Overview:

AT&T and Ericsson have completed a milestone Cloud RAN test by successfully demonstrating Ericsson’s AI-native Link Adaptation [1.] on a Cloud RAN stack powered by Intel Xeon 6 SoC.  The test showed how artificial intelligence (AI) can improve spectral efficiency and network responsiveness in real-world conditions.  Conducted over AT&T’s licensed frequency bands, the experiment was the first to use portable Ericsson RAN software running on Intel’s new Xeon 6 system-on-chip (SoC) platform—an architecture designed for high-performance, cloud-native processing of RAN workloads. Engineered specifically for network and edge deployments, Intel Xeon 6 SoC delivers breakthrough AI RAN performance with built-in acceleration. Integrated Intel Advanced Vector Extensions (AVX) and Intel Advanced Matrix Extension (AMX) technologies eliminate the need for discrete accelerators while maximizing capacity, efficiency, and TCO optimization.

…………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………..

Note 1. AI-native Link Adaptation dynamically adjusts to changes in signal quality and interference, boosting RAN performance on purpose-built and cloud-based infrastructure alike.

Other Notes:

• vRAN: A radio access network (RAN) in which the baseband processing functions run as software on general-purpose processors (mostly from Intel) instead of on dedicated hardware at the cell site. In vRAN, the functional split defines how baseband processing is divided between centralized processors and the radio unit at the site, and that split drives fronthaul bandwidth, latency, and cost.

• Cloud RAN: An evolution of vRAN where those same RAN functions are re-architected as cloud‑native microservices/containers with CI/CD (Continuous Integration and either Continuous Delivery or Continuous Deployment), automation, and orchestrators, optimized for elastic scaling across distributed cloud infrastructure.

• Ericsson Cloud RAN is a cloud native software solution that handles compute functionality in the RAN. It virtualizes RAN functions on Commercial Off The Shelf (COTS) hardware, decoupling software from hardware to enable more flexible, scalable, and efficient network deployments.

• According to Dell’Oro Group, Cloud RAN (often encompassing vRAN) accounted for approximately 5% to 10% of the total global Radio Access Network (RAN) market revenues in 2025.  In early 2026, Dell’Oro revised Cloud RAN projections downward. While virtualization remains a “key pillar” for the long term, short-term adoption is being slowed by performance, power, and cost-parity challenges when compared to purpose-built hardware.

• The total RAN market stabilized in late 2025 after losing approximately 20% of its value between 2022 and 2024. Market concentration reached a 10-year high in 2025, with the top five vendors (Huawei, Ericsson, Nokia, ZTE, and Samsung) capturing 96% of the revenue.

………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………..

Image Credit: Ericsson

In this proof-of-concept setup, Ericsson’s disaggregated and containerized RAN software operated within AT&T’s target Cloud RAN configuration, built on open, commercial off-the-shelf hardware. The test advanced from basic call functionality to validation of feature-rich network behavior in a cloud computing environment. Ericsson’s AI-native Link Adaptation is a learning algorithm that continuously assesses channel state and interference to determine the optimal modulation and coding scheme for each transmission interval. By generating real-time predictions of link quality, the AI model dynamically adjusts data rates to maximize throughput and spectral efficiency.

Early results were promising. Throughput gains reached up to 20% compared with conventional rule-based link adaptation approaches, alongside measurable improvements in spectral efficiency. Ericsson and Intel also used the trial to benchmark various AI inference models, demonstrating performance scalability and energy efficiency on general-purpose compute nodes rather than proprietary hardware accelerators. This suggests a more pragmatic path for deploying AI workloads across distributed RAN architectures.

AI-native Link Adaptation dynamically adjusts to changes in signal quality and interference, boosting RAN performance on purpose-built and cloud-based infrastructure alike.

Ericsson Cloud RAN is a cloud native software solution that handles compute functionality in the RAN. It virtualizes RAN functions on Commercial Off The Shelf (COTS) hardware, decoupling software from hardware to enable more flexible, scalable, and efficient network deployments.

Engineered specifically for network and edge deployments, Intel Xeon 6 SoC delivers breakthrough AI RAN performance with built-in acceleration. Integrated Intel Advanced Vector Extensions (AVX) and Intel Advanced Matrix Extension (AMX) technologies eliminate the need for discrete accelerators while maximizing capacity, efficiency, and TCO optimization.

Beyond the immediate performance improvements, the trial illustrates how open RAN architectures can accelerate innovation. By decoupling RAN software from vendor-specific hardware, AT&T can integrate AI capabilities and update network functions more quickly, avoiding the constraints of lock-in. The portability demonstrated here—running production-grade Ericsson RAN software on Intel Xeon 6 silicon—marks an industry first.

For AT&T, the achievement represents more than a lab milestone. It provides a technical template for scaling AI-native RAN functions into its cloud infrastructure, pointing to a future where machine learning operates natively within radio environments to fine-tune performance in real time. As operators continue balancing cost, flexibility, and efficiency, AI-optimized Cloud RAN deployments could become the next competitive frontier in 5G—and eventually, 6G—network evolution.

………………………………………………………………………………………………………………………………………………………………………………………………………………………..

Quotes:

Rob Soni, Vice President, RAN Technology at AT&T, says: “AT&T is leading the charge toward an open, intelligent, and scalable network future by advancing Open RAN and Cloud RAN with AI-native capabilities at their core. This demo highlights how AI capabilities, powered by our next-generation Cloud RAN platform, can be deployed seamlessly to drive innovation and deliver superior customer experiences.”

Mårten Lerner, Head of Networks Strategy and Product Management, Business Area Networks at Ericsson, says: “Together with AT&T and Intel, Ericsson is demonstrating how our domain expertise combined with AI-native RAN software can drive transformative advancements in both Cloud RAN and purpose-built deployments. Our industry-leading AI-native Link Adaptation serves as the first proof point on this journey. With a hardware-agnostic RAN software stack, Ericsson is committed to offering maximum flexibility and enabling all our customers to benefit from future innovations – regardless of their chosen underlying hardware. This milestone underscores Ericsson’s commitment to helping operators advance their networks by deploying AI functionality across the RAN stack.”

Cristina Rodriguez, VP and GM, Network and Edge at Intel, says: “This successful collaboration with AT&T and Ericsson showcases the power of Intel Xeon 6 SoC to enable and accelerate AI workloads in Cloud RAN environments. Xeon 6 SoC is architected to handle the demanding compute requirements of AI-native network functions, delivering the performance and efficiency operators need to unlock the full potential of intelligent networks. By providing a flexible, standards-based platform, Intel Xeon 6 enables service providers like AT&T to deploy innovative AI capabilities while maintaining the openness and choice that drive industry innovation.”

………………………………………………………………………………………………………………………………………………………………………………………………………………………….

AI-Native Link Adaptation vs. Traditional Methods:

Traditional link adaptation in RAN relies on deterministic, rule-based algorithms that select the Modulation and Coding Scheme (MCS) from predefined lookup tables. These methods primarily use instantaneous Channel Quality Indicator (CQI) reports or estimated Signal-to-Interference-plus-Noise Ratio (SINR) thresholds, often adjusted via Outer Loop Link Adaptation (OLLA) based on ACK/NACK feedback from the UE. This reactive approach applies conservative margins to account for channel estimation errors, prediction lag, and varying interference, which can lead to suboptimal throughput—either underutilizing the link with low MCS or triggering excess HARQ retransmissions with overly aggressive selections.

AI-native Link Adaptation shifts to a predictive, model-driven paradigm using machine learning (typically lightweight neural networks or time-series models) trained on historical channel data. Rather than static thresholds, the AI processes sequences of CQI, beam metrics, mobility patterns, and interference traces to forecast the probable channel state for the next transmission time interval (TTI). This enables precise MCS selection that hugs the Shannon capacity limit more closely, minimizing BLER while maximizing spectral efficiency in dynamic scenarios like high-mobility NLOS or bursty interference.

Key differences include: