Full-Stack RAN and AI

A new era for telecom

Telecom is changing rapidly. With the demand for faster and more reliable networks, Radio Access Networks (RAN) are becoming smarter and more flexible. Operators, service providers, and SaaS companies can now use edge AI to bring technology closer to users. By managing everything at the edge, including routing and billing, they can deliver efficient and customized solutions.

The Journey of RAN: Building toward intelligence

Traditional RAN: yesterday’s model

For decades, RAN was hardware-centric: siloed, specialized systems designed to keep the network running. It was mostly confined to the lower part of the communication stack (physical, link, network). But in today’s world of massive data demands and high customer expectations, integrated RAN stacks can change this, allowing operators to reduce costs and create new revenue streams.

The phases of RAN evolution

• Distributed RAN (dRAN): Physical cell-site model, largely hardware-bound.
• Virtualized RAN (vRAN): Software-driven, allowing some flexibility and scalability.
• Cloud RAN (C-RAN): Centralizes processing to lower overhead and simplify operations.
• Open RAN (O-RAN): Promotes interoperability with open interfaces and disaggregated components. Key to O-RAN is the RAN Intelligent Controller (RIC), which uses analytics and control loops for optimized resource allocation in real time.
• AI-RAN: The next leap—embedding AI at the edge with a full-stack approach up to the application level. By whitelabeling AI services and managing costs through advanced routing and back-billing, operators and service providers can deliver intelligent, efficient, and scalable solutions.

AI-RAN isn’t just an upgrade. It’s a fundamental shift that allows machine learning models to optimize networks in real-time, improving performance and efficiency. This transformation extends beyond traditional telecom functions, opening new opportunities for operators to offer AI-driven services.

With AI running at the edge, latency is minimized, making applications like real-time analytics, smart automation, and interactive experiences more responsive and reliable. Additionally, integrating AI into the full-stack RAN infrastructure enables advanced routing, cost-efficient billing models, and enhanced scalability, benefiting telecom providers, enterprises, and end-users alike.

Full-Stack RAN

A full-stack RAN weaves together hardware, software, and orchestration into one cohesive environment—ideal for AI-heavy applications. This architecture helps combine traditional network workloads with the demands of cutting-edge AI-driven services. Key components include:

1. Advanced hardware: High-performance GPUs (e.g., NVIDIA GH200) tailored to run large AI workloads with minimal latency. These GPUs can dynamically switch between advanced 5G/6G beamforming, radio modeling and optimization, and SaaS edge AI services, making them a versatile asset in modern telecom infrastructure.
2. Software-Defined flexibility: Fully virtualized RAN components (Layers 1 to 3), supporting dynamic scaling and easy updates. This software-driven approach enables seamless integration of AI services at the edge while optimizing radio performance. This flexibility allows operators to manage network functions efficiently while delivering AI-driven applications with minimal latency and maximum scalability.

By integrating all these elements, a full-stack RAN paves the way for new edge services, such as real-time AR/VR, autonomous systems, private 5G implementations, and AI inferencing where low latency is mission-critical.

AI as a whitelabel edge application

Whitelabel AI applications enable telecom operators to provide an AI-ready platform at the network edge, allowing enterprises and service providers to launch branded solutions without deploying their own physical infrastructure. These edge AI apps deliver lower latency and higher reliability, essential for scenarios like autonomous vehicles, robotics, and real-time data analytics.

This is how you could execute this concept:

1. Shared resources: Operators offer AI-optimized infrastructure (e.g., AITRAS: AI-based Telecom RAN System) that can run diverse workloads, ranging from SaaS to RAN modeling and optimization
2. Cloud collaboration: Service providers (AWS, Google Cloud, Azure) host AI apps on this infrastructure, offloading the need to invest in specialized on-prem GPU clusters.
3. Revenue sharing: Operators, OEMs, and cloud providers split profits from these edge AI services, creating a sustainable business model for all parties.

Practical example: Consider an airline that wants to enhance passenger experiences with AI throughout the travel journey. By leveraging a whitelabel AI solution from a telecom operator, the airline can offer intelligent services such as personalized travel assistance, real-time notifications, and seamless connectivity.

Running on the operator’s edge nodes, this AI can function during flights, at the airport, or upon arrival—especially when passengers lack active data connections due to roaming delays. This setup reduces latency, improves service accuracy, and eliminates the need for complex data-center infrastructure, benefiting both the airline and the telecom provider.

Why it matters? What are the benefits of AI in a RAN

For telecom operators

• New Revenue Streams
• Optimized Resource Utilization
• Scalable and Future-Proof

AI enables telecom operators to generate new revenue streams by offering AI-based services such as predictive analytics, AR/VR delivery, and robotics control. It optimizes resource utilization by allowing multi-tenant systems to handle multiple AI workloads simultaneously without degrading network performance. Additionally, AI makes networks more scalable and future-proof, enabling quick adaptation to traffic surges or emerging applications through virtualization and intelligent orchestration.

For cloud providers

• Lower costs and faster response times
• More AI-powered services
• Stronger partnerships with telecom companies

By processing AI tasks closer to users instead of in distant data centers, cloud providers save on data transfer costs and make apps run faster. They can also offer more useful AI services, like chatbots, image recognition, and smart recommendations, right where they're needed. Partnering with telecom companies helps them reach new customers and expand into different industries, making their services more widely available.

For enterprises

• Faster performance for real-time applications
• Lower costs by letting others handle AI infrastructure
• Quicker and easier rollout of new AI features

Businesses that rely on real-time AI, like self-driving cars, smart factories, or interactive AR/VR, get faster and more reliable performance. Instead of buying and maintaining expensive AI hardware, they can let telecom providers handle it, saving money and reducing hassle. This also makes it easier to add new AI-powered features without long setup times, helping companies stay ahead of the competition.

Monetizing AI with OEM partnerships

The billing framework

Operators can monetize their infrastructure through models such as:

• Resource-Based Fees: Charging for GPU/CPU time, storage, or bandwidth consumption.
• Dynamic Pricing: Adjusting rates as workloads scale.
• Profit Sharing: Splitting revenues from AI applications among ecosystem players

A practical scenario

An OEM (e.g., Dell, Huawei) integrates NVIDIA-powered infrastructure to support Azure-based AI (Microsoft) services. As these services run at the edge for a range of customers, the operator (STC) collects usage fees for GPU time and splits revenue with both the OEM (for hardware usage) and the cloud provider (for the AI platform), creating a multi-way revenue model.

Examples of AI applications at the edge

• Self-driving cars
• Smart robots
• AR/VR for city planning
• AI-powered chatbots
• Private 5G networks

Self-driving cars react instantly to avoid accidents by processing information on the spot instead of relying on distant servers. Smart robots recognize objects and respond in real time, making them more useful in warehouses, factories, and homes. AR/VR tools help city planners visualize new roads, buildings, and traffic patterns interactively. AI-powered chatbots answer customer questions immediately while keeping private information secure. Private 5G networks let businesses run AI applications smoothly and securely without interruptions.

The Road Ahead: AI-Driven Telecom

The fusion of AI and RAN is setting the stage for telecom networks to become innovation platforms rather than mere pipes. Operators who invest in AI as a whitelabel application can tap into new markets, power advanced edge computing scenarios, and future-proof their networks for the demands of tomorrow.

The real question isn’t whether the industry will embrace AI-RAN—it’s how quickly operators can deploy it. With the right partnerships, robust orchestration, and a clear vision, the possibilities for groundbreaking services and transformative user experiences are limitless.