AI RAN
Verizon and Ericsson Advance AI-Native RAN Operations with Nationwide EIC Rollout
Executive Summary:
Verizon and Ericsson are expanding their longstanding collaboration to introduce AI-native automation into Radio Access Network (RAN) operations. The companies plan a nationwide deployment of Ericsson Intelligent Controller (EIC) [1.], a software platform for running RAN applications (rApps) co-developed by Ericsson and Verizon.
The initiative is intended to improve mobile network performance, automate network optimization and advance Verizon’s longer-term objective of achieving Level 4 (L4) network autonomy. The companies have demonstrated EIC and associated rApps in a live network environment, using AI-based cell shaping and traffic steering to optimize Massive Multiple-Input Multiple-Output (Massive MIMO) antenna operation.
According to Ericsson and Verizon, field deployment of the Advanced Antenna System (AAS) Cell Shaper rApp produced throughput gains of up to 8.9% in downlink data radio bearer (DRB) throughput and up to 8% in uplink user equipment (UE) throughput.
The collaboration has three principal objectives:
- RAN autonomy: Building on open, programmable interfaces to automate network operations and optimization.
- AI-based optimization: Deploying rApps to improve radio performance, traffic distribution and operational efficiency.
- Operator-controlled innovation: Enabling Verizon to develop and deploy its own rApps alongside Ericsson applications rather than relying exclusively on vendor-developed network optimization software.
Note 1. “Ericsson Intelligent Controller (EIC) is a new product part of the Ericsson Intelligent Automation Platform (EIAP) which is Ericsson’s Open RAN aligned service management and orchestration (SMO) solution. EIAP provides multivendor RAN automation, management, and orchestration of Cloud and Open Radio access networks. Ericsson differentiator is the extended support to purpose-built radio access networks.” SOURCE: Ericsson
Image generated by ChatGPT which was also used for research and writing this article.
………………………………………………………………………………………………………………………………………………………………………………………………………….
AI-Native RAN Automation and Massive MIMO Optimization:
A central component of the collaboration is the application of AI to Massive MIMO antenna systems. Massive MIMO uses large antenna arrays at 5G base stations to transmit and receive multiple spatially separated data streams. Through beamforming, the radio system can concentrate transmitted energy toward individual users or groups of users rather than radiating power uniformly in all directions. Combined with spatial multiplexing, this can improve spectral efficiency, increase network capacity and extend coverage.
However, optimizing Massive MIMO performance across a large mobile network is complex. Radio propagation conditions, user locations, traffic demand, interference, antenna configuration and mobility patterns change continuously. A configuration that improves performance in one cell or at a particular time of day may not be optimal under different network conditions.
Ericsson’s Advanced Antenna System (AAS) Cell Shaper rApp applies AI-based optimization to antenna operation and cell coverage. Its purpose is to adjust radio coverage characteristics and traffic distribution to better match changing network conditions.
Cell shaping and traffic steering are particularly important in densely populated areas where neighboring cells may experience significantly different traffic loads. Intelligent adjustment of antenna coverage and traffic distribution can help balance demand, reduce congestion and improve utilization of available radio resources.
These reported throughput improvements demonstrate the potential to improve the utilization of existing radio infrastructure through software-based optimization. However, the companies have not disclosed the geographic scope, test duration, baseline conditions, radio configurations or measurement methodology underlying the maximum reported gains. Consequently, the results should not be interpreted as nationwide average improvements or as evidence that equivalent gains will be achieved throughout Verizon’s network.
Automating Fixed Wireless Access Mobility:
Another application demonstrated by Verizon addresses mobility problems affecting Fixed Wireless Access (FWA) devices transitioning between LTE and 5G Standalone (SA) networks.
Verizon implemented a Connected Mode Inter-Radio Access Technology (Inter-RAT) handover methodology to address failed transitions from LTE to 5G SA for FWA devices. According to the companies, the automated methodology eliminates the need for manual intervention when these handover failures occur.
In this scenario, Inter-RAT mobility involves transitioning a device between different radio access technologies: 4G LTE and 5G NR.
Conventional 5G Non-Standalone (NSA) deployments use an LTE anchor, with 5G NR added as a secondary radio access technology (5G NSA is essentially 4G LTE with a 5GNR RAN interface). It relies on the LTE/Evolved Packet Core (EPC) architecture. By contrast, 5G SA uses a 5G NR radio access network connected directly to a 5G Core (5GC), without requiring an LTE anchor. Only 5G SA can realize the 5G features and functions defined by 3GPP’s 5G Architecture specs.
–>A successful 5G NSA to 5G SA transition therefore requires appropriate mobility procedures, network configuration and coordination between the LTE and 5G SA environments.
This application illustrates how programmable RAN automation can address specific operational problems through targeted software applications rather than relying exclusively on conventional network-management procedures and manual troubleshooting. It also demonstrates that RAN automation can address mobility management, network reliability and operational efficiency beyond radio-throughput optimization
Expanding the rApp Ecosystem and Network Autonomy:
Verizon plans to deploy EIC nationally during 2026 and introduce additional rApps developed both internally and by Ericsson. Planned applications include:
- Cell Anomaly Detector: Identifies abnormal cell behavior that may affect network performance.
- Root Cause Explainer: Helps identify potential causes of network problems, supporting more efficient troubleshooting and service restoration.
- Anomaly General Optimizer: Supports network optimization in response to detected anomalies.
The names of these applications suggest different stages of the network assurance and optimization lifecycle. Anomaly detection identifies departures from expected network behavior; root-cause analysis helps determine potential underlying problems; and optimization applications can initiate corrective actions.
When integrated into a coordinated automation framework, these functions can form a closed-loop operational process:
- Monitor: Collect network performance measurements, alarms, configuration data and other operational information.
- Detect: Identify abnormal conditions or performance degradation using analytics and AI/ML models.
- Diagnose: Analyze network data to identify potential root causes.
- Optimize: Determine appropriate corrective actions based on network policies and operating constraints.
- Actuate: Apply approved changes to network configuration or operating parameters.
- Verify: Monitor subsequent network performance to determine whether the corrective action achieved the intended result.
This feedback process is fundamental to autonomous network operations. It allows network optimization to continue without requiring human intervention for every individual adjustment, while retaining appropriate operator oversight and control.
The companies have not provided detailed technical specifications for the three planned applications, so their precise functions, interfaces and degrees of automation remain to be established.
Ericsson Intelligent Automation Platform and Open Interfaces:
The Ericsson Intelligent Controller is a component of the Ericsson Intelligent Automation Platform (EIAP), Ericsson’s open network management and automation environment designed for multivendor, multi-technology networks.
Key platform capabilities include:
- Support for 4G and 5G Radio Access Networks and Ericsson core networks.
- A Non-RT RIC execution environment for rApps.
- Standardized interfaces, including the O-RAN R1 interface.
- Application lifecycle management and deployment.
- Network data collection, analytics and AI/ML workflow capabilities.
- Policy management and coordination of network optimization activities.
- Controlled application execution and network actuation.
Ericsson reports that EIAP’s ecosystem includes more than 100 available rApps from over 100 ecosystem members, supporting different stages of the network automation lifecycle.
The ability for Verizon to develop its own rApps is particularly important. It gives the operator an opportunity to create network-specific optimization functions based on its traffic patterns, spectrum holdings, radio configurations and operational requirements, while also drawing on Ericsson’s broader rApp ecosystem.
An open application environment can reduce dependence on proprietary, vendor-specific optimization functions and encourage third-party application development.
However, the availability of open interfaces and a broad rApp ecosystem should not be confused with a fully interoperable multivendor RAN deployment. Ericsson describes EIAP as supporting multivendor networks, including purpose-built radio access networks. This means that deploying EIC and using O-RAN-aligned interfaces does not, by itself, establish that Verizon is replacing its existing RAN equipment with Open RAN equipment.
The distinction is important: Open RAN automation interfaces and an open application ecosystem can be deployed alongside conventional, purpose-built radio infrastructure.
The Road to Level 4 Network Autonomy:
The nationwide EIC deployment represents another step toward more autonomous RAN operations. Verizon’s longer-term objective is Level 4 (L4) network autonomy, in which network functions can perform increasingly complex operational and optimization tasks with minimal human intervention.
The TM Forum’s Autonomous Networks framework defines L4 as a highly autonomous network capability involving predictive decision-making and active closed-loop management of service-driven and customer-experience-driven networks, supported by AI modeling and continuous learning.
Achieving L4 autonomy requires considerably more than deploying AI applications. It requires:
- Reliable, continuous monitoring of network conditions and performance.
- Accurate AI/ML models that can operate under changing radio and traffic conditions.
- Closed-loop optimization with measurable performance verification.
- Coordination among multiple applications and network domains.
- Policy enforcement, security controls and safeguards against unintended network changes.
- Appropriate human oversight and the ability to intervene when automated actions produce unexpected results.
The distinction between automating individual network functions and achieving end-to-end autonomous network operations is important. An operator may successfully deploy several autonomous rApps without having achieved L4 autonomy across its entire RAN.
Furthermore, the TM Forum autonomy levels are assessed against specific network scenarios and operational domains. Achieving L4 in one function or network domain does not necessarily mean that the entire nationwide network has reached that level.
Verizon’s collaboration with Ericsson provides a programmable software platform and an expanding application ecosystem to support that transition. Whether it ultimately delivers L4 autonomy will depend on the scope of automation, the reliability of closed-loop operations and the integration of these capabilities across Verizon’s network.
Conclusions:
The Verizon-Ericsson initiative illustrates how AI-based software applications, open interfaces and closed-loop control can improve the operation of existing 4G and 5G radio access networks. The reported Massive MIMO throughput improvements and automated LTE-to-5G SA FWA handover demonstrate practical applications beyond AI experimentation. The EIC deployment also provides Verizon with a platform for developing proprietary rApps while leveraging Ericsson’s broader application ecosystem.
The larger challenge will be scaling these capabilities across a nationwide network while maintaining interoperability, reliability, security and measurable improvements in customer experience and network economics. Ultimately, the success of AI-native RAN automation should be judged by sustained improvements in network performance, spectral efficiency, energy consumption and operating costs—not simply by the number of AI applications deployed.
……………………………………………………………………………………………
References:
Verizon and Ericsson Partner to Transform AI-Native Autonomous Networks — November 3, 2026
https://www.ericsson.com/en/portfolio/cross-portfolio/rapp-directory
Autonomous Networks Mission and Levels Taxonomy — TM Forum
About Ericsson Intelligent Controller
About Ericsson Intelligent Automation Platform
AI-RAN and Agentic AI get real: Ericsson, Nokia, Verizon & other operators enter into a new network automation era
Private 5G’s Business Model Shifts: Ericsson’s Move Signals a Market Reset
Ericsson signs MoU’s with Murata and Sumitomo to progress next-gen mobile network infrastructure
4.8 GHz to 4.9 GHz frequency band uses & Verizon Wireless experimental license for testing ISAC with Ericsson
Ericsson leads SK Telecom AI RAN vs. NVIDIA’s GPU centric AI RAN Alliance
Dell’Oro: Private Wireless Revenue Growth Prospects and Challenges
ATIS Proposes Wireless Physical-Layer Foundation Models for AI-Native 6G Networks
Disclaimer: ChatGPT was used extensively to read and summarize the 25 page ATIS Whitepaper described in this article.
………………………………………………………………………………………………………………………………………………………………………….
Introduction:
The Alliance for Telecommunications Industry Solutions (ATIS) has introduced Wireless Physical Layer Foundation Models (WPFMs), a proposed approach to applying artificial intelligence directly to radio-frequency (RF) signals and wireless channel measurements. The objective is to develop reusable AI models that learn characteristics of the radio environment and apply that knowledge to multiple physical-layer communications, sensing, and signal-processing functions.
The ATIS white paper, Wireless Physical Layer Foundation Models for Advanced Communication, Sensing, and Neuro-Symbolic Decision-Making, describes how these models could improve wireless signal interpretation, channel estimation, interference detection, beam management, localization, and joint communications and sensing (JCAS). It is a promising direction for AI in telecommunications because it moves beyond network management, orchestration, and application-layer intelligence. Instead, WPFMs seek to apply foundation-model techniques to the underlying radio signals and propagation characteristics that determine wireless network performance.
“Future wireless networks will need to do more than respond to changing conditions. They will need to understand their environments and make increasingly complex decisions,” said ATIS President and CEO Susan Miller. “This research explores how a shared foundation for AI could help wireless networks operate more efficiently, adapt more effectively, and make decisions that can be better understood and trusted.”
“Learning from large amounts of unlabeled wireless signal data means Wireless Physical Layer Foundation Models need less labelled data for each new task,” said Lead Researcher for the WPFM Jaron Fontaine, imec – IDLab Ghent – Ghent University. “Combined with neuro-symbolic reasoning, they can help networks reason about changing conditions and make decisions that are easier to explain.”
Source: ATIS Whitepaper
The white paper highlights the potential for WPFMs to support more integrated and intelligent wireless systems that can move beyond individual AI capabilities as the industry advances toward 6G. It also identifies opportunities for further research and industry collaboration. This ATIS proposal is particularly relevant to the evolution of 5G-Advanced (3GPP Releases 18-20) [1.] and future 6G wireless networks under the ITU-R IMT-2030 framework. That’s where increasingly complex spectrum environments, higher frequencies, massive antenna arrays, mobility, and integrated sensing will create additional demands on physical-layer signal processing.
Note 1. The latest ITU-R M.2150-3 recommendation (IMT 2020 RIT/SRITs), approved in February 2026, officially incorporates the initial 5G-Advanced baseline from 3GPP Release 18.
…………………………………………………………………………………………………………………………………………………………………………………………………………………………
From Task-Specific Wireless AI to Foundation Models:
Today’s wireless AI implementations typically employ specialized machine-learning models for individual tasks. Examples include modulation recognition, spectrum sensing, interference detection, channel-state information (CSI) compression, beam management, localization, and RF sensing.
Although deep learning has improved the ability to recognize complex signal patterns, these models often remain dependent on particular datasets, hardware platforms, operating conditions, and wireless environments. A model trained for one application may not generalize effectively to another.
ATIS proposes using a foundation-model architecture to address this fragmentation. WPFMs would be pre-trained on large, diverse collections of wireless RF data, primarily using self-supervised learning. This allows models to learn underlying signal and channel characteristics from unlabeled data, reducing the need for extensive manually labeled datasets for each downstream application. It’s important to note that some of the functions depicted are proposed future capabilities, not demonstrated implementations. Figure 1. below provides a useful overview of the WPFM concept and its relationship to wireless physical-layer intelligence.
The resulting representations could subsequently be fine-tuned or adapted for different wireless tasks. The RF data used for training and inference can include:
- Raw in-phase and quadrature (I/Q) samples representing complex wireless signals.
- Channel-state information (CSI) and channel impulse response (CIR) measurements.
- Spectrograms and other time-frequency representations.
- Doppler signatures and Fast Fourier Transform (FFT)-based features.
- Contextual metadata, including sampling rate, center frequency, antenna identity, timing, and hardware platform.
Source: ATIS Whitepaper
…………………………………………………………………………………………………………………………………………………………………………………………………………………….
The white paper describes a multimodal architecture that maps different RF data types into a common latent representation. That’s shown in the illustration below.
Transformer-based models are one candidate architecture because of their ability to capture relationships across time, frequency, and spatial dimensions. An important design consideration is preserving the physical meaning of RF observations. Timing, frequency shifts, antenna configurations, and propagation characteristics cannot simply be treated as interchangeable numerical inputs. The model must retain sufficient information about the underlying radio conditions to support accurate interpretation.
Source: ATIS Whitepaper
…………………………………………………………………………………………………………………………………………………………………………………………………………………………
Potential Applications in Wireless Networks:
ATIS identifies several areas where a shared RF representation could support multiple wireless applications.
Radio resource management: WPFMs could provide reusable information about channel quality, mobility, interference, and network loading. This information could help optimize physical resource block (PRB) allocation, scheduling, and handover decisions.
Spectrum management and interference detection: By learning RF characteristics across different bands, waveforms, and wireless technologies, WPFMs could help identify unfamiliar signals, detect interference, and improve spectrum awareness.
Channel prediction and localization: Shared representations of propagation conditions and channel behavior could support channel prediction, positioning, and localization across changing environments.
Joint communication and sensing (JCAS): WPFMs could help extract information about motion, occupancy, object presence, and environmental conditions while simultaneously supporting wireless communications. The paper also discusses applications involving passive RF sensing.
Industrial IoT and connected vehicles: Potential applications include detecting anomalies in industrial wireless environments, supporting cooperative vehicle perception, and improving wireless performance under changing propagation conditions.
These are proposed applications, not demonstrated commercial capabilities. ATIS explicitly describes the performance improvements in its use-case tables as illustrative evaluation targets rather than experimentally validated results.
…………………………………………………………………………………………………………………………………………………………………………………………………………………………
Massive MIMO, Phased-Array Beamforming and Beam Management:
Massive multiple-input multiple-output (MIMO) systems use large antenna arrays to serve multiple users and improve spectral efficiency through spatial multiplexing and directional transmission. Massive MIMO is already deployed in sub-6 GHz cellular networks, while at millimeter-wave and higher frequencies, electronically steered phased-array antennas are particularly important because they concentrate RF energy into narrow beams to compensate for higher propagation losses.
Managing these beams requires continual estimation of channel conditions, selection of appropriate beam directions, and adaptation to user mobility, blockage, and changing multipath propagation. Conventional beam-training and beam-search procedures can introduce signaling overhead and latency, particularly in highly dynamic environments.
A Wireless Physical Layer Foundation Model could learn relationships among channel-state information (CSI), channel impulse responses (CIR), Doppler measurements, antenna configurations, and observed RF signal characteristics. These learned representations could help predict beam quality, identify alternative propagation paths, and recommend beam adjustments before an existing connection deteriorates.
For massive MIMO, this intelligence could also assist with antenna-array configuration, spatial channel characterization, and user-specific beam selection. Potential benefits include reduced beam-training overhead, more efficient spatial resource utilization, improved link reliability, and faster adaptation to changing radio conditions.
These applications are especially relevant to 5G-Advanced and future 6G systems employing large antenna arrays, higher-frequency spectrum, and increasingly sophisticated beamforming techniques. However, the practical value of WPFMs will depend on demonstrating improvements in beam-selection accuracy, processing latency, and overall radio performance compared with conventional channel estimation and beam-management algorithms.
…………………………………………………………………………………………………………………………………………………………………………………………………………………………
O-RAN: A Potential Deployment Architecture:
Although O-RAN [2.] has not delivered on its original promise of broad, plug-and-play multi-vendor RAN interoperability, its disaggregated architecture remains a potentially relevant environment for WPFMs. That’s because its disaggregated architecture distributes RAN intelligence across functions operating at different timescales.
ATIS proposes that shared RF representations could be made available to O-RAN’s RAN Intelligent Controller (RIC) applications, including:
- Non-real-time RIC rApps: Applications operating on longer timescales could use WPFM-derived information for network optimization, planning, and policy-related functions.
- Near-real-time RIC xApps: Applications could use shared RF information to support more adaptive RAN optimization and resource-management decisions.
Note 2. O-RAN refers to the O-RAN ALLIANCE’s architecture and specifications for OpenRAN.
The potential benefit is that different RAN applications, including those developed by different vendors, could work from a common representation of radio conditions rather than each independently collecting, processing, and interpreting similar RF data. However, the white paper does not define a standardized WPFM interface or establish how these representations would be exchanged among multi-vendor RAN components. Such interoperability remains an important research and development requirement.
…………………………………………………………………………………………………………………………………………………………………………………………………………………………
Neuro-Symbolic AI: Adding Reasoning and Operational Constraints:
One of the more interesting aspects of the ATIS proposal is its integration of WPFMs with neuro-symbolic AI.
A neural foundation model can recognize RF patterns, estimate network conditions, identify anomalies, and predict changes in the radio environment. However, pattern recognition alone does not establish whether a proposed network action is appropriate under operator policies, spectrum regulations, protocol requirements, or physical constraints.
Neuro-symbolic AI combines neural-network learning with explicit rules, knowledge representations, and logical reasoning.
In the proposed architecture, a WPFM provides the perceptual foundation. A separate reasoning layer evaluates its outputs against operational policies and constraints before a network-control function executes an action.
For example, a WPFM might identify deteriorating channel conditions and propose a beam change or resource-block reallocation. The neuro-symbolic reasoning layer could then evaluate the proposal against interference conditions, quality-of-service requirements, spectrum constraints, and network-slicing priorities.
This separation is important: the WPFM is not intended to independently control the network. Instead, its learned representations and predictions become inputs to a broader perception-reasoning-action process.
ATIS also discusses a future agentic architecture in which multiple AI agents and tools coordinate network decisions. However, the development of such neuro-symbolic and agentic architectures is explicitly identified as future work beyond the scope of the current white paper.
…………………………………………………………………………………………………………………………………………………………………………………………………………………………
Major Technical Challenges Remain:
Despite the potential advantages, substantial engineering work is necessary before WPFMs can become practical components of operational wireless networks.
Real-time performance and computational requirements: Wireless physical-layer functions often operate under stringent latency constraints. ATIS illustrates a possible model hierarchy ranging from large models with tens of millions of parameters for offline pre-training to lightweight models intended for edge and device-level operation. Its illustrative latency targets range from approximately 100 milliseconds for large models to less than one millisecond for ultra-lightweight models. These are proposed deployment trade-offs, not measured performance results.
Generalization across wireless environments: A model must operate reliably across different frequency bands, propagation conditions, antenna configurations, hardware platforms, mobility conditions, and terrestrial and non-terrestrial networks. Performance in one environment does not guarantee comparable results elsewhere.
RF datasets and training: Developing sufficiently large and diverse datasets is difficult. Data quality, hardware diversity, synchronization, metadata consistency, and representation of uncommon but operationally important RF conditions all affect model performance. Synthetic data, ray tracing, and digital twins may supplement real-world RF measurements, but simulation-to-reality transfer must be demonstrated.
Neuro-symbolic integration: Translating high-dimensional RF representations into explicit knowledge and rules without losing important signal information is a significant challenge. The combined neural and symbolic processing must also satisfy wireless latency and throughput requirements.
Multi-vendor interoperability: Independently developed WPFMs could generate incompatible internal representations or embeddings. Standardized interfaces, semantic alignment, model provenance, data governance, and interoperability testing will be necessary if multiple vendors are to share and use these models.
Validation and benchmarking: The industry currently lacks standardized multi-task benchmarks for WPFMs. Demonstrating improvements against conventional task-specific models under realistic RF conditions will be essential to establishing their practical value.
…………………………………………………………………………………………………………………………………………………………………………………………………………………………
Conclusions:
ATIS’s Wireless Physical Layer Foundation Model proposal represents a promising direction for AI in telecommunications. Rather than adding another collection of task-specific AI applications to wireless networks, it seeks to establish a reusable understanding of the physical radio environment that can support communications, sensing, localization, and RAN optimization.
The proposed combination of shared RF representations and neuro-symbolic reasoning could eventually provide a foundation for more adaptive and explainable wireless-network operations.
However, the distinction between a proposed architecture and a deployable technology is important. ATIS has outlined a research framework and potential applications; it has not announced a commercially available WPFM, demonstrated operational network deployment, or established a new 3GPP wireless standard.
The key questions are whether these models can generalize across real-world RF environments, meet stringent physical-layer latency and computational requirements, interoperate across vendors, and demonstrate measurable improvements over existing specialized AI models.
For the telecommunications industry, the significance of WPFMs will ultimately depend not on the foundation-model concept itself, but on whether a shared AI understanding of the radio environment can deliver measurable improvements in wireless network performance, efficiency, and operational reliability.
……………………………………………………………………………………………………………………………………………………………………………………………………………………..
References:
ATIS Introduces Wireless Physical Layer Foundation Models for Next-Generation Wireless Intelligence
ITU-R M.2150-3: https://www.itu.int/rec/R-REC-M.2150-3-202602-I/en
AI-Native 6G RAN in Practice: Research and Validation Insights from 6G-MIRAI-HARMONY
T-Mobile’s Strategy of Deploying Multi-Vendor AI-RAN on “Kinetic Edge” using AWS Pillars
AI-RAN Push: NVIDIA, Nokia, and Operators Drive Towards Distributed Compute Platforms
Deutsche Telekom: Device-to-Cloud AI Traffic Won’t Force RAN Upgrades or Increase CAPEX
Analysis: Nvidia–MediaTek Deepen AI Platform Collaboration -What it Means for AI RAN & DCI
Ericsson leads SK Telecom AI RAN vs. NVIDIA’s GPU centric AI RAN Alliance
Analysis: Nokia’s new AI-RAN platform and Standalone AI-RAN node with Nvidia GPUs
Dell’Oro: AI RAN revenue forecast: $35B from 2026-to-2030; 3 types of AI RAN explained
Dell’Oro: AI RAN to account for 1/3 of RAN market by 2029; AI RAN Alliance membership increases but few telcos have joined
AI-Era Cloud Network Transformation: A Reference Architecture and Implementation Roadmap
AI-RAN and Agentic AI get real: Ericsson, Nokia, Verizon & other operators enter into a new network automation era
Federated Wireless Spectrum AI: Advancing CBRS Efficiency Through AI-Driven RAN Optimization
Analysis: Nvidia’s rumored new 6G AI-RAN – likely features/functions and industry impact
AT&T and Ericsson boost Cloud RAN performance with AI-native software running on Intel Xeon 6 SoC
AI-RAN Push: NVIDIA, Nokia, and Operators Drive Towards Distributed Compute Platforms
Introduction:
A massive industry consortium spearheaded by NVIDIA, Nokia, and leading global telecom operators has announced an expanded ecosystem initiative aimed at transforming traditional radio access network (RAN) sites into converged, distributed intelligence platforms. While vendor marketing frequently leans on the ambiguous hype of “AI-RAN,” this specific alignment underscores a tangible structural shift: the push to monetize edge network infrastructure by hosting centralized AI inference and processing workloads directly within the carrier’s decentralized footprint.
The coalition, which includes T-Mobile US, Deutsche Telekom, Orange, and SoftBank, is actively deploying multi-vendor frameworks to validate whether cell sites can simultaneously manage real-time layer-1/layer-2 radio scheduling and high-throughput external AI workloads on a single, unified hardware fabric.
Multi-Vendor Validation and Live Network Milestones:
Rather than remaining confined to isolated laboratory benchmarks, the initiative highlighted several live-network implementation milestones:
-
- T-Mobile US Commercial Network Execution: T-Mobile completed a live AI-RAN call within a commercial production environment, leveraging Nokia’s cloud-native RAN software integrated with NVIDIA’s accelerated computing architecture. The trial successfully demonstrated dynamic, real-time compute resource allocation between standard cellular traffic and concurrent AI applications.
- European Operator Deployment Frameworks: Deutsche Telekom and Orange are initiating deep architectural trials across their European footprints. These validation tests focus explicitly on multi-tenant control planes and the orchestration of heterogeneous GPU resources across legacy and greenfield environments.
- SoftBank Industrial Clusters: Moving past speculative trials, SoftBank is designing localized edge computing clusters that treat cell towers as distributed mini-data centers, targeted directly at hosting low-latency, localized autonomous vehicle and industrial robotics workloads.
Globally, nearly 20 telecom operators have publicly announced AI-RAN collaborations using Nokia anyRAN software and NVIDIA ARC.
- T-Mobile recently marked the world’s first AI-RAN call on its commercial network in Bellevue, Washington, running on commercial ARC servers, Nokia’s NVIDIA CUDA-accelerated RAN software and massive multiple-input multiple-output radios, and connected to an existing 5G Standalone core and a commercial smartphone.
- BT is planning live AI-RAN trials in Birmingham, England, with Nokia and NVIDIA in 2027.
- Deutsche Telekom has validated Nokia and NVIDIA’s commercial AI-RAN platform in Bonn and Oulu labs, including AI-enhanced uplink channel estimation to improve uplink user throughput, with plans to move to field trials in 2027.
- Elisa simultaneously ran voice, mobile data and video analytics on the NVIDIA ARC platform, marking a European first in Elisa’s over-the-air field environment using 5G spectrum. The trial clears the way for distributed deployment and commercial AI-RAN services in 2027.
- Orange is testing Nokia 5G software on NVIDIA ARC, focusing on AI-RAN spectral efficiency improvements and support for edge services in live networks.
NVIDIA is introducing two ARC configurations — MGX 2U and MGX 4U — designed for dense, high-capacity intelligence and radio workloads, typically deployed at centralized RAN sites. Powered by NVIDIA RTX PRO™ 6000 Blackwell Server Edition GPUs, the configurations will offer x86 and NVIDIA Vera CPU options. Dell Technologies, Quanta Cloud Technology and Supermicro will offer ARC-ready servers.
Image generated by Google Gemini
………………………………………………………………………………………………………………………………………………………………………………………
6G Splinter Risk – Will AI-RAN Interoperability Outpace the 6G SA Core?
This real-world push for decentralized AI-RAN execution exposes a critical timing and protocol gap within the broader standardization landscape. As vendors accelerate hardware deployments, the 3GPP SA2 Working Group remains technically fractured over how to formalize the underlying 6G core architecture. Rather than standardizing deterministic state behaviors, the current 6G study phase risks repeating the exact implementation voids that crippled 5G Standalone (SA) interoperability. Today’s commercial push for AI integration highlights the three architectural paths currently splintering 3GPP SA2:
-
- Direction 1 (Separate AI Domain): Anchors the core to the existing 5G Service-Based Architecture (SBA). It utilizes a dedicated AI domain for intent handling via the Service-Based Interface (SBI) while routing Non-Access Stratum (NAS) signaling through the AMF. Backed strongly by Western operators and vendors (including T-Mobile, Deutsche Telekom, Nokia, and NVIDIA), this path preserves 5G infrastructure investments but shifts the burden of multi-vendor integration directly to the device client.
- Direction 2 (AI Functionality in 6G NFs): Integrates machine learning natively within dedicated, standardized 6G network functions following legacy pipeline procedures.
- Direction 3 (Agent-Handled Connectivity): Eliminates traditional structural silos by deeply embedding autonomous AI agents directly into control and user plane procedures, bypassing the AMF entirely via a new Signaling Routing Function (SRF). This introduces massive procedural disruption to baseband silicon and core security frameworks.
………………………………………………………………………………………………………………………………………………………….
Conclusions – Bridging the Hype Gap:
The core engineering risk is that AI and machine learning models are fundamentally non-deterministic. If 3GPP restricts its focus to defining logical API endpoints rather than mandating strict behavioral state-machine realization, open agent-to-agent telemetry, and standardized model interchange formats, 6G will degenerate into a hyper-complex web of proprietary black boxes.
•Telcos have historically struggled to compete with hyperscalers (AWS, Azure, Google Cloud) on raw cloud storage and compute. AI-RAN could possibly the game because of physical close proximity and deterministic latency. By hosting AI workloads at the edge of the connectivity path, network operators might offer “Sovereign AI” and ultra-low-latency processing which centralized hyperscaler data centers physically cannot match.
As detailed in several previous IEEE Techblog posts (see References below), the telecom industry cannot afford another capital expenditure cycle driven by speculative lab benchmarks. By transforming cell towers into high-margin compute platforms, this distributed intelligence alliance offers carriers a concrete pathway to monetize low-latency edge workloads. However, for AI-RAN to achieve true multi-vendor utility, the engineering community must force 3GPP to bridge the gap between paper standards and deterministic software reality. We cannot afford to build another ghost in the machine.
………………………………………………………………………………………………………………………………………………………….
References:
6G Architecture and Economics: Core Network Evolution, AI Integration and Business Case
Warning: 6G core network must not repeat the 5G SA 3GPP architecture specs vs lack of interoperable standards
Sept 2026 3GPP meeting updates for 5G Advanced and 6G planning (with timeline for submisson to ITU-R WP 5D via ATIS)
Roles of 3GPP and ITU-R WP 5D in the IMT 2030/6G standards process
3GPP approves timelines for Release 21 which will specify 6G RAN, Core and 5G Advanced
Dell’Oro: RAN Market Stabilized in 2025 with 1% CAG forecast over next 5 years; Opinion on AI RAN, 5G Advanced, 6G RAN/Core risks
Ericsson leads SK Telecom AI RAN vs. NVIDIA’s GPU centric AI RAN Alliance
Ericsson goes with custom silicon (rather than Nvidia GPUs) for AI RAN
Dell’Oro: AI RAN revenue forecast: $35B from 2026-to-2030; 3 types of AI RAN explained
Deutsche Telekom: Device-to-Cloud AI Traffic Won’t Force RAN Upgrades or Increase CAPEX
Introduction:
Deutsche Telekom is in the process of “upgrading mid-range plans,” according to Dhananjay Mirchandani, DT’s senior VP for group controlling and the group’s next CFO from May 1, 2027. “I cannot recall a single conversation in which Alex Jenbar [CTO of Telekom Deutschland] … or somebody else approached us and said, ‘in addition to whatever we currently have on our roadmaps for RAN modernization, that there is an incremental requirement for an investment to be able to prepare ourselves for additional uplink/AI-related traffic,'” he said. “[That’s] just to give you a sense of the degree of confidence we have in terms of our own capex planning, specifically related to mobile network capacity,” he added.
His assessment on medium-term mobile capacity is bound to be sobering for its radio access network (RAN) vendors in Germany: Ericsson, Huawei and Nokia.
The rise of agentic and physical AI could upend decades of mobile network design that dedicates most bandwidth to download speeds (i.e., downlink) and much less to the data channels for uploads (i.e., uplink). It’s not certain how or when traffic patterns will change in a big way, but telcos are talking about preparing for a wave of AI-fueled traffic coming from smartphones, smart glasses or robots.
The Reality Check – No Incremental RAN Capex:
During an investor event, financial analysts directly pressed Deutsche Telekom’s incoming CFO, Srinivas Mirchandani, on whether capital intensity would escalate after 2027 to handle the upstream traffic generated by physical AI, wearables, and smartphone-based LLM queries.
• The Stand: Mirchandani honestly noted that he could not recall a single internal conversation where the CTO of Telekom Deutschland (Alex Jenbar) requested incremental investment beyond their existing radio access network (RAN) modernization roadmaps to handle AI uplink traffic.
• The Baseline Numbers: DT’s long-term guidance (established at its 2024 Capital Markets Day) mandates that capital spending outside the U.S. and spectrum will sit at 21% of service revenue by 2027. While DT is trading slightly above that now, it is entirely due to heavy fiber-optic rollouts in Germany, not mobile radio expansions.
| Attribute | Deutsche Telekom (Europe Group) | T-Mobile US |
|---|---|---|
| Capex Stance | Rigid cap on mobile intensity; excess savings diverted strictly to German landline fiber infrastructure. | Confirmed network architecture expansion funded via standardized 5G-Advanced cycles. |
| AI Traffic Philosophy | Believes traffic peaks are manageable within existing, scheduled modernization roadmaps. | Views the network explicitly as the “connective tissue for physical AI and AI wearables.” |
| Uplink Solutions | Delaying heavy structural changes; emphasizing spectrum efficiency under legacy boundaries. | Implementing Uplink Carrier Aggregation, Uplink MIMO, and Transmit Switching under 5G-Advanced. |
- Delaying Capex via Software: Panelists from across the telco ecosystem—including Turkcell and Deutsche Telekom’s own group partnering division—noted that AI is actually delaying capex cycles rather than accelerating them. Operators are deploying AI algorithms inside the radio access network to optimize power consumption, conduct predictive capacity planning, and boost edge throughput. In short: AI software is making existing hardware last longer.
- The Opex ROI Conundrum: Telcos are seeing immediate financial returns from “AI for the network” (e.g., DT projects €2.5 billion in cumulative cost savings by 2030 through AI automation). However, the business model for a “network for AI”—where consumers or enterprise clients pay a premium for high-speed, low-latency uplink to feed remote LLMs—remains entirely unproven.
- The 6G Boundary Wall: Based on this author’s carrier checks, most Tier-1 network operators now view massive, structural upstream re-architecting as a “6G-era investment narrative” rather than an immediate 5G sub-6GHz demand. Until dedicated enterprise use cases validate moving further up the value chain, hardware vendors will have to wait for a significant revenue lift from device-to-cloud AI traffic.
Source: Deutsche Telekom, Photo: Norbert Ittermann
…………………………………………………………………………………………………………….
Conclusions:
Deutsche Telekom (DT) indicates that rising device-to-cloud traffic volumes will not necessitate premature radio access network (RAN) upgrades. Technical analyses suggest that upcoming capacity demands driven by agentic AI may not immediately accelerate RAN capital expenditure.
DT does not see AI uplink traffic driving an incremental increase to its RAN capex plans in the medium term. DT is holding a rigid perimeter around its long-term financial guidance. By 2027, capital intensity (excluding the U.S. and spectrum) is strictly capped at 21% of service revenues. Excess capital is not being funneled into expanding cell site capacities for upstream AI traffic; it is being aggressively diverted toward landline fiber-optic buildouts in Germany.
-
- Uplink Carrier Aggregation (CA): Allows the network to combine multiple frequency bands (e.g., mid-band and low-band) exclusively for the upstream path. This maximizes the utilization of already-deployed spectrum without requiring operators to acquire or build new macro tower infrastructure.
- Uplink MIMO & Transmit Switching: Enhances data throughput from consumer AI wearables and smartphones back to the cloud by multiplying the data paths between the device and the tower antenna arrays. [
- Network Slicing: Under 5G SA, carriers can allocate a virtualized, highly efficient “slice” of the existing spectrum explicitly for low-latency AI queries. Slicing can improve resource allocation and protect selected service requirements, but it is not a substitute for sufficient uplink capacity. However, it isolates upstream data paths without needing incremental physical hardware upgrades.
References:
https://www.openranforum.com/home
AI-RAN and Agentic AI get real: Ericsson, Nokia, Verizon & other operators enter into a new network automation era
Cisco Execs: New “Network Supercycle” as Agentic AI Workloads Reshape Telecom Infrastructure
Cisco report: Agentic AI to reshape WAN traffic, AI inference will be ~25% of total traffic by 2035
Nokia’s AI Applications Study: “Physical AI” may require RAN redesign to support high‑volume, low‑latency uplink traffic
STL Partners webinar: Agentic AI needed for RAN autonomy & efficiency
The Financial Trap of Autonomous Networks: Scaling Agentic AI in the Telecom Core
AT&T’s 600 MHz Deployment with Ericsson: Turning Low-Band Spectrum Into Coverage and Uplink Capacity
SoftBank’s Transformer AI model boosts 5G AI-RAN uplink throughput by 30%, compared to a baseline model without AI
T‑Mobile achieves record 5G Uplink speed with 5G NR Dual Connectivity
Telstra achieves 340 Mbps uplink over 5G SA; Deploys dynamic network slicing from Ericsson
Finland’s Elisa, Ericsson and Qualcomm test uplink carrier aggregation on 5G SA network
AI-Native 6G RAN in Practice: Research and Validation Insights from 6G-MIRAI-HARMONY
By Shammi Thakur, Research Director, MarkNtel Advisors with Shazia Hasnie, PhD
Introduction:
A wireless AI model can perform well in the laboratory and still face a very different test once the radio environment changes. However, propagation conditions shift as users move. Traffic loads vary. Devices and carrier frequencies differ. Hardware introduces imperfections that a clean simulation may not capture. Distributed RANs add another complication because a decision made at one network element can affect conditions elsewhere.
A recent 5G trial shows why the distinction matters. In August 2026, SoftBank and Ericsson reported vendor results from a single commercial-network validation of an AI-native scheduler for link adaptation on SoftBank’s 5G network in Japan. The companies evaluated spectral efficiency, downlink user throughput, robustness, and stability under live network conditions. Average gains across the evaluated areas were about 10% for both spectral efficiency and downlink user throughput, while the highest reported gains reached approximately 25% and 50%, respectively. [1]
Commercial-network testing adds something a controlled simulation cannot: exposure to the variability that the system is expected to handle.
IEEE ComSoc TechBlog has already examined the move from AI as an overlay in 5G toward AI-native 6G, along with the developing IMT-2030 requirements and the role of AI in future RANs. [2][3] This article takes a narrower view. It examines how AI-native RAN functions can be trained, coordinated, and tested when wireless conditions, hardware, data, and network topology all change.
6G-MIRAI-HARMONY provides a concrete research setting for that question. The EU-Japan program connects realistic channel and hardware modeling, AI-based physical-layer functions, distributed coordination, AI-ready RAN architecture, and common approaches to data management, benchmarking, and validation. [4][5]
The Research Challenge for AI-Native 6G RAN:
Training data quietly becomes part of an AI model’s operating assumptions. Conventional wireless algorithms are generally built around assumptions that engineers can state explicitly. Machine learning adds another dependency: the examples used during training and evaluation influence what the model learns to recognize as normal.
A model can consequently look robust while remaining sensitive to conditions absent from its training environment.
Wireless conditions can change without the underlying network function changing. Users move through different propagation environments. Traffic loads rise and fall. Carrier frequencies and device characteristics vary. Hardware can introduce quantization, RF impairments, and converter limitations.
Distributed RANs add another layer. Measurements used by one function may come from elsewhere in the network. Beamforming and scheduling decisions can affect later decision cycles. Fronthaul capacity can determine how much information reaches another processing point before a coordinated action has to be taken.
An AI function therefore cannot always be judged as an isolated block. Network behavior around the function can influence the result just as much as the model architecture itself.
6G-MIRAI-HARMONY: Research Approach and Scope:
6G-MIRAI began on April 1, 2025, and runs for 36 months through March 2028 under Horizon Europe and the Smart Networks and Services Joint Undertaking. Grant Agreement 101192369 lists total project costs of €3,188,935 and an EU contribution of €2,979,966.50. Ericsson France is the coordinating organization. [5]
The European consortium includes Ericsson France, Fraunhofer, Telefónica, IS-Wireless, Sequans, Apple Technology Engineering, KU Leuven, CNIT, and the University of Pisa. The associated HARMONY work in Japan brings in organizations including the University of Tokyo, Kyocera, NEC Networks & System Integration, Shimizu, Tokyo University of Agriculture and Technology, and KDDI Research. [6]
Five Research and Technology Items connect the technical work:
- Realistic models and datasets covering channel, hardware, and measurement conditions.
- AI/ML for physical-layer components, including learning-based approaches to wireless functions.
- AI-driven coordination for distributed and cell-free deployments.
- AI-ready 6G-RAN architecture covering where intelligence, data, and computing are placed.
- Data management, testing, and validation for common scenarios and reproducible evaluation. [4][5]
The sequence matters because each part affects the next. Data conditions influence AI training. AI functions interact with distributed coordination. Architecture determines where models can run and what information they can access. Benchmarking and validation determine how the resulting measurements can be compared.
Realistic Models and AI-Based Physical-Layer Functions:
The wireless environment represented in an experiment becomes part of the AI system whether researchers intend it or not.
6G-MIRAI-HARMONY’s methodology combines link- and system-level modeling with analytical evaluation and later laboratory- or prototype-oriented validation. QuaDRiGa is identified for spatially and temporally consistent channel generation, while Sionna supports differentiable communication-system modeling. Measurement data and experimental RAN data provide additional routes toward less idealized evaluation conditions. [4]
Three parts of the modeling chain deserve particular attention:
- The channel: Propagation, mobility, and spatial variation determine the conditions presented to the model.
- The hardware: RF impairments, quantization, finite-resolution converters, and other non-idealities can alter physical-layer behavior.
- The data: Training and test datasets determine which situations the model encounters and which remain unseen.
Those conditions matter for functions such as channel estimation, CSI prediction and compression, neural receivers, precoding, power control, and link adaptation.
Independent research is addressing similar issues. A 2026 study of CNN-based hybrid precoding for cell-free massive MIMO evaluates imperfect CSI, phase noise, channel aging, molecular absorption, and transceiver hardware nonidealities in a THz simulation environment. The reported results include spectral-efficiency gains against several conventional approaches, while the study also measures the effect of individual hardware impairments on performance. [7]
Physical-layer intelligence therefore has to be tested against the radio conditions it is expected to encounter. An improvement under one channel or hardware assumption does not establish the same improvement under another.
Distributed Intelligence for Cell-Free Massive MIMO:
Distributed access points can work together, but the information needed to coordinate them is distributed as well. Beamforming decisions affect interference seen by other users. Scheduling changes the traffic mix entering later decision cycles. Power-control decisions can alter the conditions under which subsequent optimization takes place.
Fronthaul places a direct limit on how much information can be exchanged. Fully centralized processing can require substantial CSI and data transfer as the number of access points and users grows. Practical designs therefore have to balance cooperation against available transport and processing resources.
Independent cell-free massive MIMO research continues to address that scalability problem. A 2025 IEEE Access study describes network overloading and scalability as major design challenges and evaluates a joint pilot and access-point assignment method in systems tested with up to 150 access points. [8] The paper notes that fully centralized processing can create significant fronthaul signaling and computational demands as deployments expand.
Synchronization adds another requirement. Coherent joint processing depends on suitable phase and frequency alignment across distributed access points. A 2024 IEEE Open Journal of the Communications Society study examines phase misalignment among access points in cell-free massive MIMO and evaluates a partially coherent transmission framework designed to reduce the need for network-wide phase alignment. [9]
AI-driven coordination has to operate inside those constraints. Improving beamforming or scheduling is only part of the problem when the information required for that decision is delayed, compressed, or unavailable at the point where the model runs.
Designing an AI-Ready 6G RAN:
Where the model runs can be as important as what the model does.
A latency-sensitive control loop may need processing close to the network element generating the measurements. Another workload may benefit from a larger computational environment with access to a wider network view. Data access, inference latency, model updates, and control interfaces all affect that choice.
Four practical questions follow:
- Where does inference run?
- Which measurements reach the model?
- How frequently are those measurements updated?
- What happens when the required information sits elsewhere in the RAN?
O-RAN already has work addressing part of this problem. Its WG2 specifications cover the Non-Real-Time RAN Intelligent Controller, A1 and R1 interfaces, AI/ML learning and deployment, and the AI/ML workflow. [10]
O-RAN’s current work does not define the final architecture for 6G. It does show where AI lifecycle and deployment questions are already being addressed in today’s RAN architecture. 6G-MIRAI-HARMONY takes those concerns into a research setting that also includes distributed and potentially disaggregated 6G architectures. [4]
Common Data, Benchmarking and Validation:
Performance figures become difficult to compare when the experiments behind them use different conditions. Two studies might report higher spectral efficiency while using different datasets, channel assumptions, training procedures, hardware models, and test conditions. Without those details, the numbers are difficult to interpret.
The 6G-MIRAI-HARMONY D3.1 deliverable addresses this problem directly. It provides the project’s first consolidated framework for benchmarking and validation, data-management principles, and common scenarios. The document places particular emphasis on repeatability, traceability, comparability, data quality, and reuse. It also defines KPI/KVI considerations and protocols for training, evaluation, and generalization. [11] The evaluation chain is shown in Figure 1.
Figure 1. The evaluation chain for AI-native RAN research.
The scenario defines the conditions represented in the experiment. The dataset determines what the model sees. The model is evaluated through selected metrics and then tested under stated validation conditions.
Changing one part of that chain can change the result. An unseen propagation condition may expose a generalization problem. A realistic hardware model may reduce the gain seen with ideal hardware. A different metric may favor a different approach. Benchmarking and validation also answer different questions. Benchmarking establishes common conditions for comparing approaches. Validation examines whether the resulting system behaves as required under defined conditions.
D3.1 is an existing project output. Later deliverables are intended to add datasets and results covering common scenarios, validation, and benchmarking. Those later outputs should remain described as project work rather than completed findings until the corresponding evidence is published. [11][12]
From Research Validation to Standardization:s
ITU-R’s IMT-2030 work has already moved beyond the initial framework.
Recommendation ITU-R M.2160 established the framework and overall objectives for IMT-2030 in November 2023. The framework includes six usage scenarios, among them Artificial Intelligence and Communication (AIAC), and identifies ubiquitous intelligence as one of its overarching design principles. [13]
In February 2026, ITU-R Working Party 5D completed the draft report on minimum technical performance requirements for IMT-2030 radio interfaces. The draft defines 20 minimum technical performance requirements and provides a consistent basis for specification and evaluation. The draft has been submitted to Study Group 5 for consideration in December 2026. [14] In June 2026, Working Party 5D also completed the draft guidelines for evaluating candidate IMT-2030 radio interface technologies. The guidelines define three evaluation methods—simulation, analytical evaluation, and inspection—and specify seven test environments. The draft has likewise been submitted to Study Group 5 for approval in December 2026. [14]
ITU-R’s candidate radio-interface technology submission process is scheduled to begin in February 2027, with the candidate-submission window extending through February 2029. [14] 3GPP is pursuing AI/ML work within the RAN in parallel. TR 38.745, “Study on Artificial Intelligence (AI)/Machine Learning (ML) for NG-RAN Phase 3,” was created for Release 20 on September 5, 2025, and was placed under change control on March 19, 2026. [15]
Research results do not become standards simply because an algorithm performs well in one experiment. The useful evidence includes the conditions behind the result: the scenario, dataset, metrics, validation method, limitations, and reproducibility of the test.
Conclusions:
AI-native 6G RAN will require more than putting machine learning inside individual RAN functions.
Propagation changes. Hardware is imperfect. Network intelligence becomes distributed. Fronthaul and synchronization can limit coordinated processing. Architecture determines where data and computation are available. Training and evaluation conditions affect what an AI result actually shows.
6G-MIRAI-HARMONY brings these issues into one research program by connecting realistic wireless models, AI-based PHY techniques, cell-free coordination, AI-ready RAN architecture, and common approaches to data, benchmarking, and validation. The work points to a practical requirement for AI-native RAN research: performance results need enough information around them to show how they were produced, where they hold, and what changes when the underlying conditions change.
References:
- SoftBank Corp. and Ericsson, “SoftBank Corp. and Ericsson conduct Japan’s first trial of Ericsson AI in RAN on a 5G commercial network,” August 20, 2026.
https://www.ericsson.com/en/press-releases/2/2026/softbank-corp-and-ericsson-conduct-japans-first-trial-of-ericsson-ai-in-ran-on-5g-commercial-network - IEEE ComSoc Technology Blog, “Comparing AI Native mode in 6G (IMT 2030) vs AI Overlay/Add-On status in 5G (IMT 2020),” January 15, 2026.
https://techblog.comsoc.org/2026/01/15/comparing-ai-native-mode-in-6g-imt-2030-vs-ai-overlay-add-on-status-in-5g-imt-2020/ - IEEE ComSoc Technology Blog, “IMT-2030 (‘6G’) Minimum Technology Performance Requirements for Radio Interface Technologies,” March 17, 2026. https://techblog.comsoc.org/2026/03/17/update-imt-2030-6g-minimum-technology-performance-requirements/
- 6G-MIRAI-HARMONY, “Concept and Methodology,” 2026.
https://6g-mirai-harmony.eu/concept-and-methodology/ - European Commission, CORDIS, “Machine Intelligence based Radio Access Infrastructure (6G-MIRAI),” Grant Agreement No. 101192369.
https://cordis.europa.eu/project/id/101192369 - 6G-MIRAI-HARMONY, “Consortium and Objectives,” 2026.
https://6g-mirai-harmony.eu/consortium-and-objectives/ - Tadele A. Abose and Thomas O. Olwal, “Hardware-Impairment-Aware CNN-Based Hybrid Precoding for Cell-Free Massive MIMO Systems Under Imperfect CSI in Terahertz-Enabled 6G Networks,” Telecom, vol. 7, no. 3, 2026.
https://www.mdpi.com/2673-4001/7/3/70 - Mhammad B. Hmayed and Ali H. Bastami, “Scalable Cell-Free Massive MIMO System: Joint Optimal Pilot and Access Point Assignment,” IEEE Access, vol. 13, pp. 201778–201788, 2025. DOI: 10.1109/ACCESS.2025.3638655
https://ieeexplore.ieee.org/document/11271199 - Unnikrishnan Kunnath Ganesan, Tung Thanh Vu, and Erik G. Larsson, “Cell-Free Massive MIMO With Multi-Antenna Users and Phase Misalignments: A Novel Partially Coherent Transmission Framework,” IEEE Open Journal of the Communications Society, vol. 5, pp. 1639–1655, 2024.
DOI: 10.1109/OJCOMS.2024.3373170.
https://ieeexplore.ieee.org/document/10459246 - O-RAN Alliance, “WG2: Non-Real-time RAN Intelligent Controller and A1,” Technical Work Group scope.
https://www.o-ran.org/technical-groups/wg2 - 6G-MIRAI-HARMONY, “D3.1 — Definition of Initial Common Scenarios, Data Management and Benchmarking Methodology,” 2026.
https://6g-mirai-harmony.eu/wp-content/uploads/2026/02/6G-MIRAI_Deliverable_D3.1_Scenarios_Data_Bench_v1.0.pdf - 6G-MIRAI-HARMONY, “Deliverables, Publications and Workshops,” 2026.
https://6g-mirai-harmony.eu/deliverables-publications-and-workshops/ - ITU-R Recommendation M.2160-0, “Framework and overall objectives of the future development of IMT for 2030 and beyond,” November 2023.
https://www.itu.int/rec/R-REC-M.2160-0-202311-I/en - ITU-R, “IMT towards 2030 and beyond (IMT-2030),” current development, evaluation, and submission process.
https://www.itu.int/en/ITU-R/study-groups/rsg5/rwp5d/imt-2030/pages/default.aspx - 3GPP, TR 38.745, “Study on Artificial Intelligence (AI)/Machine Learning (ML) for NG-RAN Phase 3,” Release 20.
https://portal.3gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=4445
Author Bio:
Shammi Thakur is Research Director at MarkNtel Advisors, with more than 15 years of experience in strategic market intelligence, industry forecasting, and competitive analysis. He leads research initiatives across global technology and communications markets, overseeing the development of market studies and strategic research frameworks. His work focuses on translating complex industry developments into evidence-based insights, with particular experience in emerging technologies, market evolution, and the forces shaping next-generation communication systems.
Website: MarkNtel Advisors
Analysis: Nvidia–MediaTek Deepen AI Platform Collaboration -What it Means for AI RAN & DCI
Executive Summary:
Nvidia has expanded its partnership with Taiwan based semiconductor firm MediaTek, committing $3.5 billion to a bond investment the two companies describe as a deepening of their “longstanding collaboration to build the next generations of AI computing platforms.” The move extends Nvidia’s pattern of strategic capital deployment across its compute ecosystem, this time reaching into custom silicon and advanced packaging.
According to the announcement, the collaboration couples Nvidia’s accelerated computing, AI, graphics, and software stack with MediaTek’s capabilities in custom silicon, high-performance computing (HPC), system-on-chip (SoC) design, and advanced packaging. MediaTek vice chairman and CEO Rick Tsai framed the investment as reinforcing a relationship spanning “cloud AI infrastructure, local AI computing and automotive in the era of physical AI,” combining Nvidia’s accelerated-computing and AI software ecosystem with MediaTek’s AI technology portfolio and custom-silicon resources.
Over the past several years, MediaTek has repositioned itself from a supplier of predominantly mid- and low-tier handset chipsets into a presence across smartphones, AI data centers, automotive, and IoT. Securities firm Yuanta Financial Holdings expects the closer alignment to accelerate MediaTek’s transition from edge AI into data-center infrastructure, device-based AI computing, and automotive platforms. Trading in MediaTek shares was suspended on the Taipei exchange Tuesday after the stock hit its daily 10% upside limit. The shares closed at NT$4,315 (about US$136.05), reflecting investor enthusiasm for the deepening of the collaboration.
NVIDIA and MediaTek are collaborating in three major areas:
- AI infrastructure: MediaTek will work with NVIDIA’s NVLink Fusion ecosystem to enable customers to develop custom AI infrastructure designed to integrate with NVIDIA rack-scale systems and AI factories.
- Local AI computing: The companies will continue to collaborate on multiple generations of NVIDIA RTX Spark™ and DGX Spark™ PC chips, powering consumer PCs, AI developer supercomputers and enterprise-class workstations, that integrate NVIDIA GPUs with MediaTek SoCs.
- Automotive: MediaTek and NVIDIA will continue developing platforms for AI-powered, software-defined vehicles in the era of physical AI.
“AI is transforming every computing platform — from the world’s largest AI factories to the PC and the car,” said Jensen Huang, founder and CEO of NVIDIA. “MediaTek is one of the world’s great semiconductor companies, with exceptional expertise in system-on-chip design, connectivity, leading performance and power efficiency. Together, we’re building platforms that bring NVIDIA accelerated computing to new markets and give customers the freedom to create differentiated AI systems at enormous scale.”
“MediaTek and NVIDIA share a vision for making advanced AI computing pervasive across the technology landscape,” said Rick Tsai, vice chairman and CEO of MediaTek. “NVIDIA’s investment strengthens a collaboration that spans cloud AI infrastructure, local AI computing and automotive in the era of physical AI. By combining NVIDIA’s leadership in accelerated computing and AI software ecosystem with MediaTek’s expertise in a diverse AI technology portfolio from edge to cloud, and our leadership position in custom silicon, we can accelerate innovation for our customers.”
Custom AI Infrastructure and NVLink Fusion:
Yuanta identified MediaTek’s participation in Nvidia’s NVLink Fusion as the most consequential element of the arrangement. Through that linkage, MediaTek could support the design of custom AI chips for clients while connecting them directly into Nvidia’s AI infrastructure — a complementary rather than competing role alongside the GPU vendor’s proprietary interconnect fabric.
The scale of the bond purchase also signals Nvidia’s long-term commitment to the relationship. Nvidia, currently the world’s most valuable company at roughly a $5 trillion market capitalization, has been deploying capital aggressively across both upstream and downstream partners. This latest deal, however, inevitably risks overlapping with other Nvidia investments — notably its $2 billion partnership with Marvell, which is also heavily oriented around NVLink Fusion and pitch-for the RAN with a one-chip architecture.
NVLink Fusion vs. Ethernet Scale-Out Interconnect:
NVLink Fusion and Ethernet-based scale-out interconnects target fundamentally different layers of the AI fabric, and the distinction turns on memory semantics rather than raw link rate. NVLink Fusion sits in the scale-up domain: it delivers memory-coherent, tightly coupled attachment for custom XPUs into Nvidia’s rack-scale NVLink fabric, with point-to-point GPU/XPU-to-GPU/XPU connectivity and an integrated high-bandwidth-memory interface (NVHBM) that trades die area and power for latency and bandwidth. It is proprietary, closed, and full-coherency by design — precisely the properties that make it hard for open standards to clone. Ethernet scale-out, by contrast, is the scale-out workhorse governing traffic between racks, pods, and clusters; it is loss-based, coarser-grained, and standardized through Ultra Ethernet and the broader IEEE 802.3 ecosystem.
The real contest is not NVLink against Ethernet so much as how much of the memory-coherent scale-up domain Ethernet-based challengers can capture. Standards-backed alternatives — UALink 2.0, Ultra Ethernet, and Broadcom’s Scale-Up Ethernet (SUE) — are pressing into scale-up territory, but they lack NVLink’s memory coherency and direct XPU-to-XPU connectivity, and SUE explicitly does not wire GPUs directly to GPUs. Meanwhile Nvidia’s strategy with NVLink Fusion is to co-opt custom XPUs (AWS Trainium4, and now MediaTek- and Marvell-designed silicon) into its fabric rather than let them migrate to those open scale-up paths. The practical outcome in the data center is a layered model — Ethernet surging in scale-out, and NVLink Fusion consolidating the latency-sensitive, memory-coherent scale-up spine — with Nvidia working to keep its proprietary layer the mandatory gateway that custom accelerators must pass through to reach the wider Ethernet-based world.
The $3.5 billion convertible-bond investment is best read not as a portfolio decision but as a defensive move within the AI rack-scale interconnect stack. The technical centerpiece is MediaTek’s adoption of the NVLink Fusion platform. Nvidia positions it as the connectivity technology and intellectual property that lets hyperscalers and AI-native operators drop custom accelerators (“XPUs”) and CPUs directly into its NVLink scale-up fabric.
MediaTek will now build against the three subsystems that define NVLink Fusion: the NVLink Fusion chiplet, which bridges a customer’s custom XPU to the NVLink scale-up domain over photonic or electrical interconnects (the chiplet itself carries up to ~1.8 TB/s of bidirectional bandwidth); NVLink-C2C, the high-bandwidth die-to-die link between XPUs, Nvidia’s Vera/Rosa CPUs, and other compatible processors; and NVHBM, a customized high-bandwidth-memory interface that trades off die area and power efficiency. The strategic significance is that Nvidia is effectively licensing its interconnect and memory layer as a licensable subsystem rather than enforcing a closed, all-Nvidia island — which lets it absorb “frenemy” silicon into the fabric instead of fighting it.
Two implications stand out for interconnect strategy:
-
A hedge on hyperscaler in-house silicon. AWS has already integrated its Trainium4 custom ASICs with NVLink Fusion for rack-scale deployment, and Google, Meta, Microsoft, and OpenAI are all advancing private accelerators. By standardizing the interface (NVLink Fusion, NVLink-C2C, NVHBM, chiplets, and rack-scale integration) that these custom XPUs must speak, Nvidia keeps chiplets, packages, memory, and racks—the connective infrastructure itself—inside its own revenue funnel. MediaTek, in turn, becomes the design conduit that helps hyperscalers and frontier model firms get to that tape-out without building an independent scale-up stack.
-
The scale-up vs. scale-out contest sharpens. NVLink remains essentially the only mature, memory-coherent scale-up fabric in production, whereas Ethernet is consolidating its dominance in scale-out (front-end and increasingly AI back-end) fabrics, and standards like Ultra Ethernet, UALink, and Broadcom’s Scale-Up Ethernet (SUE) are pressing into the scale-up domain. UALink and SUE, notably, lack NVLink’s memory coherency and point-to-point GPU-to-GPU connectivity, so NVLink Fusion’s positioning — co-opting custom XPUs rather than competing with them — lowers the attack surface those open standards could otherwise exploit.
The buy-in therefore consolidates Nvidia’s grip on the two layers that are hardest to commoditize: the latency-sensitive scale-up interconnect and the custom-silicon design flow that feeds it. What operators and chip partners give up is architectural independence — the bond structure and NVLink Fusion licensing effectively bind MediaTek into Nvidia’s ecosystem rather than allowing it to become an independent AI data-center silicon house.
What It Means for AI-RAN:
For the RAN, this is arguably a more consequential signal than the data-center economics. Nvidia’s AI-RAN play, via the AI Aerial software-defined accelerated-computing platform and its founding role in the AI-RAN Alliance, is built on having vRAN and AI workloads share the same GPU-accelerated infrastructure — what the community variously calls “AI for RAN,” “AI on RAN,” and “AI-and-RAN” on common hardware.nvidia+1
Here the MediaTek deal intersects directly with Nvidia’s earlier $2 billion Marvell partnership, which last year wired Marvell into the same NVLink Fusion platform for both the AI factory and AI-RAN ecosystem — Marvell supplying custom XPUs and NVLink Fusion-compatible scale-up networking, while Nvidia contributes the Vera CPU, ConnectX NICs, BlueField DPUs, Spectrum-X switches, and the rack-scale AI compute. The two arrangements are complementary in practice but overlapping in intent: Marvell owns the scale-up networking and the “one-chip-to-rule-the-RAN” custom silicon angle, while MediaTek brings SoC design, advanced packaging, and stronger reach into edge, automotive, and device-grade silicon. Together they give Nvidia two design houses capable of producing custom XPUs that plug into both data-center racks and AI-RAN infrastructure.
The operative question for IEEE readers is whether this broadens the AI-RAN supplier base going forward. Ericsson and Samsung have indicated their vRAN software built for Intel could be ported to AMD or Arm silicon with relatively modest effort, and Dell’Oro projections already have AI-RAN taking a significant share of the broader RAN market. Nvidia’s strategy is to ensure that, however the RAN silicon shakes out, the scale-up fabric and the custom-XPU design path remain NVLink Fusion-based — extending its data-center interconnect franchise into the radio access domain itself. The risk, flagged in the news report, is over-concentration: stacking Marvell and MediaTek onto the same NVLink Fusion foundation concentrates architectural leverage in Nvidia’s hands and could crowd out the open, multi-vendor fabric choices that O-RAN advocates would prefer to see served by standard Ethernet and UALink-style paths.
References:
https://www.lightreading.com/finance/nvidia-tips-3-5b-into-mediatek-in-latest-tech-partnership
Analysis: Nvidia’s $2 billion investment in Marvell; NVLink Fusion ecosystem & RAN vendor silicon strategy
Ericsson and MediaTek Demonstrate 3GPP-Based GNSS RTK Positioning with Sub-30cm Accuracy Over a Commercial 5G Network
5G infrastructure moves from coverage and speeds to cloud-native, orchestration, automation and AI-assisted networks
MediaTek overtakes Qualcomm in 5G smartphone chip market
Ericsson leads SK Telecom AI RAN vs. NVIDIA’s GPU centric AI RAN Alliance
Executive Summary:
Ericsson says it has been selected as the sole global technology partner in SK Telecom’s consortium for Korea’s Hyper-AI Network Infrastructure Demonstration Project, a South Korea government-backed initiative led by the Ministry of Science and ICT and the National Information Society Agency. The program is intended to validate AI-RAN pilot networks as a cornerstone of Korea’s national “AI Highway” strategy and its longer-term 6G roadmap.
Ericsson’s contribution [1.] to this project centers on the RAN and the automation layer that surrounds it. According to Ericsson, its role in the project includes 5G Standalone expertise, AI-in-RAN capabilities, and intelligent network automation through the Ericsson Intelligent Automation Platform (EIAP) and AI-powered rApps, enabling autonomous control, resource orchestration, and real-time optimization for physical AI services. Ericsson is not being positioned as a general AI infrastructure provider, but as the core network technology partner bringing telco-grade RAN, automation, and orchestration into a live industrial validation environment. Ericsson will contribute what it calls an “end-to-end network foundation” to the project that is intended to connect and continuously optimize intelligent machines and autonomous industrial operations. In the second phase of the project, Ericsson’s AI-RAN pilot network is expected to be applied to a real-world logistics environment at KG Mobility’s Pyeongtaek plant.
Note 1. It’s interesting to note that Ericsson is a founding member of the Nvidia GPU centric AI-RAN Alliance, contributing the operator-centric “AI-for-RAN” perspective by embedding telco-grade AI into basebands and radios to improve network performance, automation, and energy efficiency. The company’s delegates hold leadership roles in the alliance’s working groups and technical committees.
For telecom operators, the significance is not just radio performance but the shift toward a network architecture that can support physical AI use cases with tighter latency, stronger uplink capacity, and higher service assurance. SK Telecom plans to demonstrate AI-RAN pilots through 2027 across scenarios such as autonomous transport, industrial safety monitoring, and humanoid robotics, with a second-phase deployment targeted for KG Mobility’s Pyeongtaek plant.
Sibel Tombaz, Head of Customer Unit Korea, Ericsson, says: “Being selected as a key global technology partner in this government-backed Hyper-AI Network consortium is a significant recognition of Ericsson’s technology leadership and our long-term partnership with SK Telecom. Korea is moving with clear ambition to shape the future of AI-native networks and 6G, and Ericsson is proud to contribute our global expertise in 5G, AI-RAN, orchestration, and intelligent network automation to this important national initiative. We look forward to working closely with SK Telecom and the consortium partners to help turn Korea’s 6G vision into real industrial value.”
Takki Yu, VP and Head of Network R&D at SK Telecom, says: “As a key national project supporting Korea’s Hyper-AI Network strategy, SK Telecom is pleased to collaborate with Ericsson as a key technology partner to advance AI-RAN technology through global cooperation. Together, we aim to build a strategic win-win partnership, demonstrate real industrial value, and help strengthen Korea’s AI-RAN technology capabilities and ecosystem for the future.”
Comparison with NVIDIA GPU centric AI-RAN Alliance:
The SK Telecom–Ericsson initiative and the NVIDIA-led AI-RAN ecosystem share a common thesis: the RAN is evolving into a distributed compute platform that can host or coordinate AI workloads closer to where data is generated. But they emphasize different layers of the stack. SK Telecom’s Ericsson-led project is centered on operator-grade RAN validation, network autonomy, and physical AI service delivery in industrial settings, while NVIDIA’s effort is broader and more compute-centric, tied to AI factories, GPU infrastructure, and open 6G ecosystem development.
- NVIDIA is a founding member of the AI-RAN Alliance and acts as one of its most visible technical drivers, contributing reference architectures and accelerated-computing platforms for AI-RAN.
- Ericsson is the operator-focused RAN technology partner proving AI-RAN in live telco and industrial network deployments.
The practical takeaway is that Ericsson’s role is more narrowly telecom-native, while NVIDIA’s consortium is more infrastructure- and compute-oriented. For 6G, the two approaches are complementary rather than contradictory: one is proving how the network behaves as a deterministic industrial platform, the other is building the compute substrate that can feed AI-native services into that platform.ericsson+2
Why it matters:
SKT announced the Hyper-AI Network Infrastructure Development project last month, and at the time Ericsson, Nokia, Samsung and private mobile networking specialist HFR were named as those providing the kit and related network and software solutions.
South Korea has become one of the most advanced and competitive mobile markets, and this makes it a useful proving ground for AI-RAN. If the SK Telecom pilot succeeds, it could strengthen the case that future mobile networks must support not only connectivity, but distributed inference, orchestration, and machine control for robots, vehicles, and industrial systems.
From a standards and market perspective, the more interesting question is how these pilots influence the eventual division of labor between RAN, edge compute, and centralized AI infrastructure in 6G-era networks. That is where the SK Telecom–Ericsson model, with its operator-led network validation, may prove especially influential.
…………………………………………………………………………………………………………………………………………………………………………………………………
References:
https://www.ericsson.com/en/press-releases/2026/8/skt-hyper-ai-consortium
https://www.telecoms.com/ai/ericsson-named-sole-global-vendor-for-sk-telecom-s-hyper-ai-consortium
Ericsson goes with custom silicon (rather than Nvidia GPUs) for AI RAN
Dell’Oro: AI RAN revenue forecast: $35B from 2026-to-2030; 3 types of AI RAN explained
Dell’Oro: RAN Market Stabilized in 2025 with 1% CAG forecast over next 5 years; Opinion on AI RAN, 5G Advanced, 6G RAN/Core risks
Dell’Oro: Analysis of the Nokia-NVIDIA-partnership on AI RAN
RAN silicon rethink – from purpose built products & ASICs to general purpose processors or GPUs for vRAN & AI RAN
Dell’Oro: AI RAN to account for 1/3 of RAN market by 2029; AI RAN Alliance membership increases but few telcos have joined
Dell’Oro: RAN revenue growth in 1Q2025; AI RAN is a conundrum
Analysis: Nokia’s new AI-RAN platform and Standalone AI-RAN node with Nvidia GPUs
Nokia today announced what it describes as the first commercial AI-RAN platform, signaling a potentially significant inflection point in radio access network architecture. As AI workloads increasingly influence network design and operations, service providers are under pressure to deliver higher capacity, improved cost efficiency, and faster service innovation without depending on traditional hardware refresh cycles. Nokia’s AI-RAN platform is positioned to address these requirements by extracting greater uplink and downlink performance from existing spectrum and radio assets, while enabling a transition toward AI-native network architectures through software-centric evolution. Key take-aways:
- Nokia launches the industry’s first commercial AI-RAN platform, turning AI-RAN from vision into reality and providing a practical path to AI-native networks.
- Built on Nokia’s AI-native anyRAN software and NVIDIA’s Aerial AI-RAN platform, it will deliver more than 100% spectral efficiency gains by 2028, doubling the capacity of existing spectrum assets.
- Nokia’s anyRAN software will support three new accelerated computing baseband platforms. In addition, its existing portfolio will be fully ORAN compliant so operators can modernize at their own pace.
“AI-RAN is the biggest innovation in radio in decades. AI-RAN makes the network intelligent, extends AI into the physical world, and allows telcos to get more from their existing infrastructure, including a software upgrade path to 6G. Nokia’s anyRAN software, powered by NVIDIA’s Aerial AI-RAN platform, unlocks greater performance from the spectrum operators already have and can be deployed with existing Nokia or ORAN-compliant radio units. For operators, that means more performance, better returns and faster delivery of new services,” said Justin Hotard, President and Chief Executive Officer at Nokia.
The platform is built on Nokia’s AI-native network architecture and leverages NVIDIA’s accelerated computing stack to enable AI-driven radio optimization. Initial results indicate spectral efficiency gains exceeding 20% through AI-enhanced radio resource management and signal processing techniques. Nokia’s roadmap targets up to 50% gains by 2027 and greater than 100% by 2028, with the objective of increasing capacity in dense deployments while reducing cost per bit and improving user experience.
“Telecommunications is entering the AI era — the radio access network is the next AI infrastructure,” said Jensen Huang, CEO and founder of NVIDIA. “Together with Nokia, we are bringing NVIDIA CUDA and AI into the baseband, transforming RAN into a planet-scale AI computer. This is a generational shift for operators — unlocking more capacity and efficiency from today’s spectrum while creating the foundation for new AI services and the 6G era.”
Image credit: Nokia
A key element of the offering is a software subscription model that enables operators to access ongoing AI-driven enhancements, feature updates, and performance improvements independent of hardware upgrade cycles. Nokia expects pilot deployments to begin by the end of the year, with broader commercial availability targeted for 2027. The roadmap incorporates NVIDIA’s programmable merchant silicon to support continued performance scaling and feature evolution.
“Nokia’s AI-RAN launch represents an important step in bringing AI-RAN from industry vision to commercial reality. The addition of the new AI-RAN node alongside the AirScale capacity plug-in unit and cloud-native deployment options gives operators practical choices for adopting AI-native networks based on their existing infrastructure and transformation goals. By combining AI-accelerated computing with a software-defined architecture and a clear product roadmap, Nokia is helping operators unlock greater capacity, improve network economics and accelerate the transition toward AI-native RAN,” commented Rémy Pascal, Practice Leader, Mobile Infrastructure at Omdia.
Light Reading’s Iain Morris wrote:
Nokia’s vision, outlined during an exclusive interview with Light Reading, is that the Nvidia-based hardware products announced today and available from next year will potentially last customers deep into the 6G era. That cannot be said of the latest Nokia hardware in commercial use, based on custom silicon provided by Marvell Technology, according to Atkinson. The same would be true of the latest hardware from rival Ericsson, he believes: “It will get them into 6G, but it won’t see them long into 6G.”
The Nokia pivot to GPUs sets up a riveting clash between the two Nordic companies. Ericsson remains firmly attached to its own custom silicon, and its top executives think writing code for CUDA, Nvidia’s software platform, could make Nokia a victim of “vendor lock-in,” imprisoned by a single vendor’s hardware.
For telcos interested in general-purpose processors and the greater freedom they promise, Ericsson instead offers a set of “virtual” RAN products. While based today on Intel’s central processing units (CPUs), the same software can run on CPUs from other chipmakers after minimal tweaks, according to Ericsson. Only a small amount of code remains hardware-dependent, it says.
,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
Nokia’s AI-RAN platform is based on a common, software-defined architecture integrating its anyRAN software with NVIDIA’s accelerated computing. The platform supports 4G, 5G, and forward evolution toward 6G, while maintaining compliance with Open RAN specifications to enable multi-vendor interoperability. Operators can select from multiple deployment options aligned with their installed base and transformation strategy, while leveraging a unified software roadmap.
For network operators with existing Nokia AirScale deployments, the introduction of a GPU-accelerated capacity plug-in unit provides a relatively low-friction upgrade path. This approach integrates accelerated computing into the current RAN footprint, enabling step-function capacity improvements while preserving prior infrastructure investments. Nokia also highlights support for AI-optimized merchant silicon, including contributions from partners such as Marvell, reflecting a broader ecosystem strategy for AI-RAN evolution.
Nokia is also introducing a GPU-based standalone AI-RAN node designed for flexible deployment across diverse network environments. The platform supports 4G, 5G, and future 6G workloads and can be deployed as a discrete node, in clustered configurations, or integrated with existing AirScale systems as a logical baseband. This enables scalable deployment of AI-native capacity while maintaining architectural flexibility.
For network operators pursuing cloud-native RAN strategies, Nokia’s AI-RAN capabilities extend to GPU-enabled COTS server platforms delivered through ecosystem partners. This approach supports deployment on standardized, accelerated infrastructure while aligning with open and secure supply chain principles. It combines cloud-native operational models with the performance requirements of AI-intensive RAN workloads.
Nokia’s AI-RAN introduces a shift from hardware-centric lifecycle management to a software-driven innovation model. Through its subscription-based framework, operators gain continuous access to evolving AI algorithms, spectral efficiency enhancements, and network optimization capabilities. This model enables ongoing improvements in performance, efficiency, security, and resilience without requiring discrete hardware upgrades, thereby improving total cost of ownership and extending asset lifecycles.
By combining AI-accelerated computing, software-defined RAN architecture, and an open ecosystem approach, Nokia’s AI-RAN platform is intended to provide operators with a scalable pathway to increased capacity, improved economic performance, and continuous innovation as networks evolve toward the 6G era.
References:
https://www.nokia.com/radio-access/ai-ran/
https://www.lightreading.com/6g/nokia-says-long-term-6g-is-not-doable-without-nvidia
Dell’Oro: AI RAN revenue forecast: $35B from 2026-to-2030; 3 types of AI RAN explained
According to a new AI RAN Advanced Research Report published by Dell’Oro Group, cumulative AI RAN revenue is projected to reach $35 B over the next five years (2026-2030). However, AI RAN is not expected to expand the overall RAN market.
“Our market assessment and long-term AI RAN position remain unchanged,” said Stefan Pongratz, Vice President at Dell’Oro Group. “AI RAN is already happening and will scale ahead of 6G. At the same time, these tools will enhance the RAN, but they are unlikely to expand the overall RAN market. Even as suppliers introduce new software-based subscription models, we expect AI RAN to generate little, if any, incremental RAN revenue the end of the forecast period,” continued Pongratz.
Additional highlights from the June 2026 AI RAN Advanced Research Report:
- The base-case forecast assumes that AI RAN will not expand the RAN market. Nevertheless, AI RAN is expected to become an important technology enabler as operators incorporate greater virtualization, intelligence, automation, and O-RAN capabilities into their RAN roadmaps.
- GPU RAN projections have been revised upward—GPU RAN is now expected to be a $1 B+ market by the end of the forecast period.
- In the near term, the AI RAN market will remain centered on AI-for-RAN, single-purpose deployments, non-GPU architectures, D-RAN, and 5G.
- Incumbent RAN radio and baseband suppliers are well-positioned in the initial AI RAN phase, driven primarily by AI-for-RAN upgrades leveraging existing hardware. Per Dell’Oro Group’s regular RAN coverage, the top five RAN suppliers contributed approximately 96 percent of 2025 RAN revenue. See charts below.
Dell’Oro Group’s AI RAN Advanced Research Report includes a 5-year forecast for AI RAN by location, tenancy, technology, and region. To purchase this report, please contact us at [email protected].
………………………………………………………………………………………………………………………………………………………………………….
Total & Wireless Telecom Equipment Revenue- top 4 and top 3:
………………………………………………………………………………………………………………………………………………………………………………….
Analysis (Source: Perplexity.ai AND Google Gemini):
There are three versions of AI-RAN which are not mutually exclusive:
- AI for RAN: Embeds AI into the base software stack to automatically manage radio waves, optimize spectrum efficiency, enhance beamforming, and reduce energy consumption in real time. Nokia, Ericsson, NVIDIA.
- AI on RAN: Uses cell towers and base stations as decentralized computing nodes. This allows telecom networks to host AI workloads locally rather than sending all data to distant cloud servers, providing ultra-low latency for applications like robotics, AR/VR, and autonomous vehicles. Nokia, NVIDIA, and operator trial partners like T-Mobile, Indosat, and SoftBank.
- AI and RAN: Combines the two to support “Networks for AI,” where distributed telecom networks act as an active, intelligent backbone to serve end-user AI traffic. AI-RAN Alliance plus Nokia and NVIDIA as the most visible industry champions.
References:
AI RAN to Reach $35 B Over Next Five Years, According to Dell’Oro Group
NVIDIA AI RAN video: youtube.com/watch?v=hwLLBfzoSko&t=26
AI-Era Cloud Network Transformation: A Reference Architecture and Implementation Roadmap
Ericsson goes with custom silicon (rather than Nvidia GPUs) for AI RAN
Dell’Oro: RAN Market Stabilized in 2025 with 1% CAG forecast over next 5 years; Opinion on AI RAN, 5G Advanced, 6G RAN/Core risks
AI-RAN Reality Check: hype vs hesitation, shaky business case, no specific definition, no standards?
Analysis: Nvidia’s rumored new 6G AI-RAN – likely features/functions and industry impact
Dell’Oro: 2H2026 Data Center Capex to Accelerate due to massive AI Deployments
Dell’Oro: Analysis of the Nokia-NVIDIA-partnership on AI RAN
RAN silicon rethink – from purpose built products & ASICs to general purpose processors or GPUs for vRAN & AI RAN
Dell’Oro: AI RAN to account for 1/3 of RAN market by 2029; AI RAN Alliance membership increases but few telcos have joined
Dell’Oro: RAN revenue growth in 1Q2025; AI RAN is a conundrum
AI-RAN and Agentic AI get real: Ericsson, Nokia, Verizon & other operators enter into a new network automation era
Disclaimer: Perplexity.ai was used for research in this article.
Executive Summary:
A cluster of announcements in early-to-mid June 2026 signals a real shift from AI research to commercial AI-driven network automation. Telcos are transitioning from isolated AI pilots to production-grade AI operations deployed across live networks.
-
Ericsson launched its AI in RAN commercial software subscription on June 11th, claiming up to 20% higher downlink throughput and up to 10% better spectral efficiency across more than 15 live deployments using existing baseband silicon.
-
Nokia and Indosat Ooredoo Hutchison (Indonesia) announced a GPU-accelerated AI-RAN partnership in Indonesia on June 8, expanding the Nokia–NVIDIA architecture already adopted by T-Mobile US, SoftBank, and Vodafone.
-
Verizon disclosed that its 60,000-site vRAN is now applying agentic AI to planned configuration changes, service assurance, and network optimization, while publicly calling for industry-wide interoperability standards for agentic systems.
-
Nokia launched an agentic AI framework for IP network operations within its Network Services Platform (NSP), marking its third agentic product announcement in a four-week period.
A growing number of network operators are transitioning from traditional connectivity providers into AI infrastructure operators. SK Telecom (South Korea) announced a gigawatt-scale AI Cloud built on NVIDIA DGX SuperPOD architecture; Deutsche Telekom (Germany) secured the German federal government’s sovereign AI cloud contract; and MTN Group (South Africa) detailed a plan to convert 18,000 African tower locations into a distributed AI inference grid.
Over a six-week window, six major network operators—SK Telecom, Deutsche Telekom, MTN Group, Verizon, SoftBank, and Indosat Ooredoo Hutchison—have converged on a single strategic premise: existing telecommunications infrastructure, including connectivity, physical real estate, and data center capacity, constitutes the foundational footprint for a commercial AI compute business.
………………………………………………………………………………………………………………………………………………………………………………………………
Government’s Buys Into AI Compute:
Government involvement in AI compute is intensifying, with direct implications for telecom strategy. China’s $295 billion program defines the upper bound of state-backed AI compute investment. Beijing announced plans to invest 2 trillion yuan ($295 billion) over five years in AI datacenter infrastructure. China Mobile and China Telecom are designated as the primary operators of a national AI compute network, while Huawei will supply the majority of AI chips—explicitly bypassing NVIDIA. The plan accelerates China’s original 2030 national computing network target to 2028, funded through sovereign debt.
- China’s National Data Administration reported 140 trillion daily AI token flows by March 2026—up 1,400-fold from the start of 2024. China Mobile, China Telecom, and China Unicom launched commercial AI token packages in May, with per-token costs that centralized operations can reduce by approximately 30 percent. China Mobile separately unveiled AI-eSIM, which embeds an autonomous decision layer directly into the SIM.
- Chinese network operators are advancing through the full AI lifecycle—token economy, infrastructure mandate, and sovereign chip supply—at a pace and scale unmatched by any other single market.
Technical Implications for Network Architecture:
The convergence of AI-RAN, agentic AI, and AI infrastructure provision demands architectural evolution across several dimensions:
Standards and Interoperability Gap:
Verizon’s public call for industry-wide interoperability standards for agentic systems highlights a critical bottleneck. Agentic AI frameworks from Ericsson, Nokia, and other vendors must interoperate across multi-vendor networks, yet no standardized protocol exists for agentic command, control, and assurance. This gap mirrors the early RAN interoperability challenges that Open RAN later addressed.
The TM Forum’s Autonomous Networks L4/5 roadmap and the 3GPP 6G standardization process will need to incorporate agentic AI interoperability as a core requirement. Without standards, telcos risk vendor lock-in for AI automation capabilities, undermining the multi-vendor flexibility that has been a telco industry priority for decades.
What This Means for 5G and 6G Roadmaps:
AI-driven automation is becoming a prerequisite for 6G L4/5 autonomous networks. The June 2026 announcements suggest that:
-
5G Advanced deployments will increasingly incorporate AI-RAN as a standard feature, not an optional enhancement.
-
6G specifications (expected by end-2028 per Ericsson) will likely embed agentic AI and autonomous decision layers as core architectural elements.
-
Network economics will shift from bandwidth-centric to compute-centric revenue models, with AI token services and inference grids becoming significant revenue streams.
For network architects, the implication is clear: AI infrastructure must be designed as a first-order network capability, not a second-order application layer. GPU acceleration, agentic orchestration, and token-economy support need to be part of the baseline network architecture from the outset.
Conclusions — The Automation Tipping Point:
June 2026 marks a tipping point where AI-driven network automation transitions from pilot to production. The combination of commercial AI-RAN subscriptions, agentic AI deployments at tens-of-thousands-of-site scale, and telco-led AI infrastructure provision signals that AI is no longer an experimental capability but a core network function.
The critical question for telcos is not whether to adopt AI automation, but how to avoid vendor lock-in while achieving the interoperability required for multi-vendor, multi-domain autonomous networks. Standards bodies, operator consortia, and vendor alliances must address this gap before agentic AI becomes a strategic constraint rather than a competitive advantage.
………………………………………………………………………………………………………………………………………………………………………………………………
References:
https://mtnconsulting.substack.com/p/the-unmanned-network-17-june-2026
Cisco Execs: New “Network Supercycle” as Agentic AI Workloads Reshape Telecom Infrastructure
Cisco Execs: New “Network Supercycle” as Agentic AI Workloads Reshape Telecom Infrastructure
STL Partners webinar: Agentic AI needed for RAN autonomy & efficiency
The Financial Trap of Autonomous Networks: Scaling Agentic AI in the Telecom Core
Nokia to showcase agentic AI network slicing; Ericsson partners with Ookla to measure 5G network slicing performance
T-Mobile US announces new broadband wireless and fiber targets, 5G-A with agentic AI and live voice call translation
Telecom operators investing in Agentic AI while Self Organizing Network AI market set for rapid growth
Ericsson integrates Agentic AI into its NetCloud platform for self healing and autonomous 5G private networks
Agentic AI and the Future of Communications for Autonomous Vehicles (V2X)
Ericsson’s June 2026 Mobility Report Highlights + AI impact on network traffic
Ericsson launches AI in RAN as commercial software subscription:
Ericsson goes with custom silicon (rather than Nvidia GPUs) for AI RAN
Dell’Oro: Analysis of the Nokia-NVIDIA-partnership on AI RAN
RAN silicon rethink – from purpose built products & ASICs to general purpose processors or GPUs for vRAN & AI RAN
RAN Silicon Rethink- Part II; vRAN and General-Purpose Compute
Analysis: Nvidia’s rumored new 6G AI-RAN – likely features/functions and industry impact
Analysis: Nvidia’s $2 billion investment in Marvell; NVLink Fusion ecosystem & RAN vendor silicon strategy
Dell’Oro: AI RAN to account for 1/3 of RAN market by 2029; AI RAN Alliance membership increases but few telcos have joined
