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Open and Cloud RAN re creating the programmable foundation for AI-RAN, with spectral efficiency, automation and orchestration emerging as key steps toward AI-native 6G networks
The radio access network (RAN) is entering a period of architectural convergence as AI-RAN takes shape. Open RAN, cloud-native infrastructure and artificial intelligence (AI) have largely developed as separate industry movements, but they are increasingly becoming components of the same transition toward a more programmable, adaptive and software-driven RAN.
That makes the current AI-RAN cycle the latest stage in a much longer evolution of network automation. Self-organizing networks provide useful historical context. SON developed alongside LTE as operators sought to automate optimization and reduce manual intervention across increasingly complex radio networks. By the middle of the last decade, machine learning algorithms were already being incorporated into SON functions, extending optimization beyond static, rules-based approaches.
Stéphane Téral, founder and chief analyst at Téral Research, described the longstanding objective as creating systems that “minimize human interaction to the lowest level possible.”
Open RAN subsequently expanded the architectural surface available for that intelligence. Disaggregating hardware and software, opening interfaces and introducing the RAN Intelligent Controller created new opportunities for supplier interoperability and software-based innovation through rApps and xApps.
The significance of openness, however, extends beyond building a RAN with the largest possible number of vendors. It creates architectural optionality: the ability to introduce different radios, software and applications where they deliver value.
Cloud RAN pushes the same transition further by virtualizing and containerizing baseband functions and moving them onto increasingly common compute infrastructure. As Téral put it: “You start by disaggregating software from hardware. Then you want to run functions on virtual machines. At some point, you containerize and then you move to the cloud.”
This is where the different technology trajectories begin to reinforce one another. Open interfaces create programmability. Cloud-native infrastructure increases software velocity and deployment flexibility. AI increases the sophistication of the decisions that software can make.
The near-term AI-RAN opportunity is centered primarily on improving the performance of the RAN itself. AI-driven algorithms can increase spectral efficiency, optimize network resources and support more advanced automation. Operators are also applying AI to root-cause analysis, intent-based optimization and closed-loop network operations.
But AI-RAN does not necessarily mean deploying GPUs at every cell site. Compute architecture will depend on workload, geography, latency requirements and economics. The relevant question is where additional intelligence produces enough performance or operational value to justify the infrastructure required to support it.
That question becomes more complicated as the industry moves toward shared AI and RAN infrastructure. Over time, radio workloads and AI applications could operate on common distributed compute platforms, extending intelligence closer to users and devices.
That makes orchestration one of the critical problems ahead. Operators will need to determine what workloads run where, how compute capacity is allocated and how increasingly heterogeneous infrastructure can be managed without overwhelming operational complexity.
This is also where today’s AI-RAN work begins to point toward AI-native 6G. The longer-term destination is not simply a RAN with more AI applications layered on top. It is a network in which connectivity, compute, software and intelligence are increasingly coordinated as a single system—capable of observing conditions, interpreting intent and continuously adapting resources around network, business and customer outcomes.
