From the newsletter (September 25; sign-up or follow if you want it sooner): Rakuten Mobile and T-Mobile offer tangible AI-RAN examples, embedding intelligence in network automation and distributed compute. Their closed-loop systems point toward practical AI-native RAN, beyond the usual AI-RAN hype, with human-defined guardrails.
More AI-RAN shenanigans, following some of the 101 stuff from yesterday about AI for RAN and RAN for AI (or whatever it is called), and how it is all becoming more tangible. In another write-up from Intelligent RAN Forum earlier this week (available here), Rakuten Mobile says it is using its celebrated virtualized open RAN foundation to automate network operations, with more than 20% energy savings from a closed-loop system that has achieved ‘level-four’ validation from TM Forum. It is also working with Intel to put AI inference alongside its vRAN workloads – rather than shipping everything back to a central data center.
Which is a glimpse of the future, really – of RAN for AI, or AI on RAN, or networks for AI (etc etc); using the network’s distributed compute environment for inference at the edge. Meanwhile, T-Mobile is approaching the problem differently in the US, adding AI to its self-organizing network (SON) set-up. (Someone said on social media this week that AI-RAN is like ‘SON-on-steroids’, related to coverage of Orange’s AI-RAN work with Nokia.) T-Mobile’s so-called AutoPilot solution uses intent-based automation to adjust neighboring cells when sites fail; its Dynamic CX tech predicts demand and adjusts capacity ahead of major events.
T-Mobile says AutoPilot can make network adjustments in half the time, and its SON solution made 30,000 antenna adjustments during a January winter storm (Fern). Both good cases for intelligence inside automation loops, within human-defined guardrails. Which feels closer to AI-native RAN than the most of the usual hype. See also Optus’ AI-RAN case study with Ericsson from yesterday (September 24).
