Enterprise AI Analysis
A Review of Open-RAN Intelligence: Opportunities, Challenges, Real-Life Applications and Impacts
The dynamic nature of modern network settings presents challenges for traditional base stations, leading to inefficient resource utilisation. These issues are exacerbated by the unpredictability of network scenarios, such as sudden surges in user demand or unforeseen hardware issues. Additionally, the increasing number of IoT devices and their diverse requirements necessitate the development of more flexible and intelligent systems. This need extends to various telecommunication network applications, including smart cities and vehicular systems. Optimising performance, especially in light of escalating data requirements and the demand for real-time responsiveness, poses significant challenges for traditional base stations. Thus, adopting an intelligent and flexible mobile network is imperative to address these challenges. The intelligent system should include a software layer capable of adaptation and learning, incorporating artificial intelligence (AI) and machine learning (ML) to enhance resource efficiency, adapt to dynamic conditions, and effectively handle complex scenarios, thereby optimising communication networks.
Published Date: 04 February 2026
Executive Impact Snapshot
Key performance metrics and findings from the comprehensive analysis.
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Latency Reduction
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Throughput Boost
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Resource Utilization
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Anomaly Detection Accuracy
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
AI/ML Capabilities
Challenges
Real-Life Applications
Impacts
AI/ML Capabilities Overview
Open-RAN leverages a wide array of AI/ML models including supervised, unsupervised, deep learning (FNN, CNN, RNN), reinforcement learning (RL, FL, GA), and probabilistic methods (Bayesian Networks, Fuzzy Logic, Quantum Computing) to enhance network efficiency, resource allocation, and predictive maintenance. These capabilities are crucial for adapting to dynamic network conditions and managing complex scenarios.
94%
Accuracy achieved by Neural Networks combined with xAPPs for predicting network failures. (Source: Section 4)
49%
Throughput increase in traffic steering and handover using Deep Q-Network. (Source: Section 4, Table 5)
97%
Intersection over Union (IoU) accuracy for radar detection using YOLOv3. (Source: Section 4, Table 5)
15000
Accepted resource requests without specific benchmarks using Reinforcement Learning and Double Deep Q-network. (Source: Section 4, Table 5)
| RIC | Architecture | E2 Interface | Interoperability | Enhancements | Outcomes |
|---|---|---|---|---|---|
| Nokia | Cloud-Centric | ✓ | ✓ | Spectrum Efficiency, Dynamic Traffic Management | Trials with AT&T, Improved Spectrum Efficiency |
| SK Telecom | Cloud-Centric | ✗ | ✓ | Digital Twin Testing | Significant Improvements in Traffic Management, Testing Platform |
| VIAVI | Cloud-Centric | ✓ | ✓ | Spectrum Efficiency, Dynamic Traffic Management | PoC Trials with Vodafone, Doubled 5G Capacity |
| VMware | Cloud + Edge | ✓ | ✓ | MU-MIMO Scheduler Offloaded to RIC | Trial with Juniper, 30% Downtime Reduction, 25% Maintenance Cost Decrease |
| Deutsche Telekom | Cloud + Edge | ✓ | ✗ | Predictive AI, Closed-Loop Automation | Reduced Power Consumption |
| Rakuten Mobile | Cloud + Edge | ✓ | ✓ | Dynamic Traffic Steering, Interference Management | Enhanced Throughput, Reduced Latency |
| NTT DOCOMO | Cloud + Edge | ✓ | ✓ | Policy Management, Network Slicing | Improved Throughput and Reduced Latency |
| Ericsson | Cloud + Edge | ✓ | ✓ | DSS, COMP, Beamforming Optimization | Enhanced Throughput, Reduced Latency |
| Samsung | Cloud-Centric | ✓ | ✓ | AI-Driven RAN Optimization, Network Automation | Improved Traffic Management, Enhanced Service Quality |
Improved
Connection management with NN and DRL leads to improved throughput, coverage, and load balancing. (Source: Section 4, Table 5)
Open-RAN Intelligence Implementation Roadmap
Phase 1: Foundation & Data Collection
Establish Open-RAN architecture, secure open interfaces (A1, E2, O1) for continuous data exchange. Implement robust data preprocessing techniques for quality and availability.
Duration: 3-6 Months
Phase 2: AI/ML Model Deployment (xApps/rApps)
Deploy lightweight AI/ML models (e.g., NNs, RL) for near-real-time RIC tasks (beamforming, mobility management) and non-real-time RIC tasks (model training, orchestration).
Duration: 6-12 Months
Phase 3: Integration & Interoperability
Ensure seamless integration of multi-vendor components. Leverage Federated Learning for collaborative optimization while maintaining data privacy. Focus on standardisation efforts for common APIs and data exchange formats.
Duration: 9-15 Months
Phase 4: Advanced Capabilities & Explainable AI
Explore advanced features like Generative AI/LLMs for policy synthesis and troubleshooting. Integrate Explainable AI (XAI) techniques to build trust and transparency in intelligent decisions.
Duration: 12-24 Months
Phase 5: Continuous Learning & Optimization
Implement continuous learning strategies (e.g., online learning, incremental learning) to adapt models to evolving network dynamics. Establish robust monitoring and feedback loops for ongoing performance refinement.
Duration: Ongoing
Challenges Overview
Despite significant advancements, AI-driven Open-RAN faces several challenges including generalizability across heterogeneous RAN environments, scarcity of high-quality labelled datasets, opacity of deep learning models, training complexity, inference latency for near-RT tasks, privacy and compliance issues, limited empirical data for benchmarking, and lack of standardised metrics.
Article Structure Flow
Section I: Introduction
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Section II: Overview of O-RAN
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Section III: SLR Framework for O-RAN Intelligence
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Section IV: SLR Results and Discussion
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Section V: A road map for O-RAN Intelligence
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Section VI: Real-world industrial Use-cases
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Section VII: Advantages and Challenges
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Section VIII: Conclusion
30s
Processing time for detecting anomalies using Soda, OminAnomaly, and IF models. (Source: Section 4, Table 5)
Not specified directly
Handover prediction uses LSTM, SVM, MLP, KNN, RF but specific accuracy not provided in Table 5. (Source: Section 4, Table 5)
Real-Life Applications Overview
Open-RAN intelligence, particularly through RIC and AI/ML, finds real-world application across various sectors including Manufacturing (IIoT), Energy Management, Healthcare, Supply Chain, Agriculture, Smart Cities, and Unmanned Aerial Vehicles (UAVs). These applications benefit from dynamic network reconfiguration, predictive analytics, and optimised resource allocation.
Manufacturing Optimization with Open-RAN & AI
Key Benefit: Enhanced Production Efficiency & Reduced Downtime
Industrial IoT (IIoT) and Open-RAN offer significant benefits for manufacturing by integrating diverse sensors into a unified network. Data collected by these sensors is processed by ML within the RIC, using predictive algorithms to anticipate potential machinery issues. This enables proactive maintenance, dynamic optimisation of production data based on demand fluctuations, and enhanced human-robot collaboration safety.
Technologies: Industrial IoT (IIoT), Open-RAN, ML in RIC, Predictive Algorithms
Source: Section 6.1
Smart Cities Traffic Management
Key Benefit: Reduced Congestion & Improved Traffic Flow
In smart cities, Open-RAN plays a crucial role in advanced real-time traffic management. Intelligent sensors on roadways gather precise traffic flow data, which is analyzed by sophisticated ML algorithms within the RIC to forecast traffic trends and dynamically optimize signal timings. This proactive approach not only reduces congestion and improves traffic flow efficiency but also enhances safety and supports environmental sustainability.
Technologies: Intelligent Sensors, ML Algorithms, RIC, Dynamic Signal Optimization
Source: Section 6.6
UAV-Based Radio Access Networks (U-RANs)
Key Benefit: Dynamic Adaptability & Extended Coverage
UAVs are integrated into cellular communication systems as flying base stations or relay stations, offering dynamic adaptability to environmental changes and extended coverage. ML tools aid in U-RAN design by incorporating application-specific considerations such as optimal UAV type selection, Doppler effects, dynamic positioning, interference management, and load balancing, which are challenging for conventional model-based approaches.
Technologies: UAVs, ML Tools, Open-RAN, Dynamic Positioning, Interference Management
Source: Section 6.7
Agriculture: Smart Farming with Open-RAN IIoT
Key Benefit: Enhanced Crop Yield & Resource Optimization
Open-RAN plays a crucial role in agricultural IIoT by ensuring connectivity over expansive farms. Field-based intelligent sensors gather data on crop health and soil conditions, which ML algorithms within the RIC analyze to provide recommendations for targeted treatments and watering schedules. This enables data-driven decisions, enhancing crop yield and quality while minimizing resource wastage.
Technologies: IIoT, Open-RAN, Intelligent Sensors, ML Algorithms in RIC
Source: Section 6.5
Impacts Overview
The integration of intelligence into Open-RAN has profound societal, environmental, and economic impacts. Societally, it improves connectivity in remote areas, narrowing the digital divide. Environmentally, it promotes sustainability through adaptive power control and efficient resource usage, reducing carbon footprint. Economically, it offers long-term cost reduction, fosters innovation, and enhances competition within the telecommunications ecosystem.
| Features | C-RAN | V-RAN | Open-RAN |
|---|---|---|---|
| Network management | C | C and D | C and D |
| Virtualisation |
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| Decoupling data and control planes |
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| Vendor lock-in |
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| Operational cost | H | L | L |
| Energy consumption | M | L | H |
| Latency | H | L | H |
| AI interface |
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| Software interface |
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| Open-source |
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| Real-time |
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Calculate Your Potential ROI
Estimate the efficiency gains and cost savings AI-driven Open-RAN can bring to your enterprise.
Annual Savings
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Hours Reclaimed Annually
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Your AI-Driven Open-RAN Roadmap
Navigate the journey of integrating intelligence into your Open-RAN infrastructure with our strategic roadmap.
Phase 1: Foundation & Data Collection
Establish Open-RAN architecture, secure open interfaces (A1, E2, O1) for continuous data exchange. Implement robust data preprocessing techniques for quality and availability.
Duration: 3-6 Months
Phase 2: AI/ML Model Deployment (xApps/rApps)
Deploy lightweight AI/ML models (e.g., NNs, RL) for near-real-time RIC tasks (beamforming, mobility management) and non-real-time RIC tasks (model training, orchestration).
Duration: 6-12 Months
Phase 3: Integration & Interoperability
Ensure seamless integration of multi-vendor components. Leverage Federated Learning for collaborative optimization while maintaining data privacy. Focus on standardisation efforts for common APIs and data exchange formats.
Duration: 9-15 Months
Phase 4: Advanced Capabilities & Explainable AI
Explore advanced features like Generative AI/LLMs for policy synthesis and troubleshooting. Integrate Explainable AI (XAI) techniques to build trust and transparency in intelligent decisions.
Duration: 12-24 Months
Phase 5: Continuous Learning & Optimization
Implement continuous learning strategies (e.g., online learning, incremental learning) to adapt models to evolving network dynamics. Establish robust monitoring and feedback loops for ongoing performance refinement.
Duration: Ongoing
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