Is AI taking over the RAN_Registration

AI is everywhere and nowhere in the RAN. It is pervasive and yet elusive. We know that eventually AI will permeate the entire RAN, but we are far from this. Wide commercial deployments are still a rarity, with AI technology selectively used for specific tasks. AI can bring huge cost savings, and higher performance and power efficiency mostly through automation, but it is not yet clear what the integration path will be, and everybody in the industry (not just operators) struggles to find the right strategy. How aggressive should the transition to AI RAN be? How should we treat the legacy infrastructure? How should we change operations, organizational structure and culture to integrate AI in the RAN and, more generally, in mobile networks? How disruptive will the introduction of AI be? What’s the best way to prepare for AI and its attending challenges and risks? How should we change our approach to security, sovereignty and privacy? Will AI deliver the boost in revenues operators have been unable to get with 4G and 5G, and are not too confident that 6G will deliver?

AI will have a transformative impact on the RAN, and yet the function of the RAN will not change. The challenges will be the same that we have today, but they will be amplified. Security, for example, has always been a major concern, but AI increases the risk of security breaches. On a positive side, it also protects networks more effectively from security threats.

AI will increase complexity, but it also enables more streamlined operations and higher automation. Operators will be able to launch new services and benefit from greater efficiency, but it is unclear how fast the transition can (or should) be.

There are still many open questions that have to be addressed to chart a successful path to AI-native, fully automated RAN. There is no prescriptive that works for everyone. Each ecosystem player will have to find its own answers based on its specific context and requirements. 

As always, I followed announcements and attended analysts events in preparation for this report, but the main source for what I present here is countless informal conversations I had with those actively working on AI in the RAN—from startups to tier-one vendors, operators worldwide, researchers and experts—which were informed by my graduate research in and teaching AI and cognitive science, at a time when AI was a mostly academic endeavor and which gave me some practical appreciation of the complexity and power of AI. The result is a report that presents a somewhat tangential perspective on AI RAN, centered on how to develop an AI RAN strategy, how to identify and address the unique challenges and opportunities, and how to get to a ROI-positive AI-RAN.