Japan’s Rakuten Mobile and Intel extended their long-standing virtualized radio access network (vRAN) work to now include AI integration, propelling ongoing work toward using AI to help manage increasingly complex RAN environments.

The latest work has Rakuten Mobile and Intel testing RAN AI use cases across layer-one, layer-two, RAN operation, and network platform management. This work is using Intel’s FlexRAN reference software, vRAN AI Development Kit, AI tools, and libraries to train, optimize, and deploy RAN-specific AI models running on Intel’s Xeon 6 system-on-chip (SoC).

The stated goal is to integrate AI into the RAN stack to tackle integration challenges while continuing to meet carrier-grade reliability and specific latency requirements. Specifically, the duo cited improved wireless spectral efficiency, automated RAN operations, optimized resource allocation, and increased energy efficiency.

“Intel Xeon processors power the majority of commercial vRAN deployments worldwide, and this transformation momentum continues to accelerate,” Kevork Kechichian, EVP and GM of Intel’s Data Center Group, explained in a statement. “Intel is providing AI-ready Xeon platforms that allow operators like Rakuten to design AI-ready infrastructure from the ground up, with built-in acceleration capabilities."

The AI-related efforts extends long-standing work between Rakuten and Intel dating back to the carrier’s initial 4G LTE network launch in early 2020. It also builds on Intel’s broader work with telecom operators.

AI in the RAN

It also adds to growing work on integrating AI into the RAN. This is being spearheaded by operators looking for ways to better optimize and manage increasingly complex network architectures.

Analysts have pointed to the benefits AI can bring to the deployment and management of cloud-based open RAN networks, which are more complex orchestration challenges due to the disaggregated multivendor ecosystem. This includes the use of AI to help close performance gaps for open RAN architectures compared with legacy RAN models.

“The integration of AI and ML (machine learning) techniques, along with other innovations in energy efficiency and GPU acceleration, will accelerate performance improvements closer to traditional RAN networks,” ABI Research open RAN research analyst Larbi Belkhit noted in a report. “This will remove critical barriers to open RAN adoption and pave the way for flexible, interoperable 5G deployments for network operators rather than reliance on radio network equipment from traditional vendors currently dominating the market, such as Ericsson, Huawei, and Nokia.”

Recent efforts toward this work include AT&T implementation of a network wide generative AI tool to help control network operations, a move that the carrier said is a step toward greater network autonomy.

Raj Savoor, VP of network analytics and automation at AT&T, explained in a blog post that the AT&T Geo Modeler system is active network wide. It uses synthetic data and a network foundation model to simulate and predict network coverage “under dynamically changing network and environmental conditions.”

“Using ray tracing, the Geo Modeler simulates radio transmissions in complex geospatial environments and integrates with multiple internal systems to help automate network decisions and change how the network is configured to improve overall performance and help manage connectivity nationwide, even during extreme events or as the seasons change,” Savoor wrote.

Canadian operator Telus is taking that AI opportunity one step further by using AI to power the RAN intelligence controller (RIC).

The deployment involves Telus using Samsung’s RIC platform and accompanying applications from Samsung’s CognitiV Network Operations Suite (NOS). The initial list of applications includes the KPI Anomaly Detector and RAN Anomaly Insight to proactively identify and analyze network issues; the Energy Saving Manager for dynamic traffic prediction and automated orchestration of energy saving features; and the Load Balancing Manager to optimize resource utilization and improve network performance.