T-Mobile US is testing AI to allocate radio resources on its 5G-Advanced network, with Ericsson trial results showing the biggest gains during peak traffic.

The operator is trialling AI-Managed Beamforming on live commercial traffic at scale. Chief network officer Ankur Kapoor (pictured) outlined the results in a blog post. The work focuses on sounding reference signals (SRS), short transmissions from phones that help the network measure the path between a device and a cell site and direct beams more accurately.

Due to those resources being limited, they have traditionally been allocated under fixed rules. T-Mobile instead uses a supervised-learning model trained on its network data and running on Ericsson’s RAN compute and silicon to predict where demand is rising and redirect resources accordingly.

Kapoor explained training the model on T-Mobile’s data gives it an advantage on the operator’s network over networks which do not contribute their own tuning data.

The results came from newly activated sites in New York City, but the blog did not say how many were included or give a baseline for the trial.

T-Mobile reported the biggest gains during peak hours, including about a 50% increase in advanced beamforming using full-band SRS, a 16% improvement in downlink user throughput, 12% better spectral efficiency and a 10% increase in downlink data volume.

In May, the companies reported close to 10% better spectral efficiency from a separate AI-native scheduler with link adaptation on live traffic.

Kapoor said the technology suits T-Mobile’s network because beamforming works particularly well in TDD spectrum, where uplink and downlink share the same band, and the operator has a leading position in midband TDD.

He linked the trial to rising network demand, citing Ericsson’s June 2026 Mobility Report, which reported global mobile data traffic growth of 22% year-on-year. Video accounted for about 75% of traffic and 5G subscriptions topped 3.1 billion.

Jaco du Plooy, SVP and head of the T-Mobile customer unit at Ericsson Americas, stated the trial marked another step toward networks which respond more intelligently to demand, enabled by Ericsson’s silicon.

Kapoor said 6G networks will need to sense, learn and respond to support applications such as distributed AI, robotics, vision AI and other forms of physical AI, describing T-Mobile’s nationwide 5G-Advanced network as its proving ground.

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Nokia agreed a deal with SoftBank Corp to conduct an AI-RAN field trial in commercial traffic conditions in 2027, progressing efforts to validate the technology.

The Finnish vendor stated the trial in Japan would be an important step in SoftBank’s AI-RAN ambitions by assessing the approach in a live network environment.

For Nokia, the trial also advances its AI-RAN commercialisation roadmap by shifting the technology from laboratory development to real-world deployment.

Nokia explained it will combine its AI-native anyRAN cloud RAN software on SoftBank’s core AI-RAN platform AITRAS, along with Nvidia’s ARC platform.

Through a combination of the platforms, Nokia stated AI-RAN would bring radio and AI workloads on to a common platform, helping operators “improve infrastructure utilisation, acceleration innovation and unlock new revenue opportunities”.

Nokia and SoftBank already have an AI-RAN deal in place, backed by an Nvidia accelerated computing node.

SoftBank SVP and CTO Ryuji Wakikawa said the trial is a significant milestone in the transition of AI-RAN from concept to execution.

“We are excited to begin this next phase of validation with Nokia, paving the way for a new era of networks that can deliver both connectivity and AI services.”

Pallavi Mahajan, chief technology and AI officer at Nokia, pointed to the potential for AI-RAN to “unlock new levels of spectral efficiency while providing the distributed intelligence needed to support physical AI”.

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