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As the telecommunications industry matures its 5G capabilities and advances toward 6G, the concept of Network-as-a-Service (NaaS) is evolving beyond flexible connectivity models to incorporate intelligent and dynamic network operations. A key enabler of this transformation is the integration of Artificial Intelligence (AI) into the Radio Access Network (RAN)—a shift that promises to unlock unprecedented levels of automation, agility, and performance.
In this article, Suyash Rai, TaTa Communications’ Senior Manager for Solution Engineering, discusses how AI-powered RAN is revolutionizing the NaaS paradigm in 5G and 6G architectures, and how service providers can harness this convergence to deliver scalable, efficient, and context-aware connectivity services.
RAN and NaaS: The Convergence of Connectivity and Intelligence
Traditional RAN systems were largely static and hardware-centric. The emergence of Open RAN (O-RAN), Cloud RAN (C-RAN), and Virtualized RAN (vRAN) laid the groundwork for programmability and disaggregation. However, these advances alone are insufficient for realizing the full potential of NaaS, which demands real-time adaptability and fine-grained service personalization.
Enter AI RAN—the infusion of AI/ML capabilities into RAN elements, particularly through the RAN Intelligent Controller (RIC). The combination of NaaS with AI-enhanced RAN enables:
- Dynamic slicing based on user demand and application profiles.
- Predictive resource allocation leveraging historical and real-time data.
- Self-optimizing networks that adjust RF parameters autonomously.
- Proactive fault detection and healing via anomaly detection models.
AI RAN Functional Components Supporting NaaS
The AI RAN architecture typically includes several functional elements that align with the NaaS model:
1. Near-Real-Time RIC (Near-RT RIC)
This component hosts xApps that use AI/ML models to optimize scheduling, mobility, and interference management in near real-time. These xApps can be dynamically instantiated and customized for different NaaS use cases. For example, AI-based traffic steering algorithms running within the Near-RT RIC can make microsecond-level decisions based on user mobility patterns, RF signal quality, and cell congestion metrics—helping deliver deterministic SLAs for mission-critical services.
2. Non-Real-Time RIC (Non-RT RIC)
Deployed at the service management level, the Non-RT RIC supports training, policy management, and orchestration of rApps. It feeds learning-based insights to the Near-RT RIC, enabling closed-loop optimization. The Non-RT RIC can integrate with external analytics engines or data lakes via standardized interfaces, enhancing flexibility in how policies are defined and enforced across tenant-specific service instances. This ensures alignment with NaaS tenants’ business objectives, such as latency-sensitive video delivery or IoT telemetry aggregation.
3. Data Layer and AI Model Pipeline
AI RAN depends on continuous data ingestion from user devices, network nodes, and OSS/BSS systems. Pipelines preprocess and feed this data into training and inference models, forming the digital backbone of NaaS operations. Technologies like distributed feature stores, edge inference runtimes, and model versioning systems are increasingly being used to ensure model reliability and traceability. Furthermore, adaptive model retraining based on real-time KPIs ensures resilience to changing network behaviors and user trends.
NaaS Use Cases Empowered by AI RAN
AI-driven RAN can enhance several service offerings within the NaaS framework:
Enterprise Private 5G
AI models detect usage patterns and allocate slices or edge compute dynamically. Real-time anomaly detection algorithms can trigger resource boosts for video surveillance feeds or automated machinery requiring ultra-reliable, low-latency connectivity.
Industrial IoT
AI RAN adjusts latency and throughput policies for URLLC applications on demand. Techniques like reinforcement learning enable continual policy tuning for machine-to-machine communication in harsh environments, improving predictive maintenance and minimizing unplanned downtime.
Network Monetization
Network slices can be tailored and priced based on real-time AI-driven QoS analytics. AI RAN enables operators to define slice templates that respond autonomously to user engagement levels or regional demand surges, enabling granular monetization models based on SLA enforcement.
Energy Efficiency-as-a-Service
AI reduces power consumption by predicting low-traffic windows and dynamically switching off resources. AI RAN integrates with power amplifiers and baseband units to enable sleep modes during off-peak hours without impacting service-level guarantees, supporting sustainable telecom goals.
Challenges
While promising, AI RAN deployment within NaaS models faces several challenges:
- Model generalization across diverse deployment environments.
- Data privacy and sovereignty in multi-tenant infrastructures.
- Interoperability across vendors and domains.
Future Outlook
Looking ahead to 6G, we anticipate:
- Native AI integration across all RAN and Core functions.
- Federated learning to preserve privacy while training across distributed data.
- Cognitive NaaS platforms that adapt to user intent and environmental context in real time.
Conclusion
The fusion of AI RAN with NaaS is not just an incremental improvement—it is a foundational shift toward intelligent, user-centric connectivity. As 5G networks evolve and 6G takes shape, operators that embrace AI at the RAN level will be best positioned to deliver differentiated, automated, and monetizable NaaS offerings.
By moving from static to smart RAN, the telecommunications industry is redefining what it means to offer “as-a-service” connectivity—not just as a product, but as an adaptive, intelligent experience.
Learn More
- Learn more about Mplify’s vision for NaaS and get the NaaS Industry Blueprint.
- Download the Mplify NaaS Customer Experience White Paper.
- Explore Mplify’s 5G service standards.
- Join us at Mplify’s next Global NaaS Event
Suyash Rai
Senior Manager, Solution Engineering | TaTa Communications