AI is increasingly moving from the applications and data centers that power it into the networks that connect them. A new trial by SoftBank and Ericsson in Japan offers a concrete example, with AI being applied directly to a commercial 5G radio access network in real time.
The companies said their validation of Ericsson’s AI-native Scheduler for Link Adaptation delivered gains of up to approximately 25% in spectral efficiency and approximately 50% in downlink user throughput, compared with conventional technology. Across the evaluated locations, both metrics improved by approximately 10% on average.
The significance goes beyond the performance figures. Rather than using AI simply to analyze network data or support operations, the trial applied an AI model directly within the RAN, allowing it to respond to changing radio conditions and optimize link parameters in real time.
That approach could become increasingly relevant as mobile networks face new traffic patterns driven by generative AI, autonomous agents, and other emerging applications. The challenge is no longer simply adding capacity, but making better use of existing network resources as demand becomes more variable and complex.
The SoftBank-Ericsson trial also illustrates a broader point about the AI infrastructure race: the technology stack is changing rapidly, often in places that were previously considered relatively mature. From data center optics and AI computing to antennas and now the RAN itself, established infrastructure is being redesigned around AI.
That raises a bigger question about how operators and infrastructure providers should plan when the pace of technological change itself is becoming harder to predict. In today’s newsletter, Nokia’s Mike Bushong explores that question in “Planning for next: Black swans during an AI frenzy,” examining how the intensity of the AI investment cycle could itself create new sources of disruption.
Juan Pedro Tomas
Editor
RCR Wireless News
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