AI-RAN 시대의 RAN 자동화, SON, RIC, SMO, rApps, xApps, dApps(2026-2040년) : 기회, 과제, 전략 및 예측

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' AN (Autonomous Network) journeys towards L4 (Highly Autonomous) and eventually L5 (Fully Autonomous) operations. 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 (Capital Expenditure), optimizing performance, improving user experience and enabling new services.

Rakuten Mobile, China Mobile, STC (Saudi Telecom Company), TDC NET and several other operators have already achieved a limited level of L4 autonomy for specific use cases in their live RAN operations. The proven, quantifiable benefits of this level of intent-driven, closed-loop RAN automation range from 20% energy savings without compromising customer experience to a reduction of up to 60% in cell outages during major events such as the annual Hajj pilgrimage. Beyond terrestrial public mobile networks, RAN automation is also gaining traction in other segments of the cellular industry, supporting applications ranging from NTN (Non-Terrestrial Network) resource orchestration and terrestrial-satellite interworking to agentic AI-enabled private 5G network management, adaptive power control in offshore cellular deployments, ISAC (Integrated Sensing & Communications) for counter-drone operations and emission control for tactical 5G networks.

The RAN automation market traces its origins to the early 2010s when SON (Self-Organizing Network) technology – in the form of both embedded D-SON (Distributed SON) and C-SON (Centralized SON) implementations – was introduced to reduce cellular network complexity through self-configuration, self-optimization and self-healing. The pursuit of higher levels of autonomy, coupled with the multi-vendor interoperability, scalability and latency-related limitations of legacy SON solutions, is accelerating the transition to Open RAN automation with standards-based components that enable greater levels of RAN programmability and automation. These include the Near-RT (Real-Time) and Non-RT RICs (RAN Intelligent Controllers), SMO (Service Management & Orchestration) framework, rApps (RAN Applications), xApps (Extended Applications) and dApps (Distributed Applications).

In particular, centrally coordinated rApps hosted on the SMO and Non-RT RIC layer have emerged as the preferred approach to multi-vendor RAN automation on a network-wide basis, especially as hybrid and Open RAN architectures become increasingly common in new RAN swap and rollout contracts. Additionally, SMO platforms are being positioned as a common foundation for cross-domain network autonomy that links observability, intelligence, intent management and closed-loop automation. From Swedish wireless giant Ericsson to Spanish private 5G orchestration specialist Neutroon, multiple SMO providers have already incorporated core network interfaces for end-to-end management and automation.

SMO/Non-RT RIC adoption is beginning to accelerate among brownfield operators – including but not limited to AT&T, Verizon, Telus, Vodafone, DT (Deutsche Telekom), Swisscom, STC, NTT DoCoMo, Chunghwa Telecom and Telstra – to enable network-wide automation across thousands of cell sites through RAN vendor-supplied, third-party and internally developed rApps. This follows greenfield operator Rakuten Mobile’s nationwide RIC deployment, along with in-house and third-party rApp integration, across its Open RAN-compliant footprint of 350,000 cells supplied by 10 distinct RU (Radio Unit) and DU/CU (Distributed & Centralized Unit) vendors.

While reluctance among DU/CU vendors has held back adoption of xApps and the Near-RT RIC, there is growing interest in the recently proposed E3 (Edge‑Enhanced Exposure) interface, which provides a standardized mechanism for dApps to directly interface with Open RAN baseband nodes for closed-loop control beyond the 10-millisecond latency limits of xApps, including lower-layer performance optimization, geolocation, integrated sensing, spectrum sharing and interference cancellation in 5G and future 6G deployments. RAN vendors and application developers – such as Nokia, NVIDIA, Radisys, IS-Wireless, Cohere Technologies, DeepSig, ISCO International, Unikie, Booz Allen Hamilton and ODC (ORAN Development Company) – have already committed to support the E3 interface. An alternative to dApps being pursued by some vendors is to offer proprietary AI-enabled, real-time optimization capabilities as software upgrades to existing RAN platforms through subscription-based licensing models. For example, Ericsson has recently launched a software subscription offering for service providers that embeds AI/ML models directly into RAN basebands and radios to improve performance and efficiency through microsecond-level inference for use cases such as scheduling, link adaptation, beamforming and multi-layer coordination.

Commonly referred to as AI-for-RAN, the deeper integration of AI, ML and their derivatives for RAN automation and optimization is the most commercially mature facet of the broader AI-RAN movement, which also encompasses AI-and-RAN – the co-hosting of RAN and external AI workloads on shared, GPU-accelerated infrastructure; and AI-on-RAN – the enablement of edge AI services for mobile subscribers. The launch of the first 6G networks – which are being standardized as AI-native systems from the outset – around 2030 is expected to significantly increase the scope for hardware-agnostic, software-led innovation and take the industry closer to fully autonomous network operations. RAN digital twins are also expected to play an increasingly important role, allowing operators to validate AI-enabled air interface features and train optimization algorithms in high-fidelity virtual environments before introducing them into live 6G networks.

Largely driven by growing investments in both centrally coordinated SMO/RIC-enabled automation at the network level and AI-for-RAN software upgrades for embedded real-time intelligence and the replacement of static rule-based algorithms in RAN nodes, annual spending on AI-RAN automation and optimization software and services is expected to grow at a CAGR of 9% over the next four years, eventually reaching $1.1 billion by the end of 2030. Total spending on the wider RAN automation market – which includes traditional SON/proprietary network-level automation, AI-for-RAN upgrades, Open RAN SMO/RIC and d/x/rApps, RF planning tools and automated test/measurement solutions – is expected to grow at a CAGR of approximately 4% during the same period.

The “RAN Automation, SON, RIC, SMO, rApps, xApps & dApps in the AI-RAN Era: 2026 – 2040 – 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 2026 to 2040. The forecasts cover three network domains, nine functional areas, three access technology generations, 12 use case categories, three network types, two supplier categories and five regional markets.

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

Key Findings

• The report has the following key findings:
• Largely driven by growing investments in both centrally coordinated SMO/RIC-enabled automation at the network level and AI-for-RAN software upgrades for embedded real-time intelligence and the replacement of static rule-based algorithms in RAN nodes, annual spending on AI-RAN automation and optimization software and services is expected to grow at a CAGR of 9% over the next four years, eventually reaching $1.1 billion by the end of 2030.
• Total spending on the wider RAN automation market – which includes traditional SON/proprietary network-level automation, AI-for-RAN upgrades, Open RAN SMO/RIC and d/x/rApps, RF planning tools and automated test/measurement solutions – is expected to grow at a CAGR of approximately 4% during the same period.
• Mobile operators are making tangible progress towards L4 RAN operations as they work towards the longer-term goal of L5 networks. Rakuten Mobile, China Mobile, STC, TDC NET and several other operators have already achieved a limited level of L4 autonomy for specific use cases in their live networks. The proven, quantifiable benefits of this level of intent-driven, closed-loop RAN automation range from 20% energy savings without compromising customer experience to a reduction of up to 60% in cell outages during major events such as the annual Hajj pilgrimage.
• The pursuit of higher levels of autonomy, coupled with the multi-vendor interoperability, scalability and latency-related limitations of legacy SON solutions, is accelerating the transition to Open RAN automation with standards-based components that enable greater levels of RAN programmability and automation.
• In particular, centrally coordinated rApps hosted on the SMO and Non-RT RIC layer have emerged as the preferred approach to multi-vendor RAN automation on a network-wide basis, especially as hybrid and Open RAN architectures become increasingly common in new RAN swap and rollout contracts. Additionally, SMO platforms are being positioned as a common foundation for cross-domain network autonomy that links observability, intelligence, intent management and closed-loop automation. From Swedish wireless giant Ericsson to Spanish private 5G orchestration specialist Neutroon, multiple SMO providers have already incorporated core network interfaces for end-to-end management and automation.
• SMO/Non-RT RIC adoption is beginning to accelerate among brownfield operators – including but not limited to AT&T, Verizon, Telus, Vodafone, DT, Swisscom, STC, NTT DoCoMo, Chunghwa Telecom and Telstra – to enable network-wide automation across thousands of cell sites through RAN vendor-supplied, third-party and internally developed rApps. This follows greenfield operator Rakuten Mobile’s nationwide RIC deployment, along with in-house and third-party rApp integration, across its Open RAN-compliant footprint of 350,000 cells supplied by 10 distinct RU and DU/CU vendors.
• While reluctance among DU/CU vendors has held back adoption of xApps and the Near-RT RIC, there is growing interest in the recently proposed E3 interface, which provides a standardized mechanism for dApps to directly interface with Open RAN baseband nodes for closed-loop control beyond the 10-millisecond latency limits of xApps, including lower-layer performance optimization, geolocation, integrated sensing, spectrum sharing and interference cancellation in 5G and future 6G deployments. RAN vendors and application developers – such as Nokia, NVIDIA, Radisys, IS-Wireless, Cohere Technologies, DeepSig, ISCO International, Unikie, Booz Allen Hamilton and ODC – have already committed to support the E3 interface.
• An alternative to dApps being pursued by some vendors is to offer proprietary AI-enabled, real-time optimization capabilities as software upgrades to existing RAN platforms through subscription-based licensing models. For example, Ericsson has recently launched a software subscription offering for service providers that embeds AI/ML models directly into RAN basebands and radios to improve performance and efficiency through microsecond-level inference for use cases such as scheduling, link adaptation, beamforming and multi-layer coordination.
• The deeper integration of AI, ML and their derivatives for RAN automation and optimization – commonly referred to as AI-for-RAN – is the most commercially mature facet of the broader AI-RAN movement. The launch of the first 6G networks – which are being standardized as AI-native systems from the outset – around 2030 is expected to significantly increase the scope for hardware-agnostic, software-led innovation and take the industry closer to fully autonomous network operations. RAN digital twins are also expected to play an increasingly important role, allowing operators to validate AI-enabled air interface features and train optimization algorithms in high-fidelity virtual environments before introducing them into live 6G networks.
• Beyond terrestrial public mobile networks, there is growing interest in RAN automation for other segments, including the use of an SMO/RIC governance layer for dynamic resource orchestration in NTNs and interworking with terrestrial RAN infrastructure, from mobility and handover management to on-demand satellite coverage activation – particularly during disaster response scenarios.
• In the private 5G segment, RAN automation applications are being pursued for diverse use cases, including agentic AI to simplify network deployment and administration, converged management of Wi-Fi and private 5G networks, dynamic spectrum allocation between incumbents and other user groups, adaptive power control in offshore cellular deployments, indoor positioning for physical AI and industrial automation in 5G-connected factories, ISAC for drone detection and emission control to limit the electromagnetic signature of tactical 5G infrastructure during military operations.
• One recent example is the implementation of an NVIDIA-powered agentic AI solution for autonomous private 5G network optimization aboard vessels operated by Norwegian shipping company Color Line. In Japan, domestic integrator NTT East has recently concluded a series of tests with 26 vendors to evaluate RIC functionality for AI-enabled autonomous network control applications supporting transmit power optimization and interference mitigation in private 5G networks.
• The Open RAN automation ecosystem is exhibiting signs of consolidation with Nokia’s acquisition of Juniper Networks' SMO/RIC assets from HPE (Hewlett Packard Enterprise) and VMware's exit from the market altogether. We anticipate seeing further M&A activity reminiscent of the SON boom in the previous decade. However, there’s no lack of diversity in the application segment with dozens of companies – including RAN vendors, third-party developers and mobile operators themselves – actively engaged in the development of rApps, xApps and, more recently, dApps.
• Nordic telecommunications infrastructure giants Ericsson and Nokia are presenting an unusually united front by joining each other’s SMO/rApp ecosystems, seemingly moving away from the SON era walled garden approach to clear the path for true cross-vendor automation.

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, d/x/rApps, AI-for-RAN software upgrades, RAN planning tools and test/measurement solutions
• Review of over 85 RAN automation use cases, ranging from ANR, PCI and RACH optimization to automated cell shaping, advanced traffic steering, QoE-based resource allocation, energy savings, anomaly detection, dynamic RAN security, adaptive network slicing, ISAC, NTN orchestration, private 5G automation and RAN digital twins
• Key trends in intelligent RAN implementations, including the SON-to-SMO/RIC transition, closed-loop automation, intent-driven management, embedded real-time intelligence, operational AI/ML, deep reinforcement learning, Gen AI, agentic 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 25 production-grade RAN automation deployments across multiple operator groups, national mobile operators and private network operators
• Worldwide review of ongoing RAN automation projects across over 100 operators, covering traditional SON, Open RAN and AI/ML-driven automation approaches
• Future roadmap of RAN automation
• Standardization and collaborative initiatives
• Profiles and strategies of more than 300 ecosystem players, including RAN vendors, companies active in the core and xHaul segments, RIC, SMO and OSS platform providers, d/x/rApp developers, AI/ML technology specialists, RAN planning software suppliers and test/measurement solution providers
• Strategic recommendations for RAN automation solution providers and mobile operators
• Market analysis and forecasts from 2026 to 2040

Forecast Segmentation

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

• Mobile Network Automation Submarkets
  • RAN
  • Mobile Core
  • xHaul Transport
• RAN Automation Functional Areas
  • Traditional SON & Vendor-Specific RAN Automation
    • SON & Proprietary Network-Level Automation Platforms
    • AI-for-RAN Software Upgrades for Real-Time RAN Intelligence & Automation
  • SMO/RIC & Open RAN Automation
    • Non-RT RIC & SMO
    • Near-RT RIC
    • rApps
    • xApps
    • dApps
  • RF Planning & Optimization Tools
  • Test & Measurement Solutions
• Access Technology Generation Submarkets
  • LTE
  • 5G
  • 6G
• Use Case Segments
  • PCI, RACH & Configuration Automation
  • Mobility, Handover & Load Management
  • RAN Resource & Performance Optimization
  • Energy Efficiency & Sustainability
  • Spectrum Management & Multi-RAT Connectivity
  • Network Healing & Protection
  • Massive MIMO, Beamforming & Lower-Layer Optimization
  • Network Slicing & Vertical Applications
  • ISAC, Positioning & Tracking
  • Network Planning & Evolution
  • Automation & AI Enablement
  • Multi-Domain Use Cases
• Network Types
  • Public Mobile Operator Networks
  • NTN Infrastructure
  • Private Cellular Networks
• Supplier Categories
  • RAN Vendors
  • Third-Party Suppliers
• Regional Markets
  • North America
  • Asia Pacific
  • Europe
  • Middle East & Africa
  • Latin & Central America

Table of Contents

1. Introduction

• 1.1 Executive Summary
• 1.2 Topics Covered
• 1.3 Forecast Segmentation
• 1.4 Key Findings
• 1.5 Methodology
• 1.6 Target Audience

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), rApps, xApps & dApps
  • 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 Software & Infrastructure Vendors
  • 2.4.3 rApp/xApp/dApp & 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 Operators
    • 2.4.7.4 Neutral Hosts
    • 2.4.7.5 Satellite Operators
  • 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 Infrastructure
  • 2.5.2 AI-RAN, Open RAN & vRAN Adoption
  • 2.5.3 TCO (Total Cost of Ownership) Reduction
  • 2.5.4 Energy Savings, Sustainability & Environmental Conservation
  • 2.5.5 Popularity of Operational, Generative & Agentic AI Technologies
  • 2.5.6 Subscriber Experience & Network Performance Benefits
  • 2.5.7 Network Slicing & New Revenue-Generating Opportunities
  • 2.5.8 Proliferation of Private 5G, Neutral Host & Non-Terrestrial 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 d/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. 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, rApps, xApps & dApps
  • 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 rApps (RAN Applications)
    • 3.2.1.5 xApps (Extended Applications)
    • 3.2.1.6 dApps (Distributed 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 E3 Interface Between dApps & RAN Nodes
    • 3.2.2.4 O1 Interface for OAM (Operations, Administration & Maintenance)
    • 3.2.2.5 O2 Interface for Cloud Infrastructure Management
    • 3.2.2.6 R1 Interface for rApp Portability Across RIC Platforms
    • 3.2.2.7 xApp APIs (Application Programming Interfaces)
    • 3.2.2.8 Potential Decoupling of the SMO & Non-RT RIC
    • 3.2.2.9 Open Fronthaul M-Plane Interface
    • 3.2.2.10 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 Concept for Real-Time Inference & Decisions
  • 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 & AAS (Active Antenna System) Optimization
    • 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 RAN Site Energy Orchestration
    • 3.7.4.8 Carbon Footprint Awareness
    • 3.7.4.9 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 Emission Control for Military Operations
    • 3.7.5.7 Shared & Unlicensed Spectrum Coordination
    • 3.7.5.8 DSS (Dynamic Spectrum Sharing)
    • 3.7.5.9 4G-5G DC (Dual Connectivity) Control
    • 3.7.5.10 ISAC (Integrated Sensing & Communications)
  • 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 Predictive Maintenance of RAN Infrastructure
    • 3.7.6.5 RET (Remote Electrical Tilt) Adjustment in Disaster Scenarios
    • 3.7.6.6 CPM (Congestion Prediction & Management)
    • 3.7.6.7 RF Jamming Detection
    • 3.7.6.8 Signaling Storm Protection
    • 3.7.6.9 Closed Loop RAN Security
    • 3.7.6.10 Root Cause Analysis
  • 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, In-Building, 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 In-Building Wireless Optimization
    • 3.7.8.6 IIoT (Industrial IoT) & Enterprise RAN Customization
    • 3.7.8.7 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 Twins
    • 3.7.9.6 High-Fidelity AI-RAN Simulation
    • 3.7.9.7 Automated RAN Migration Workflows
    • 3.7.9.8 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 Outdoor UE Positioning & Trajectory Prediction
    • 3.7.10.4 Indoor UE Positioning & Localization
    • 3.7.10.5 KPI (Key Performance Indicator) Monitoring
    • 3.7.10.6 MDT (Minimization of Drive Tests) & RAN Data Collection
    • 3.7.10.7 Integration of Datasets External to the RAN
    • 3.7.10.8 RAN-Enabled Forecasting of Localized Weather Events
    • 3.7.10.9 AI/ML-Enabled Network Insights & Diagnostics
    • 3.7.10.10 Natural Language Interaction with RAN Data
    • 3.7.10.11 Real-Time Wireless Channel Observability
    • 3.7.10.12 Traffic Forecasting & QoS/QoE Prediction
  • 3.7.11 Multi-Domain, Core & Transport-Related Use Cases
    • 3.7.11.1 Automated Configuration Management
    • 3.7.11.2 Test & Validation Automation
    • 3.7.11.3 Dynamic Autoscaling of Network Resources
    • 3.7.11.4 Service Assurance, Fault Management & Cybersecurity
    • 3.7.11.5 AI/ML-Driven Intelligence for End-to-End Network Slicing
    • 3.7.11.6 Core Network Automation & Intelligent Orchestration
    • 3.7.11.7 NWDAF (Network Data Analytics Function) for Core Network Analytics
    • 3.7.11.8 MDAF (Management Data Analytics Function) for Management Plane Analytics
    • 3.7.11.9 SDN (Software-Defined Networking)-Based xHaul Transport Automation
    • 3.7.11.10 Interference Management in Microwave & mmWave (Millimeter Wave) Transport Links
    • 3.7.11.11 Interworking Between RAN SMO, NWDAF, MDAF & Transport Domain SDN Controllers

4. Key Trends in Intelligent RAN Implementations

• 4.1 Transition From SON to Open RAN-Based RIC, SMO, rApps, xApps & dApps
  • 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 Agentic AI
  • 4.5.1 From AI-Assisted Operations to Autonomous Execution
  • 4.5.2 Single-Agent & Multi-Agent Architectures
  • 4.5.3 Agentic AI for RAN Automation & Optimization
  • 4.5.4 Integration With SMO/RIC & Closed-Loop Automation
  • 4.5.5 Operational Guardrails & Human Oversight
• 4.6 Network Data Analytics
  • 4.6.1 Descriptive Analytics
  • 4.6.2 Diagnostic Analytics
  • 4.6.3 Predictive Analytics
  • 4.6.4 Prescriptive Analytics
• 4.7 Observability of Network Operations
  • 4.7.1 Deeper Visibility Into RAN Telemetry
  • 4.7.2 Integrating Supplementary Data Sources
  • 4.7.3 End-to-End Network Observability Control
• 4.8 Cloud-Native & Software-Centric Networking
  • 4.8.1 Cloud-Native Technologies
  • 4.8.2 Microservices & SBA (Service-Based Architecture)
  • 4.8.3 Network Virtualization & Containerization
  • 4.8.4 SDN for Network Programmability
  • 4.8.5 DevOps & CI/CD (Continuous Integration & Delivery)
• 4.9 Other Trends & Developments
  • 4.9.1 RAN Densification & Multi-Layer Coordination
  • 4.9.2 Plug & Play Small Cells in Industrial, Enterprise & Public Venues
  • 4.9.3 RAN Automation for Private 5G Network Management
  • 4.9.4 Support for Vertical Industry-Specific Use Cases
  • 4.9.5 FWA (Fixed Wireless Access) Deployments
  • 4.9.6 D2D (Direct-to-Device) Satellite Connectivity & TN-NTN Integration
  • 4.9.7 Shared & Unlicensed Spectrum
  • 4.9.8 Network Slicing Enablement
  • 4.9.9 AI-RAN & Edge Computing
  • 4.9.10 Application Awareness
  • 4.9.11 Dynamic Security

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 RAN Automation in 5G Advanced Systems
  • 5.1.6 Release 19: AI/ML Air Interface Framework, Predictive CCO, MRO Enhancements & NTN/Slice-Aware MDT
  • 5.1.7 Release 20: Further AI/ML, Self-Optimization & MDT Enhancements for the 5G-Advanced Evolution
  • 5.1.8 Release 21 & Beyond: 6G RAN Automation & New AI/ML Use Cases
• 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 OCUDU Ecosystem Foundation
  • 5.8.2 ONAP (Open Network Automation Platform)
  • 5.8.3 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 SMO & RIC Architecture Specifications
  • 5.11.2 RAN Automation 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 TelcoFabric
  • 5.15.1 xApps, rApps & Other Artifacts
• 5.16 TIP (Telecom Infra Project)
  • 5.16.1 OpenRAN Project Group
    • 5.16.1.1 RIA (RAN Intelligence & Automation) Subgroup
    • 5.16.1.2 ROMA (RAN Orchestration & Lifecycle Management Automation) Subgroup
  • 5.16.2 TelcoAI Project Group
• 5.17 TM Forum
  • 5.17.1 Addressing Higher-Level Aspects of AN (Autonomous Networks)
  • 5.17.2 AN Standards & Autonomy Level Assessment Tools
• 5.18 Other Initiatives & Academic Research

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 Boldyn Networks
  • 6.4.1 Vendor Selection
  • 6.4.2 Deployment Review
  • 6.4.3 Results & Future Plans
• 6.5 BT Group
  • 6.5.1 Vendor Selection
  • 6.5.2 Deployment Review
  • 6.5.3 Results & Future Plans
• 6.6 DT (Deutsche Telekom)
  • 6.6.1 Vendor Selection
  • 6.6.2 Deployment Review
  • 6.6.3 Results & Future Plans
• 6.7 Elisa
  • 6.7.1 Vendor Selection
  • 6.7.2 Deployment Review
  • 6.7.3 Results & Future Plans
• 6.8 Globe Telecom
  • 6.8.1 Vendor Selection
  • 6.8.2 Deployment Review
  • 6.8.3 Results & Future Plans
• 6.9 NTT DoCoMo
  • 6.9.1 Vendor Selection
  • 6.9.2 Deployment Review
  • 6.9.3 Results & Future Plans
• 6.10 Ooredoo
  • 6.10.1 Vendor Selection
  • 6.10.2 Deployment Review
  • 6.10.3 Results & Future Plans
• 6.11 Orange
  • 6.11.1 Vendor Selection
  • 6.11.2 Deployment Review
  • 6.11.3 Results & Future Plans
• 6.12 Rakuten Mobile
  • 6.12.1 Vendor Selection
  • 6.12.2 Deployment Review
  • 6.12.3 Results & Future Plans
• 6.13 Singtel
  • 6.13.1 Vendor Selection
  • 6.13.2 Deployment Review
  • 6.13.3 Results & Future Plans
• 6.14 SK Telecom
  • 6.14.1 Vendor Selection
  • 6.14.2 Deployment Review
  • 6.14.3 Results & Future Plans
• 6.15 SoftBank
  • 6.15.1 Vendor Selection
  • 6.15.2 Deployment Review
  • 6.15.3 Results & Future Plans
• 6.16 STC (Saudi Telecom Company)
  • 6.16.1 Vendor Selection
  • 6.16.2 Deployment Review
  • 6.16.3 Results & Future Plans
• 6.17 Swisscom
  • 6.17.1 Vendor Selection
  • 6.17.2 Deployment Review
  • 6.17.3 Results & Future Plans
• 6.18 Telecom Argentina
  • 6.18.1 Vendor Selection
  • 6.18.2 Deployment Review
  • 6.18.3 Results & Future Plans
• 6.19 Telefonica Group
  • 6.19.1 Vendor Selection
  • 6.19.2 Deployment Review
  • 6.19.3 Results & Future Plans
• 6.20 Telstra
  • 6.20.1 Vendor Selection
  • 6.20.2 Deployment Review
  • 6.20.3 Results & Future Plans
• 6.21 Telus
  • 6.21.1 Vendor Selection
  • 6.21.2 Deployment Review
  • 6.21.3 Results & Future Plans
• 6.22 TIM (Telecom Italia Mobile)
  • 6.22.1 Vendor Selection
  • 6.22.2 Deployment Review
  • 6.22.3 Results & Future Plans
• 6.23 Turkcell
  • 6.23.1 Vendor Selection
  • 6.23.2 Deployment Review
  • 6.23.3 Results & Future Plans
• 6.24 Verizon Communications
  • 6.24.1 Vendor Selection
  • 6.24.2 Deployment Review
  • 6.24.3 Results & Future Plans
• 6.25 Vodafone Group
  • 6.25.1 Vendor Selection
  • 6.25.2 Deployment Review
  • 6.25.3 Results & Future Plans
• 6.26 Worldwide Review of RAN Automation Projects
  • 6.26.1 1&1: Highly Automated Control of Europe's First Greenfield Open RAN Network
  • 6.26.2 2degrees: Expanding Automation Across RAN, Core & Transport Network Domains
  • 6.26.3 A1 Telekom Austria Group: AI-Based Platform for Group-Wide RAN Automation
  • 6.26.4 AIS (Advanced Info Service): Evolving From Telco to Cognitive Tech-Co
  • 6.26.5 America Movil: SON-Based RAN Automation for 5G Network Rollout & Optimization
  • 6.26.6 Andorra Telecom: Doubling Throughput With Automated RF Interference Mitigation
  • 6.26.7 AT&T: Transitioning From Legacy SON to Open RAN Automation
  • 6.26.8 AXIAN Telecom: Smart RAN Operations Across Pan-African Mobile Network Footprint
  • 6.26.9 Axiata Group: Autonomous Network Initiative for Streamlining Operations
  • 6.26.10 Batelco: AI-Powered Energy Savings & Carbon Footprint Reduction
  • 6.26.11 beCloud (Belarusian Cloud Technologies): Intelligent Network Management
  • 6.26.12 Beeline Russia (VimpelCom): Transforming the Mobile Experience Using C-SON
  • 6.26.13 Bell Canada: Software-Enabled SU/MU-MIMO Optimization to Improve Capacity & Subscriber Experience
  • 6.26.14 Bharti Airtel: Meeting Evolving Subscriber Needs With RAN Automation
  • 6.26.15 Boldyn Networks: Vendor-Neutral RIC Platform for Private Networks
  • 6.26.16 Bouygues Telecom: AI-Enabled RAN Automation & rApp Development
  • 6.26.17 BT Group: Intelligent RAN Operations & Automated Parameter Governance
  • 6.26.18 BTC (Botswana Telecommunications Corporation): Improving Service Quality With Automated RAN Optimization
  • 6.26.19 C Spire: SON-Enabled Automation of Regional Wireless Network
  • 6.26.20 Cellfie Mobile: Intelligent RAN Monitoring & Management
  • 6.26.21 CETIN Group: Multi-Domain Automated Network Optimization
  • 6.26.22 China Mobile: Aiming for AI/ML-Assisted L4 Automation
  • 6.26.23 China Telecom: Co-Governance of Shared 5G Network Infrastructure
  • 6.26.24 China Unicom: CUBE-RAN Intelligent Open Platform for Third Party Applications
  • 6.26.25 Chunghwa Telecom: Moving Towards AI-RAN & Autonomous Network Operations
  • 6.26.26 CK Hutchison: Accelerating the Journey Towards Highly Automated RAN Operations
  • 6.26.27 Color Line: Agentic AI Solution for Autonomous Private 5G Network Optimization
  • 6.26.28 Dhiraagu: AI-Driven RAN Optimization, Energy Management & Digital Twins
  • 6.26.29 DIGI Communications: Laying the Groundwork for Zero-Touch Automation
  • 6.26.30 Djezzy: Harnessing C-SON for Automated RAN Optimization & Management
  • 6.26.31 DT (Deutsche Telekom): Vendor-Independent SMO/RIC for Multi-Vendor RAN Automation
  • 6.26.32 du (EITC – Emirates Integrated Telecommunications Company): rApp-Enabled Autonomous 5G Network Slicing
  • 6.26.33 Econet Wireless Zimbabwe: Targeting AI-Driven RAN Automation & Sustainable Network Operations
  • 6.26.34 Elisa: In-House Closed Loop RAN Automation Platform
  • 6.26.35 Entel: Advancing Towards L4 Autonomy With Agentic RAN Solution
  • 6.26.36 Etisalat Group (e&): AI/ML-Enabled Intelligent Network Management Platform
  • 6.26.37 FET (Far EasTone Telecommunications): Advancing Sustainability Goals With ML-Driven RAN Automation
  • 6.26.38 Globe Telecom: Agentic AI Capabilities for Traffic Forecasting & Self-Healing
  • 6.26.39 ICE (Instituto Costarricense de Electricidad): Pioneering RIC/SMO-Hosted rApps in Latin America
  • 6.26.40 Indosat Ooredoo Hutchison: Paving the Way for AI-RAN & Intelligent Network Operations
  • 6.26.41 Inland Cellular: RIC/xApp-Based Open RAN Automation for Regional Mobile Network
  • 6.26.42 Inventec: rApps for Indoor Positioning & Traffic Steering in Private 5G-Connected Smart Factories
  • 6.26.43 KAC (Korea Airports Corporation): Making Surveillance Video Transmission Robust With AI-RAN
  • 6.26.44 KDDI: Harnessing Deep Learning for Automated Cell-Specific RAN Optimization
  • 6.26.45 KPN: Replacing Labor-Intensive RAN Optimization Tools With Centralized Automation
  • 6.26.46 KT Corporation: Embracing Intelligent Control of RAN Resources & Operations
  • 6.26.47 LG Uplus: Evaluating the SMO/RIC Approach for Multi-Vendor RAN Automation
  • 6.26.48 Liberty Global: Building a Customer-First 5G Network Using Autonomous Optimization Decisions
  • 6.26.49 LTT (Libya Telecom & Technology): Nationwide RAN Automation for Enhanced Network Quality
  • 6.26.50 M1: AI/ML-Enabled Automation Across End-to-End Network Infrastructure
  • 6.26.51 Maxis: Reducing Human Intervention With Automated RAN Optimization
  • 6.26.52 MegaFon: Delivering an Exemplary Subscriber Experience Through SON Technology
  • 6.26.53 MEO Portugal: Automated RAN Optimization & Service Assurance
  • 6.26.54 Mobily (Etihad Etisalat): Advancing Towards Intent-Driven RAN Automation
  • 6.26.55 MTN Group: Advancing Autonomous Mobile Networks in Africa
  • 6.26.56 MTS (Mobile TeleSystems): Self-Adjusting Intelligent Network
  • 6.26.57 NBN Co: AI-Driven Mitigation of Atmospheric Ducting in Fixed Wireless Network
  • 6.26.58 NOS (Portugal): Transforming Network Management Using Agentic AI
  • 6.26.59 NTT DoCoMo: Evolving From SON to SMO/RIC Automation for Intent-Based Network Operations
  • 6.26.60 Odido: AI-Driven Cell Site Energy Management Solution
  • 6.26.61 Omantel: AI/ML-Driven Network Automation & Experience Optimization
  • 6.26.62 One Hungary: Closed Loop Network Management & Customer Experience Monitoring
  • 6.26.63 One New Zealand: Moving Towards Autonomous Network Operations
  • 6.26.64 Ooredoo: AI-Driven RAN Optimization With Digital Twins & Deep RL
  • 6.26.65 Orange: Transitioning Multi-Country SON Implementation Towards SMO/RIC & rApps
  • 6.26.66 Paradise Mobile: Pursuing an AI-First Approach to Network & Service Operations
  • 6.26.67 Rakuten Mobile: Nationwide RIC-Based Automation Across Greenfield Open RAN Network
  • 6.26.68 Reliance Jio Infocomm: Improving Customer Experience With C-SON Technology
  • 6.26.69 Rogers Communications: Cross-Domain Service Orchestration & Automation
  • 6.26.70 SaskTel: Exploring the Path Towards Autonomous RAN Operations
  • 6.26.71 Singtel: Extending RAN Automation Into 5G Slice Assurance & AI-Native Optimization
  • 6.26.72 SK Telecom: AI-RAN, Distributed AI Computing & Autonomous Network Control
  • 6.26.73 Smart Communications (PLDT): Evaluating RIC-Based RAN Automation
  • 6.26.74 SmarTone: Pursuing a High-Performing Programmable Network Strategy
  • 6.26.75 SoftBank: Converging AI-RAN, Baseband-Hosted AI & Centralized Network Automation
  • 6.26.76 Spark New Zealand: Investing in Self-Healing Network Operations
  • 6.26.77 StarHub: Targeting Open RAN Automation Across Public & Private 5G Networks
  • 6.26.78 STC: Scaling AI-Driven Autonomous RAN & SMO/RIC-Enabled Programmability
  • 6.26.79 Swisscom: Future-Proofing Brownfield Mobile Network With SMO/Non-RT RIC & rApps
  • 6.26.80 Taiwan Mobile: Integrating AI-Powered RAN Automation Capabilities
  • 6.26.81 TDC NET: Inching Towards Net Zero Goals With RAN Automation
  • 6.26.82 Tele2: AI-Driven RAN Energy Savings & Capacity Optimization
  • 6.26.83 Telecom Argentina: Tackling RAN Complexity Through Automated Configuration & Optimization
  • 6.26.84 Telefonica Group: Extending Closed-Loop Automation Towards L4 Autonomous RAN Operations
  • 6.26.85 Telenor Group: Advancing Agentic AI for Radio Controls & Autonomous Network Operations
  • 6.26.86 Telia Company: Paving the Way for Zero-Touch Mobile Networks
  • 6.26.87 Telkomsel: Autonomous Network Program for Operational Efficiency
  • 6.26.88 Telstra: SMO-Based Foundation for AI-Driven & Autonomous RAN Use Cases
  • 6.26.89 Telus: Leveraging SMO/Non-RT RIC for Multi-Vendor Open RAN Automation
  • 6.26.90 TIM (Telecom Italia Mobile): Driving Higher Network Efficiency & Quality With Closed Loop Optimization
  • 6.26.91 T-Mobile US: Harnessing AI for Real-Time RAN Optimization & Adaptation
  • 6.26.92 TNN (TN-Network Denmark): Intent-Based AI & Automated Network Optimization
  • 6.26.93 TPG Telecom: Managing Peak Traffic Congestion With C-SON Technology
  • 6.26.94 True Corporation: Progressing Towards L4 Autonomous Network Operations
  • 6.26.95 Turk Telekom: Driving Efficiency & Energy Savings Through Network Automation
  • 6.26.96 Turkcell: Group-Wide Deployment of Centralized RAN Automation Capabilities
  • 6.26.97 U.S. DOW (Department of War): Open Source AI-RAN Innovation & Applications for Military Communications
  • 6.26.98 Ucom (Armenia): AI Functionalities for Mobile Network Modernization
  • 6.26.99 VEON: Leveraging C-SON to Enhance Network Performance in Multiple Operating Markets
  • 6.26.100 Verizon Communications: Pioneering Multi-Vendor RIC Functionality to Drive Operational Efficiency
  • 6.26.101 Viettel Group: AI/ML-Enabled Physical Layer Signal Processing
  • 6.26.102 Vodafone Group: Scaling SMO/RIC & rApps Across Multiple National Markets
  • 6.26.103 Zain Group: Targeting L4 Automation for Efficient 5G Network Operations

7. Key Ecosystem Players

• 7.1 1Finity (Fujitsu)
• 7.2 A10 Networks
• 7.3 A5G Networks
• 7.4 Aalyria
• 7.5 Abside Networks
• 7.6 Acentury
• 7.7 Actiontec Electronics
• 7.8 Adtran
• 7.9 Advaya Networks
• 7.10 Aglocell
• 7.11 AI-LINK
• 7.12 AiORA Solutions
• 7.13 Aira Technologies
• 7.14 AirHop Communications
• 7.15 Airspan Networks
• 7.16 AiVader
• 7.17 Aliniant
• 7.18 Allot
• 7.19 Alpha Networks
• 7.20 Amantya Technologies
• 7.21 Amazon/AWS (Amazon Web Services)
• 7.22 AMD (Advanced Micro Devices)
• 7.23 Amdocs
• 7.24 AmpliTech
• 7.25 ANDREW (Amphenol Corporation)
• 7.26 Anktion (Fujian) Technology
• 7.27 Anritsu
• 7.28 Antevia Networks
• 7.29 AppLogic Networks (Formerly Sandvine)
• 7.30 Arcadyan Technology Corporation (Compal Electronics)
• 7.31 Argela
• 7.32 Arm
• 7.33 ArrayComm (Chengdu ArrayComm Wireless Technologies)
• 7.34 Arrcus
• 7.35 Artemis ITS (Integrated Technical Services)
• 7.36 Artemis Networks
• 7.37 Artiza Networks
• 7.38 AsiaInfo Technologies
• 7.39 Askey Computer Corporation (ASUS – ASUSTeK Computer)
• 7.40 ASOCS
• 7.41 Aspire Technology (NEC Corporation)
• 7.42 ASTRI (Hong Kong Applied Science and Technology Research Institute)
• 7.43 Ataya
• 7.44 ATDI
• 7.45 Atesio
• 7.46 Atrinet (ServiceNow)
• 7.47 Auray Technology (Auden Techno)
• 7.48 Aviat Networks
• 7.49 AWTG
• 7.50 Azcom Technology
• 7.51 Baicells
• 7.52 Battelle
• 7.53 Betacom
• 7.54 Blare Technologies
• 7.55 BLiNQ Networks
• 7.56 Blu Wireless
• 7.57 Booz Allen Hamilton
• 7.58 BravoCom
• 7.59 Broadcom
• 7.60 BTI Wireless
• 7.61 BubbleRAN
• 7.62 B-Yond/Reailize
• 7.63 C3Spectra
• 7.64 CableFree (Wireless Excellence)
• 7.65 Capgemini Engineering
• 7.66 Cassava AI
• 7.67 CBNG (Cambridge Broadband Networks Group)
• 7.68 Celfinet (Cyient)
• 7.69 Celona
• 7.70 CelPlan Technologies
• 7.71 Ceragon Networks
• 7.72 CGI
• 7.73 Chengdu NTS
• 7.74 CICT – China Information and Communication Technology Group (China Xinke Group)
• 7.75 Ciena Corporation
• 7.76 CIG (Cambridge Industries Group)
• 7.77 Cirrus360
• 7.78 Cisco Systems
• 7.79 Clavister
• 7.80 Cloudnet.ai
• 7.81 CloudRAN.AI (Cloudnet.ai)
• 7.82 Cohere Technologies
• 7.83 Comarch
• 7.84 Comba Telecom
• 7.85 Commsquare
• 7.86 Compal Electronics
• 7.87 COMSovereign
• 7.88 Conekt.ai
• 7.89 Contela
• 7.90 coRAN LABS
• 7.91 Creanord
• 7.92 CUJO AI
• 7.93 Cyient
• 7.94 CyShield
• 7.95 DeepSig
• 7.96 Dell Technologies
• 7.97 Delta Electronics
• 7.98 DGS (Digital Global Systems)
• 7.99 Digis Squared
• 7.100 Digitata
• 7.101 D-Link Corporation
• 7.102 Druid Software
• 7.103 EDX Wireless
• 7.104 eino
• 7.105 Encora
• 7.106 Ericsson
• 7.107 Eridan Communications
• 7.108 Errigal
• 7.109 E-Space
• 7.110 ETRI (Electronics & Telecommunications Research Institute, South Korea)
• 7.111 EXFO
• 7.112 F5
• 7.113 Fairspectrum
• 7.114 Federated Wireless
• 7.115 Firecell/Accelleran
• 7.116 Flash Networks
• 7.117 Flycomm
• 7.118 Forsk
• 7.119 Fortinet
• 7.120 Four Dot Infinity
• 7.121 Foxconn (Hon Hai Technology Group)
• 7.122 Fraunhofer HHI (Heinrich Hertz Institute)
• 7.123 FullRays
• 7.124 FusionLayer
• 7.125 Future Connections
• 7.126 FYRA
• 7.127 G REIGNS (HTC Corporation)
• 7.128 Gemtek Technology
• 7.129 GENEViSiO (QNAP Systems)
• 7.130 Gigamon
• 7.131 GigaTera Communications (KMW)
• 7.132 GlobalLogic (Hitachi)
• 7.133 Globalstar
• 7.134 Google (Alphabet)
• 7.135 Groundhog Technologies
• 7.136 GXC (Formerly GenXComm)
• 7.137 HCLTech (HCL Technologies)
• 7.138 Helios (Fujian Helios Technologies)
• 7.139 HFR Networks
• 7.140 Highstreet Technologies
• 7.141 Highway 9 Networks
• 7.142 Hitachi
• 7.143 HPE (Hewlett Packard Enterprise)
• 7.144 HSC (Hughes Systique Corporation)
• 7.145 Huawei
• 7.146 IBM
• 7.147 iBwave Solutions
• 7.148 Icebreaker Technical Solutions
• 7.149 iConNext
• 7.150 Infosys
• 7.151 Infovista
• 7.152 Inmanta
• 7.153 Innova Solutions
• 7.154 Innovile
• 7.155 Intel Corporation
• 7.156 InterDigital
• 7.157 Intracom Telecom
• 7.158 Invences
• 7.159 Inventec Corporation
• 7.160 iPosi
• 7.161 ISCO International
• 7.162 IS-Wireless
• 7.163 Itential
• 7.164 ITRI (Industrial Technology Research Institute, Taiwan)
• 7.165 JaggedXY
• 7.166 JMA Wireless
• 7.167 JRC (Japan Radio Company)
• 7.168 Juniper Networks (HPE – Hewlett Packard Enterprise)
• 7.169 KCCTech
• 7.170 Kenmei
• 7.171 Key Bridge Wireless
• 7.172 Keysight Technologies
• 7.173 KMW
• 7.174 Kumu Networks
• 7.175 LatenceTech (Latence Technologies)
• 7.176 Laude
• 7.177 Lemko Corporation
• 7.178 Lenovo
• 7.179 LIG Accuver (Formerly InnoWireless)
• 7.180 Lime Microsystems
• 7.181 LIONS Technology
• 7.182 LITE-ON Technology Corporation
• 7.183 LitePoint (Teradyne)
• 7.184 LS telcom
• 7.185 LTTS (L&T Technology Services)
• 7.186 LuxCarta
• 7.187 MantisNet
• 7.188 Marvell Technology
• 7.189 Mavenir
• 7.190 Meta
• 7.191 MicroNova
• 7.192 Microsoft Corporation
• 7.193 MikroTik
• 7.194 MitraStar Technology (Unizyx Holding Corporation)
• 7.195 Mobileum
• 7.196 Moso Networks
• 7.197 Mycom
• 7.198 Nash Technologies
• 7.199 Nearby Computing
• 7.200 NEC Corporation
• 7.201 Net AI
• 7.202 Netcracker Technology (NEC Corporation)
• 7.203 NETSCOUT Systems
• 7.204 Netsia (Argela)
• 7.205 NeuroRAN
• 7.206 Neutral Wireless
• 7.207 Neutroon Technologies
• 7.208 New H3C Technologies (Tsinghua Unigroup)
• 7.209 New Postcom Equipment
• 7.210 Nextivity
• 7.211 NextNav
• 7.212 Nokia
• 7.213 Novowi
• 7.214 NumoData
• 7.215 NuRAN Wireless
• 7.216 NVIDIA Corporation
• 7.217 NXP Semiconductors
• 7.218 Oceus Networks
• 7.219 ODC (ORAN Development Company)
• 7.220 Omega Wireless
• 7.221 OneLayer
• 7.222 Ookla
• 7.223 Opanga Networks
• 7.224 OREX (NTT DoCoMo)
• 7.225 Origami Networks
• 7.226 P.I. Works
• 7.227 Palo Alto Networks
• 7.228 Parallel Wireless
• 7.229 Pente Networks
• 7.230 Phluido
• 7.231 Picocom
• 7.232 Pivotal Commware
• 7.233 Polystar | Elisa Industriq
• 7.234 Potevio (CETC – China Electronics Technology Group Corporation)
• 7.235 QCT (Quanta Cloud Technology)
• 7.236 Qualcomm
• 7.237 Quanta Computer
• 7.238 RADCOM
• 7.239 Radisys (Reliance Industries)
• 7.240 Radware
• 7.241 Rakuten Symphony
• 7.242 RAN Flow
• 7.243 Ranlytics
• 7.244 Ranplan Wireless
• 7.245 Rebaca Technologies
• 7.246 Red Hat (IBM)
• 7.247 RED Technologies
• 7.248 REPLY
• 7.249 Ribbon Communications
• 7.250 RIMEDO Labs
• 7.251 Rivada Networks
• 7.252 Rockfish Data
• 7.253 Rohde & Schwarz
• 7.254 Ruijie Networks
• 7.255 RunEL
• 7.256 SageRAN (Guangzhou SageRAN Technology)
• 7.257 Samji Electronics
• 7.258 Samsung
• 7.259 Sasken Technologies
• 7.260 Satori Solutions
• 7.261 SE-RAN.ai
• 7.262 Sercomm Corporation
• 7.263 ServiceNow
• 7.264 Shabodi
• 7.265 Signalwing
• 7.266 SIRADEL
• 7.267 Skyfora
• 7.268 Skyvera (TelcoDR)
• 7.269 SmartViser
• 7.270 Snowflake
• 7.271 SOLiD
• 7.272 Solid State of Mind
• 7.273 Sooktha
• 7.274 Spectrum Effect
• 7.275 Spirent Communications (Keysight Technologies)
• 7.276 SRS (Software Radio Systems)
• 7.277 SSC (Shared Spectrum Company)
• 7.278 Star Solutions
• 7.279 Subex
• 7.280 Sunwave Communications
• 7.281 Supermicro (Super Micro Computer)
• 7.282 Sutherland
• 7.283 SynaXG Technologies
• 7.284 Systemics-PAB
• 7.285 T&W (Shenzhen Gongjin Electronics)
• 7.286 Tarana Wireless
• 7.287 TCS (Tata Consultancy Services)
• 7.288 TEASOL Technologies
• 7.289 Tech Mahindra
• 7.290 Tecore Networks
• 7.291 TECTWIN
• 7.292 Telchemy
• 7.293 Telrad Networks
• 7.294 TEOCO/Aircom
• 7.295 ThinkRF
• 7.296 TI (Texas Instruments)
• 7.297 Tiami Networks
• 7.298 TietoEVRY
• 7.299 Totogi
• 7.300 Tropico (CPQD – Center for Research and Development in Telecommunications, Brazil)
• 7.301 TTG International
• 7.302 Tupl
• 7.303 ULAK Communication
• 7.304 Unikie
• 7.305 Vavitel (Shenzhen Vavitel Technology)
• 7.306 Verveba Telecom
• 7.307 VHT (Viettel High Tech)
• 7.308 VIAVI Solutions
• 7.309 VMware (Broadcom)
• 7.310 VNL – Vihaan Networks Limited (Shyam Group)
• 7.311 Wave Electronics (Korea)
• 7.312 WDNA (Wireless DNA)
• 7.313 Weaver Labs
• 7.314 WIM Technologies
• 7.315 Wind River Systems
• 7.316 Wipro
• 7.317 WiSights Lab
• 7.318 Wiwynn (Wistron Corporation)
• 7.319 WNC (Wistron NeWeb Corporation)
• 7.320 Xingtera
• 7.321 ZaiNar
• 7.322 Z-Com
• 7.323 Zhone Technologies (Formerly DZS)
• 7.324 Zinkworks
• 7.325 ZTE
• 7.326 zTouch Networks
• 7.327 Zyxel (Unizyx Holding Corporation)

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 Traditional SON & Vendor-Specific RAN Automation
  • 8.3.2 SMO/RIC & Open RAN Automation
  • 8.3.3 RAN Planning & Optimization Tools
  • 8.3.4 Test & Measurement Solutions
• 8.4 Traditional SON & Vendor-Specific RAN Automation Submarkets
  • 8.4.1 SON & Proprietary Network-Level Automation Platforms
  • 8.4.2 AI-for-RAN Software Upgrades for Real-Time RAN Intelligence & Automation
• 8.5 SMO/RIC & 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
  • 8.5.5 dApps
• 8.6 Access Technology Generations
  • 8.6.1 LTE
  • 8.6.2 5G
  • 8.6.3 6G
• 8.7 Use Case Segments
  • 8.7.1 PCI, RACH & Configuration Automation
  • 8.7.2 Mobility, Handover & Load Management
  • 8.7.3 RAN Resource & Performance Optimization
  • 8.7.4 Energy Efficiency & Sustainability
  • 8.7.5 Spectrum Management & Multi-RAT Connectivity
  • 8.7.6 Network Healing & Protection
  • 8.7.7 Massive MIMO, Beamforming & Lower-Layer Optimization
  • 8.7.8 Network Slicing & Vertical Applications
  • 8.7.9 ISAC, Positioning & Tracking
  • 8.7.10 Network Planning & Evolution
  • 8.7.11 Automation & AI Enablement
  • 8.7.12 Multi-Domain Use Cases
• 8.8 Network Types
  • 8.8.1 Public Mobile Operator Networks
  • 8.8.2 NTN Infrastructure
  • 8.8.3 Private Cellular Networks
• 8.9 Supplier Categories
  • 8.9.1 RAN Vendors
  • 8.9.2 Third-Party Suppliers
• 8.10 Regional Segmentation
  • 8.10.1 North America
  • 8.10.2 Asia Pacific
  • 8.10.3 Europe
  • 8.10.4 Middle East & Africa
  • 8.10.5 Latin & Central America

9. Conclusion & Strategic Recommendations

• 9.1 Why is the Market Poised to Grow?
• 9.2 Future Roadmap: 2026 - 2040
  • 9.2.1 2026 - 2030: Scaling Network-Wide RAN Automation in Brownfield Networks
  • 9.2.2 2031 - 2035: AI-Native 6G RAN Operations, Real-Time Programmability & Cross-Domain Automation
  • 9.2.3 2036 - 2040: Towards Pervasive Autonomy & Zero-Touch L5 Network Operations
• 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 New Skills & Competencies for Optimization Engineers
• 9.6 Progressing Towards Higher Levels of Network Autonomy
• 9.7 Ongoing Shift From Legacy SON to Open RAN Automation
• 9.8 Moving Beyond SON Era Walled Garden Automation
• 9.9 Accelerating SMO/Non-RT RIC & rApp Adoption
• 9.10 Growing Interest in dApps & the E3 Interface
• 9.11 Embedded AI-for-RAN Software Upgrades
• 9.12 Longer-Term Evolution Towards AI-Native 6G Systems
• 9.13 Increasing Sophistication of Use Cases & AI/ML Algorithms
• 9.14 Strong Focus on Energy Efficiency & Sustainability
• 9.15 Towards Cross-Domain Automation
• 9.16 RAN Automation for Satellite NTN Integration
• 9.17 Agentic AI & Autonomous Private 5G Networks
• 9.18 Signs of Consolidation in the SMO/RIC Ecosystem
• 9.19 Diverse Community of d/x/rApp Developers
• 9.20 Operator-Backed Ventures & Academic Spinoffs
• 9.21 Which Suppliers Are Leading the Market?
• 9.22 Strategic Recommendations
  • 9.22.1 RAN Automation Solution Providers
  • 9.22.2 Mobile Operators