Toward a Distributed AI Platform for 6G RAN

by Ganesh Ananthanarayanan, Xenofon Foukas, Bozidar Radunovic, Yongguang Zhang

 

Introduction to the Evolution of RAN

The development of Cellular Radio Access Networks (RAN) has reached a critical point with the transition to 5G and beyond. This shift is motivated by the need for telecommunications operators to lower their high capital and operating costs while also finding new ways to generate revenue. The introduction of 5G has transformed traditional, monolithic base stations by breaking them down into separate, virtualized components that can be deployed on standard, off-the-shelf hardware in various locations. This approach makes it easier to manage the network’s lifecycle and accelerates the release of new features. Additionally, 5G has promoted the use of open and programmable interfaces and introduced advanced technologies that expand network capacity and support a wide range of applications.

As we enter the era of 5G Advanced and 6G networks, the goal is to maximize the network's potential by solving the complex issues brought by the added complexity of 5G and introducing new applications that offer unique value. In this emerging landscape, AI stands out as a critical component, with advances in generative AI drawing significant interest from the telecommunications sector. AI's proficiency in pattern recognition, traffic prediction, and solving intractable problems like scheduling makes it an ideal solution for these and many other longstanding RAN challenges. There is a growing consensus that future mobile networks should be AI-native, with both industry and academia offering support for this trend. However, practical hurdles like data collection from distributed sources and handling the diverse characteristics of AI RAN applications remain obstacles to be overcome.

 

The Indispensable Role of AI in RAN

The need for AI in RAN is underscored by AI’s ability to optimize and enhance critical RAN functions like network performance, spectrum utilization, and compute resource management. AI serves as an alternative to traditional optimization methods, which struggle to cope with the explosion of search space due to complex scheduling, power control, and antenna assignments. With the infrastructure optimization problems introduced by 5G (e.g. server failures, software bugs), AI shows promise through predictive maintenance and energy efficiency management, presenting solutions to these challenges that were previously unattainable. Moreover, AI can leverage the open interfaces exposed by RAN functions, enabling third-party applications to tap into valuable RAN data, enhancing capabilities for additional use cases like user localization and security.

 

 

Distributed Edge Infrastructure and AI Deployment

As AI becomes increasingly integrated into RAN, choosing the optimal deployment location is crucial for performance. The deployment of AI applications in RAN depends on where the RAN infrastructure is located, ranging from the far edge to the cloud. Each location offers different computing power and has its own trade-offs in resource availability, bandwidth, latency, and privacy. These factors are important when deciding the best place to deploy AI applications, as they directly affect performance and responsiveness. For example, while the cloud provides more computing resources, it may also cause higher latency, which can be problematic for applications that need real-time data processing or quick decision-making. 

 

Addressing the Challenges of Deploying AI in RAN

Deploying AI in RAN involves overcoming various challenges, particularly in the areas of data collection and application orchestration. The heterogeneity of AI applications' input features makes data collection a complex task. Exposing raw data from all potential sources isn't practical, as it would result in an overwhelming volume of data to be processed and transmitted. The current industry approach of utilizing standardized APIs for data collection is not always conducive to the development of AI-native applications. The standard set of coarse-grained data sources exposed through these APIs often fail to meet the nuanced requirements of AI-driven RAN solutions. This limitation forces developers to adapt their AI applications to the available data rather than collecting the data that would best serve the application's needs.

 

The challenge of orchestrating AI RAN applications is equally daunting. The dispersed nature of the RAN infrastructure raises questions about where the various components of an AI application should reside. These questions require a careful assessment of the application's compute requirements, response latency, privacy constraints, and the varied compute capabilities of the infrastructure. The complexity is further amplified by the need to accommodate multiple AI applications, each vying for the same infrastructure resources. Developers are often required to manually distribute these applications across the RAN, a process that is not scalable and hinders widespread deployment in production environments.

 

 

A Vision for a Distributed AI-Native RAN Platform

To address these challenges, we propose a vision for a distributed AI-native RAN platform that is designed to streamline the deployment of AI applications. This platform is built on the principles of flexibility and scalability, with a high-level architecture that includes dynamic data collection probes, AI processor runtimes, and an orchestrator that coordinates the platform's operations. The proposed platform introduces programmable probes that can be injected at various points in the platform and RAN network functions to collect data tailored to the AI application's requirements. This approach minimizes data volume and avoids delays associated with standardization processes.

The AI processor runtime is a pivotal component that allows for the flexible and seamless deployment of AI applications across the infrastructure. It abstracts the underlying compute resources and provides an environment for data ingestion, data exchange, execution, and lifecycle management. The runtime is designed to be deployed at any location, from the far edge to the cloud, and to handle both AI RAN and non-RAN AI applications.

The orchestrator is the component that brings all this together, managing the placement and migration of AI applications across various runtimes. It also considers the developer's requirements and the infrastructure's capabilities to optimize the overall utility of the platform. The orchestrator is dynamic, capable of adapting to changes in resource availability and application demands, and can incorporate various policies that balance compute and network load across the infrastructure.

 

In articulating the vision for a Distributed AI-Native RAN platform, it is important to clarify that the proposed framework does not impose a specific architectural implementation. Instead, it defines high-level APIs and constructs that form the backbone of the platform's functionality. These include a data ingestion API that facilitates the capture and input of data from various sources, a data exchange API that allows for the communication and transfer of data between different components of the platform, and a lifecycle management API that oversees the deployment, updating, and decommissioning of AI applications. The execution environment within the platform is designed to be flexible, promoting innovation and compatibility with major hardware architectures such as CPUs and GPUs. This flexibility ensures that the platform can support a wide range of AI applications and adapt to the evolving landscape of hardware technologies.

Moreover, to demonstrate the feasibility and potential of the proposed platform, we have internally prototyped a specialized and efficient implementation of the AI processor, particularly for the far edge. This prototype is carefully designed to work with fewer CPUs, optimizing resource use while maintaining high performance. It demonstrates that the AI processor runtime principles can be implemented effectively to meet the specific needs of the far edge, where resources are limited and real-time processing is crucial. This specialized implementation exemplifies the targeted innovation that the platform emphasizes, showcasing how the flexible execution environment can be tailored to address specific challenges within the RAN ecosystem.

 

 

Balancing Open and Closed Architectures in RAN Integration

The proposed AI platform is adaptable, capable of fitting into open architectures that adhere to O-RAN standards as well as proprietary designs controlled by RAN vendors. This flexibility allows for a range of deployment scenarios, from a fully O-RAN compliant implementation that encourages third-party development to a fully proprietary model, or to a hybrid model that offers a balance between vendor control and innovation. In each scenario, the distributed AI platform can be customized to suit the specific needs of the infrastructure provider or adhere to the guidelines of standardization bodies.

 

Concluding Thoughts on AI's Future in 6G RAN

The integration of AI into the RAN is central to the 6G vision, with the potential to transform network management, performance optimization, and application support. While deploying AI solutions in RAN presents challenges, a distributed AI-native platform offers a pathway to overcome these obstacles. By fostering discussions around the architecture of a 6G AI platform, we can guide standards bodies and vendors in exploring opportunities for AI integration. The proposed platform is intentionally flexible, allowing for customization to meet the diverse needs and constraints of different operators and vendors.

The future of RAN will depend on its ability to dynamically adapt to changing conditions and demands. AI is essential to this transformation, providing the intelligence and adaptability needed to manage the complexity of next-generation networks. As the industry progresses towards AI-native 6G networks, embracing both the challenges and opportunities that AI brings will be crucial. The proposed distributed AI platform marks a significant step forward, aiming to unlock the full potential of RAN through intelligent, flexible, and scalable solutions.

Innovation in AI and the commitment to an AI-native RAN are key to ensuring the telecommunications industry and the telecommunications networks of the future are efficient, cost-effective, and capable of supporting advanced services and applications. Collaborative efforts from researchers and industry experts will be vital in refining this vision and making the potential of AI in 6G RAN a reality.

 

As we approach the 6G era, integrating AI into RAN architectures is not merely an option but a necessity. The distributed AI platform outlined here serves as a blueprint for the future, where AI is seamlessly integrated into RAN, driving innovation and enhancing the capabilities of cellular networks to meet the demands of next-generation users and applications. 

 

For more details, please check the full paper.

 

Acknowledgements

The project is partially funded by the UK Department for Science, Innovation & Technology (DSIT) under Open Network Ecosystem Competition (ONE) programme.