageRAN (Guangzhou SageRAN Technology)

7.229 Samji Electronics

7.230 Samsung

7.231 Sandvine

7.232 Sercomm Corporation

7.233 ServiceNow

7.234 Shabodi

7.235 Signalwing

7.236 SIRADEL

7.237 Skyvera (TelcoDR)

7.238 SOLiD

7.239 Sooktha

7.240 Spectrum Effect

7.241 Spirent Communications

7.242 SRS (Software Radio Systems)

7.243 SSC (Shared Spectrum Company)

7.244 Star Solutions

7.245 Subex

7.246 Sunwave Communications

7.247 Supermicro (Super Micro Computer)

7.248 SynaXG Technologies

7.249 Systemics-PAB

7.250 T&W (Shenzhen Gongjin Electronics)

7.251 Tarana Wireless

7.252 TCS (Tata Consultancy Services)

7.253 Tech Mahindra

7.254 Tecore Networks

7.255 TECTWIN

7.256 Telrad Networks

7.257 TEOCO/Aircom

7.258 ThinkRF

7.259 TI (Texas Instruments)

7.260 TietoEVRY

7.261 Trópico (CPQD – Center for Research and Development in Telecommunications, Brazil)

7.262 TTG International

7.263 Tupl

7.264 ULAK Communication

7.265 Vavitel (Shenzhen Vavitel Technology)

7.266 VHT (Viettel High Tech)

7.267 VIAVI Solutions

7.268 VMware (Broadcom)

7.269 VNL – Vihaan Networks Limited (Shyam Group)

7.270 Wave Electronics

7.271 WDNA (Wireless DNA)

7.272 WIM Technologies

7.273 Wind River Systems

7.274 Wipro

7.275 Wiwynn (Wistron Corporation)

7.276 WNC (Wistron NeWeb Corporation)

7.277 Xingtera

7.278 ZaiNar

7.279 Z-Com

7.280 Zeetta Networks

7.281 Zinkworks

7.282 ZTE

7.283 zTouch Networks

7.284 Zyxel (Unizyx Holding Corporation)

8 Chapter 8: Market Sizing & Forecasts

8.1 Mobile Network Automation

8.2 Network Domain Submarkets

8.2.1 RAN Automation

8.2.2 Mobile Core Automation

8.2.3 xHaul Transport Automation

8.3 RAN Automation Functional Areas

8.3.1 SON-Based Automation

8.3.2 Open RAN Automation

8.3.3 Baseband-Integrated Intelligent RAN Applications

8.3.4 RAN Planning & Optimization Software

8.3.5 Test & Measurement Solutions

8.4 SON-Based Automation Submarkets

8.4.1 RAN Vendor SON Solutions

8.4.2 Third Party C-SON Platforms

8.5 Open RAN Automation Submarkets

8.5.1 Non-RT RIC & SMO

8.5.2 Near-RT RIC

8.5.3 rApps

8.5.4 xApps

8.6 Access Technology Generations

8.6.1 LTE

8.6.2 5G NR

8.6.3 6G

8.7 Regional Segmentation

8.7.1 North America

8.7.2 Asia Pacific

8.7.3 Europe

8.7.4 Middle East & Africa

8.7.5 Latin & Central America

9 Chapter 9: Conclusion & Strategic Recommendations

9.1 Why is the Market Poised to Grow?

9.2 Future Roadmap: 2024 – 2030

9.2.1 2024 – 2026: Production-Grade Deployments of SMO & RIC Platforms for Brownfield Networks

9.2.2 2027 – 2029: Widespread Adoption of Open RAN Automation & Diverse RIC-Hosted Applications

9.2.3 2030 & Beyond: Towards AI-Native Air Interfaces & Zero-Touch 5G/6G Network Automation

9.3 Reviewing the Real-World Benefits & TCO Savings Potential of RAN Automation

9.4 Impact of Intelligent Automation on RAN Engineering Roles

9.5 Transition From SON to Open RAN Automation

9.6 Evolution of Use Cases & AI/ML Algorithms

9.7 Growing Focus on Energy Efficiency & Sustainability

9.8 Vertical Industries & Private Wireless Automation

9.9 Diversified Community of x/rApp Developers

9.10 Signs of Consolidation in the SMO & RIC Ecosystem

9.11 Which RAN Automation Platform & Application Vendors Are Leading the Market?

9.12 Prospects of Hosting Third Party Applications Within RAN Baseband Products

9.13 Paving the Path to an AI/ML-Based 6G Air Interface

9.14 Convergence of AI & RAN Infrastructure

9.15 Strategic Recommendations

9.15.1 RAN Automation Solution Providers

9.15.2 Mobile Operators

10 Chapter 10: Expert Opinion – Interview Transcripts

10.1 AirHop Communications

10.2 Amdocs

10.3 Groundhog Technologies

10.4 Innovile

10.5 Net AI

10.6 Nokia

10.7 P.I. Works

10.8 Qualcomm

10.9 Rakuten Mobile

10.10 RIMEDO Labs

List of Companies Mentioned

The following companies and organizations have been reviewed, discussed or mentioned in the report:

1&1

3GPP (Third Generation Partnership Project)

4iG Group

A10 Networks

A5G Networks

Aalyria

Aarna Networks

Abside Networks

Accedian

Accelleran

Accuver

Acentury

Actiontec Electronics

Adtran

Aglocell

AI-LINK

Aira Technologies

AI-RAN Alliance

Aircom

AirHop Communications

Airspan Networks

AiVader

Aliniant

Allot

Alpha Networks

Alphabet

Altice Portugal

Amazon

AMD (Advanced Micro Devices)

Amdocs

América Móvil

Andorra Telecom

Anktion (Fujian) Technology

Anritsu

Antevia Networks

Arcadyan Technology Corporation

Argela

ARIB (Association of Radio Industries and Businesses, Japan)

Arm

ArrayComm (Chengdu ArrayComm Wireless Technologies)

Arrcus

Artemis Networks

Artiza Networks

Arukona

AsiaInfo Technologies

Askey Computer Corporation

ASOCS

Aspire Technology

ASTRI (Hong Kong Applied Science and Technology Research Institute)

ASUS (ASUSTeK Computer)

AT&T

Ataya

ATDI

Atesio

ATIS (Alliance for Telecommunications Industry Solutions)

Atrinet

Auden Techno

Auray Technology

Aviat Networks

AWS (Amazon Web Services)

Axiata Group

Azcom Technology

Baicells

Batelco

beCloud (Belarusian Cloud Technologies)

Beeline Russia (VimpelCom)

Bell Canada

Betacom

Bharti Airtel

BLiNQ Networks

Blu Wireless

Booz Allen Hamilton

BravoCom

Broadcom

BT Group

BTC (Botswana Telecommunications Corporation)

BTI Wireless

BubbleRAN

B-Yond

C Spire

C3Spectra

CableFree (Wireless Excellence)

Cambium Networks

Capgemini Engineering

CBNG (Cambridge Broadband Networks Group)

CCI (Communication Components Inc.)

CCSA (China Communications Standards Association)

Celfinet

Cellfie Mobile

Celona

CelPlan Technologies

Ceragon Networks

CETC (China Electronics Technology Group Corporation)

CETIN Group

CGI

Chengdu NTS

China Mobile

China Telecom

China Unicom

CICT – China Information and Communication Technology Group (China Xinke Group)

Ciena Corporation

CIG (Cambridge Industries Group)

Cisco Systems

CK Hutchison

Claro Colombia

Clavister

Cohere Technologies

Comarch

Comba Telecom

CommAgility

CommScope

Compal Electronics

COMSovereign

Contela

Corning

CPQD (Center for Research and Development in Telecommunications, Brazil)

Creanord

Cyient

Datang Telecom Technology & Industry Group

DeepSig

Dell Technologies

DGS (Digital Global Systems)

DIGI Communications

Digis Squared

Digitata

DISH Network Corporation

Djezzy

D-Link Corporation

Druid Software

DSA (Dynamic Spectrum Alliance)

DT (Deutsche Telekom)

DZS

ECE (European Communications Engineering)

EDX Wireless

eino

Elisa

Elisa Polystar

Encora

Equiendo

Ericsson

Errigal

E-Space

Etisalat Group (e&)

ETRI (Electronics & Telecommunications Research Institute, South Korea)

ETSI (European Telecommunications Standards Institute)

EXFO

Fairspectrum

Federated Wireless

FET (Far EasTone Telecommunications)

FiberHome Technologies

Firecell

Flash Networks

Forsk

Fortinet

Foxconn (Hon Hai Technology Group)

Fraunhofer HHI (Heinrich Hertz Institute)

Fujitsu

FullRays (LDAS – LocationDAS)

Future Connections

FYRA

G REIGNS

Gemtek Technology

GENEViSiO

Gigamon

GigaTera Communications

GlobalLogic

Globalstar

Globe Telecom

Google

Groundhog Technologies

GSMA (GSM Association)

GTAA (Global Telco AI Alliance)

Guavus

GXC (Formerly GenXComm)

HCLTech (HCL Technologies)

Helios (Fujian Helios Technologies)

HFR Networks

Highstreet Technologies

Hitachi

HPE (Hewlett Packard Enterprise)

HSC (Hughes Systique Corporation)

HTC Corporation

Huawei

Hutchison Drei Austria

IBM

iBwave Solutions

iConNext

IETF (Internet Engineering Task Force)

Infinera

Infosys

Infovista

Inmanta

Innovile

InnoWireless

Intel Corporation

InterDigital

Intracom Telecom

Inventec Corporation

ISCO International

IS-Wireless

Itential

ITRI (Industrial Technology Research Institute, Taiwan)

ITU (International Telecommunication Union)

JMA Wireless

JRC (Japan Radio Company)

Juniper Networks

KDDI

Key Bridge Wireless

Keysight Technologies

Kleos

KMW

KPN

KT Corporation

Kumu Networks

Kuzey Kıbrıs Turkcell

Kyivstar

Lemko Corporation

Lenovo

LG Uplus

Liberty Global

life:)/BeST (Belarusian Telecommunications Network)

lifecell Ukraine

Lime Microsystems

Linux Foundation

LIONS Technology

LITE-ON Technology Corporation

LitePoint

LS telcom

LTT (Libya Telecom & Technology)

LuxCarta

MantisNet

Marvell Technology

MÁSMÓVIL

Mavenir

Maxar Technologies

MegaFon

MEO

RAN Automation, SON, RIC, xApps & rApps in the 5G Era: 2024 – 2030 – Opportunities, Challenges, Strategies & Forecasts

SNS Worldwide Ltd. 2024