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Toward a Distributed AI Platform for 6G RAN

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Author: 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. These changes are driven by the need for telecom operators to find new ways to generate revenue while lowering high capital and operating costs. 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 a variety of locations. This approach makes it easier to manage your network lifecycle and accelerate the rollout of new features. 5G also introduces advanced technologies that encourage the use of open programmable interfaces, 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 potential of the network by solving complex problems caused by the increasing complexity of 5G and introducing new applications that provide unique value. In this emerging environment, AI emerges as a critical component, and advances in generative AI are attracting significant attention in the communications sector. AI’s ability to solve intractable problems such as pattern recognition, traffic prediction, and scheduling makes it an ideal solution to these and other long-standing RAN challenges. There is a growing consensus that future mobile networks must be AI-driven, and both industry and academia support this trend. However, practical hurdles such as collecting data from distributed sources and handling the diverse nature of AI RAN applications remain hurdles to overcome.

The essential role of AI in RAN

The need for AI in RAN is highlighted by its ability to optimize and enhance critical RAN functions such as network performance, spectrum utilization, and computing resource management. AI is used as an alternative to traditional optimization methods that struggle with explosive growth in search space due to complex scheduling, power control, and antenna allocation. Along with the infrastructure optimization challenges introduced by 5G (e.g. server failures, software bugs), AI shows promise through predictive maintenance and energy efficiency management, offering solutions to these problems that were previously unattainable. AI can also leverage open interfaces exposed by RAN capabilities to enable third-party applications to leverage valuable RAN data, enhancing capabilities for additional use cases such as user location and security.

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Distributed Edge Infrastructure and AI Deployment

As AI becomes increasingly integrated into the RAN, choosing the optimal deployment location is critical to performance. Deployment of AI applications in RAN depends on the location of the RAN infrastructure, from remote to cloud. Each location offers different computing power and has its own tradeoffs in terms of resource availability, bandwidth, latency, and privacy. These factors are important when determining the best place to deploy AI applications because they directly impact performance and responsiveness. For example, the cloud provides more computing resources, but it can also result in higher latency, which can be problematic for applications that require real-time data processing or fast decision-making.

Troubleshooting AI deployment in RAN

Deploying AI in RAN requires overcoming a variety of challenges, especially in the areas of data collection and application orchestration. The heterogeneity of AI application input features complicates data collection tasks. Exposing raw data from all potential sources is impractical due to the sheer volume of data being processed and transmitted. The current industry approach of leveraging standardized APIs for data collection is not always conducive to AI-based application development. The standard set of coarse-grained data sources exposed through these APIs often do not meet the nuanced requirements of AI-based RAN solutions. These limitations force developers to adapt their AI applications to the available data rather than collecting the data that best suits their application needs.

The challenge of coordinating AI RAN applications is equally difficult. The distributed nature of RAN infrastructure raises questions about where the various components of AI applications should be located. Asking these questions requires careful evaluation of your application’s compute requirements, response latency, privacy constraints, and the various compute capabilities of your infrastructure. Complexity is further amplified by the need to accommodate multiple AI applications, each competing for the same infrastructure resources. Developers often must manually deploy these applications across the RAN, a process that is not scalable and prevents widespread deployment in production environments.

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Vision for a distributed AI-based RAN platform

To address these challenges, we propose a vision for a distributed AI-based RAN platform designed to simplify AI application deployment. The platform is built on the principles of flexibility and scalability through a high-level architecture that includes dynamic data collection probes, an AI processor runtime, and an orchestrator that coordinates platform operations. The proposed platform is programmable probe This can be injected at various points in the platform and RAN network functions to collect data tailored to the needs of AI applications. This approach minimizes the amount of data and avoids delays associated with the standardization process.

The AI ​​processor runtime is a key component that enables flexible and seamless deployment of AI applications across your infrastructure. It abstracts the underlying computing resources and provides an environment for data collection, data exchange, execution, and lifecycle management. The runtime is designed to be deployed anywhere, from remote to the cloud, and to handle both AI RAN and non-RAN AI applications.

The orchestrator is the component that brings it all together and manages the deployment and migration of AI applications across different runtimes. We also consider developer needs and infrastructure capabilities to optimize the overall utility of the platform. The orchestrator is dynamic and can adapt to changes in resource availability and application requirements, and can incorporate a variety of policies to balance compute and network loads across the infrastructure.

When articulating the vision for a distributed AI-based RAN platform, it is important to clarify that the proposed framework does not impose any specific architectural implementation. Instead, it defines high-level APIs and configurations that form the backbone of the platform’s functionality. This includes a data collection API that facilitates data capture and input from a variety of sources, a data exchange API that allows data communication and transfer between the various components of the platform, and a lifecycle management API that oversees deployment, updates, and distribution. AI application retirement. 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 allows the platform to support a wide range of AI applications and adapt to the evolving hardware technology landscape.

Additionally, to demonstrate the feasibility and potential of the proposed platform, we internally prototyped a professional and efficient implementation of an AI processor specifically for the far edge. This prototype has been carefully designed to operate with fewer CPUs, optimizing resource usage while maintaining high performance. This demonstrates that AI processor runtime principles can be effectively implemented to meet the specific requirements of Phage, where resources are limited and real-time processing is critical. This specialized implementation exemplifies the targeted innovation emphasized by the platform and demonstrates how a flexible execution environment can be tailored to address specific challenges within the RAN ecosystem.

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Balancing open and closed architectures in RAN integration

The proposed AI platform is highly adaptable, making it suitable for open architectures compliant with O-RAN standards as well as proprietary designs controlled by RAN vendors. This flexibility allows for a variety of deployment scenarios, from fully O-RAN-compliant implementations that encourage third-party development, to completely proprietary models, or hybrid models that provide a balance between vendor control and innovation. In each scenario, decentralized AI platforms can be tailored to the specific needs of infrastructure providers or adhere to the guidelines of standardization bodies.

Conclusions on the future of AI in 6G RAN

Integrating AI into RAN is central to the 6G vision and has the potential to transform network management, performance optimization, and application support. Deploying AI solutions in RAN is challenging, but distributed AI-based platforms provide a path to overcome these obstacles. By fostering discussion about the architecture of the 6G AI platform, we can guide standards bodies and vendors in exploring AI integration opportunities. The proposed platform is intentionally flexible, allowing for customization to meet the diverse needs and constraints of different operators and suppliers.

The future of RAN depends on its ability to dynamically adapt to changing conditions and needs. AI is essential to this change, providing the intelligence and adaptability needed to manage the complexity of next-generation networks. As the industry evolves toward AI-powered 6G networks, it will be important to embrace both the challenges and opportunities that AI brings. The proposed distributed AI platform is an important step forward aimed at leveraging the full potential of RAN through intelligent, flexible and scalable solutions.

Innovation in AI and our commitment to AI-enabled RAN are key to ensuring that the telecom industry and future telecom networks are efficient, cost-effective, and capable of supporting advanced services and applications. Collaborative efforts from researchers and industry experts are essential to materialize this vision and make the potential of AI in 6G RAN a reality.

As the 6G era approaches, integrating AI into RAN architecture is not just an option, it is a necessity. The distributed AI platform described here serves as a blueprint for a future where AI is seamlessly integrated into the RAN to drive innovation and enhance the capabilities of cellular networks to meet the needs of the next generation of users and applications.

For more information full paper.

Acknowledgments

This project is partly funded by the UK Department for Science, Innovation and Technology (DSIT) under the Open Network Ecosystem Competition (ONE) programme.





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