Big Data & Analytics

Verizon Expands 6G Innovation Forum with Nine New Members

API TIT — Wed, 16 Sep 2026 12:45:34 +0000

Verizon has significantly broadened the scope of its collaborative push toward next-generation wireless technology by welcoming nine major companies into the 6G Innovation Forum. The newly added members are Amazon Web Services, Cisco, Intel, Keysight Technologies, MediaTek, NVIDIA, Palo Alto Networks, Rohde & Schwarz and VIAVI Solutions. Together with founding participants Ericsson, Samsung, Nokia, Meta and Qualcomm Technologies, the expanded 6G Innovation Forum now represents a wide cross-section of the global technology landscape spanning cloud infrastructure, silicon design, cybersecurity, automated testing and accelerated computing.

The forum was originally established to build an open and resilient 6G ecosystem focused on developing foundational technologies, trialing emerging use cases, exploring new spectrum bands and ensuring alignment with global standards bodies such as 3GPP. With the addition of these nine organizations, the group gains deeper multi-disciplinary expertise that is expected to strengthen work across AI integration, cloud services, network security and independent test emulation, all of which are critical to shaping an AI-native network architecture from the ground up.

Yago Tenorio, Chief Technology Officer and Senior Vice President of Technology Development at Verizon, said, “By expanding our Innovation Forum and refining live edge-AI trials today, we are ensuring the AI-native 6G network is built from day one to scale 5G Advanced capabilities and translate these next-generation technologies into meaningful, real-world experiences for our customers.”

Successful ISAC Trials Demonstrate Practical 6G Capabilities

To move beyond theoretical research, Verizon highlighted recent field trials with founding members Samsung and Qualcomm centered on Integrated Sensing and Communication, commonly known as ISAC. This technology enables cellular networks to function as distributed, radar-like sensors without requiring physical cameras, integrating communication and sensing within the same infrastructure.

In a trial conducted with Samsung during a major international soccer celebration in Dallas, Texas, the companies demonstrated AI-powered crowd-density detection using a virtualized Radio Access Network, a single 5G base station and CBRS spectrum. Samsung’s AI RAN and edge AI platform analyzed ambient wireless signal variations from smartphones to produce real-time crowd-density heatmaps, showcasing how network-native sensing could support large-scale event management. This marked the first such ISAC trials demonstration on a virtualized RAN for crowd detection.

A separate trial with Qualcomm at its San Diego campus focused on drone and vehicle tracking. Using millimeter-wave spectrum and four synchronized transmission and reception points, the system mapped the position and velocity of an airborne target and ground vehicles while simultaneously maintaining high-speed 5G Standalone communications with a phone user on the ground. The results underscored how next-generation wireless networks could provide foundational tracking infrastructure for autonomous drone safety and similar applications. These ISAC trials represent tangible progress toward capabilities that are expected to mature further with 6G.

Expanded 6G Ecosystem Sets the Stage for Future Deployment

Looking ahead, the members of the 6G Innovation Forum plan to continue sharing findings from demonstrations inx`volving digital twins, robotics, AI and advanced wearables. Verizon has identified the 2028 Summer Olympics in Los Angeles as an interim proving ground on the path toward commercial 6G deployment, with a dedicated 6G Lab already established in the host city to stress-test capacity demands and early use cases of the AI-native network era. The expanded 6G ecosystem positions the broader industry to collaboratively address the technical and operational challenges that lie ahead.

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5G Advanced Expanding AI and Machine Learning Across Networks

API TIT — Mon, 07 Sep 2026 10:59:25 +0000

Artificial intelligence and machine learning are becoming increasingly embedded in the operation of mobile networks as 5G-Advanced develops. The focus is moving beyond using AI to analyse network information toward applying machine learning directly to functions that influence network performance, resource allocation and operational efficiency. This is giving 5G advanced AI a broader role within the network itself.

3GPP has been developing the technical foundations for this shift through its 5G-Advanced standardisation work. Rel-18 introduced AI/ML support for the 5G radio access network, including procedures for data collection, model training and inference. This creates a framework in which network measurements can be processed by models and the resulting outputs used to support specific network functions.

AI/ML is Moving Deeper into Network Operations

The initial use cases provide a clear indication of where this development is heading. 3GPP Rel-18 identified AI/ML applications for network energy saving, load balancing and mobility optimisation. These are operational functions that directly affect how networks use resources and respond to changing traffic and mobility conditions.

Energy-saving applications can use network conditions to help determine when resources can be reduced or reconfigured. Load balancing can analyse traffic and network conditions to support more effective resource allocation, while mobility optimisation can use collected measurements to improve decisions around movement between cells.

These applications show that 5G advanced AI is becoming associated with network decisions rather than being limited to a separate analytics layer. The effectiveness of these functions depends on the availability of relevant operational data, suitable models and mechanisms for applying model outputs within the network.

Standardisation is Expanding the Scope of Network Intelligence

The development is continuing through later 5G-Advanced releases. Rel-19 work has expanded AI/ML applications into areas including network slicing and coverage and capacity optimisation. At the same time, AI/ML management work is addressing functions across the 5G radio access network, 5G Core Network and management data analytics.

This broader scope increases the number of network functions that can potentially use machine learning. It also introduces more requirements around how data is collected, how models are trained and deployed, and how inference is managed after deployment. Data quality therefore becomes an important part of the overall architecture, alongside model performance.

Key Takeaway: 5G-Advanced AI/ML standardisation is expanding from defined optimisation functions toward broader intelligence across network operations and management.

The progression suggests that 5G advanced AI is becoming part of the underlying evolution of mobile network operations. As AI/ML capabilities expand across functions and network domains, 5G-Advanced is creating a more structured foundation for increasingly intelligent and automated networks.

5G-Advanced AI is Expanding Beyond Individual Network Functions

The development of AI and machine learning in 5G-Advanced is moving from individual optimisation tasks toward a broader network management capability. As standards work expands the number of functions that can use AI/ML, networks are increasingly being designed to collect information, train or apply models and use their outputs as part of ongoing operational processes.

The shift is particularly visible in network slicing and coverage and capacity optimisation. These use cases require the network to process changing conditions and produce information that can support decisions over time, moving the role of 5G advanced AI beyond static analysis toward more continuous optimisation.

Network Intelligence is Expanding Beyond RAN Optimisation

Network slicing provides one example of this broader role. Rel-19 work includes AI/ML-assisted mechanisms that can use predicted slice-level information and user-equipment performance feedback. Rather than assessing network conditions only after they occur, the approach is intended to support the use of predictive information within network management.

Coverage and capacity optimisation follows a similar direction. AI/ML can be used to identify potential future coverage or capacity issues and provide predicted information that can support optimisation decisions. This is important because network demand changes continuously across locations, users and time periods. A model capable of anticipating those changes could help operators adjust network resources before performance deteriorates.

The development also increases the importance of model lifecycle management. AI/ML functions require more than a trained algorithm. Networks need mechanisms for model training, testing, deployment, transfer, inference and ongoing management. 3GPP’s work on AI/ML management is therefore expanding alongside the use cases themselves.

From Network Data to Continuous Optimisation

The wider architecture also brings AI/ML closer to multiple layers of the network. 3GPP management work covers AI/ML capabilities across the 5G radio access network, 5G Core Network and management data analytics. This creates a broader environment in which information generated by different network functions can contribute to optimisation and decision-making.

The result is a more continuous relationship between data and network behaviour. Operational information can be collected from network elements and user equipment, processed through models and used to influence subsequent network decisions. 5G advanced AI is therefore becoming less about a single automated function and more about creating feedback mechanisms that allow networks to adjust to changing conditions.

This also creates practical requirements around data governance. Models depend on the relevance, volume and quality of the information used to train and operate them. As AI/ML expands across network functions, operators will need to manage data collection, model performance and lifecycle processes alongside conventional network operations.

The direction of standardisation suggests that 5G advanced AI will increasingly support networks that can anticipate conditions, optimise resources and adapt operations with less manual intervention. That progression provides an important bridge between current 5G-Advanced capabilities and the more deeply intelligent architectures being explored for 6G.

5G-Advanced AI is Becoming a Foundation for Network Intelligence

The evolution of 5G-Advanced is extending AI and machine learning from individual optimisation functions toward broader network intelligence. As standardisation expands into areas such as network slicing, coverage and capacity optimisation and AI/ML lifecycle management, networks are gaining more mechanisms to respond to changing conditions using operational data.

This is making 5G advanced AI increasingly relevant to how networks manage resources, anticipate performance issues and automate operational decisions. The development also creates a foundation for the transition toward 6G, where greater intelligence is expected to be integrated more deeply into network architecture.

The direction of 5G-Advanced standardisation suggests that 5G advanced AI will play an increasingly important role in creating networks that can analyse conditions, adapt operations and optimise performance with less manual intervention.

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AI Moving into Radio Resource Management

API TIT — Thu, 03 Sep 2026 07:22:51 +0000

Radio resource management is becoming a more important application for artificial intelligence as mobile networks become more dynamic and traffic patterns become harder to predict. Operators must continuously balance limited spectrum, radio capacity, transmission power and physical resources against changing user demand, mobility and interference. Traditional RRM approaches depend largely on predefined rules and optimisation algorithms, but increasingly variable network conditions are creating an opportunity for more adaptive decision making. This is where AI Radio resource management is beginning to gain importance.

The objective is not simply to monitor network performance. AI can analyse radio measurements and help determine how available resources should be allocated across users and cells. Scheduling, link adaptation, beam management and interference coordination are among the areas where machine learning can identify relationships that may be difficult to capture through fixed rules.

Recent commercial testing provides evidence that this approach is moving into live network environments. A 2026 commercial 5G validation of an AI native scheduler focused on link adaptation reported maximum improvements of approximately 25% in spectral efficiency and 50% in downlink user throughput. Average gains across evaluated locations were approximately 10% for both measures. The results are specific to the tested deployment, but they demonstrate how AI can influence radio decisions with measurable performance effects.

Scheduling is Becoming More Adaptive

Scheduling is one of the clearest applications because the network must continuously determine how available radio resources are distributed between users. Demand can change rapidly between cells, while users can experience very different channel conditions at the same time. AI models can process current measurements alongside historical patterns to support allocation decisions that adapt to those changes.

Link adaptation is closely connected to scheduling. The network needs to select transmission conditions that maximise data rates without increasing error rates beyond acceptable levels. AI can incorporate signal quality, interference, mobility and previous performance to support more responsive decisions.

The O RAN Alliance is incorporating these capabilities into its evolving architecture. Its 2026 Release 5 introduced enhancements for massive MIMO optimisation and AI and machine learning workflows connecting the Non Real Time RAN Intelligent Controller and Near Real Time RAN Intelligent Controller. These developments provide a framework for intelligent applications to influence network optimisation across different timescales.

The potential therefore extends beyond throughput. AI Radio resource management can be designed to balance competing objectives such as latency, reliability, energy consumption and user fairness. That becomes more important as a single network supports services with different performance requirements and increasingly variable traffic patterns.

Key Takeaway: AI based scheduling and link adaptation can improve radio performance, although results vary by location and operating conditions.

AI is Making Radio Resource Allocation More Adaptive

The next stage of AI Radio resource management is moving from isolated optimisation functions toward coordinated control across the RAN. The O RAN architecture provides a framework for this through the Non Real Time RAN Intelligent Controller and Near Real Time RAN Intelligent Controller, allowing applications to operate at different timescales. This is important because resource decisions range from longer term traffic planning to near real time scheduling, beam management and slice allocation.

The O RAN Alliance’s Release 5, completed in 2026, strengthened this architecture through enhanced AI and machine learning workflow services connecting the two RIC environments. The release also introduced improvements for massive MIMO optimisation and network energy saving, showing that intelligent control is expanding across several dimensions of RAN resource management.

Closed Loop Control is Bringing AI Closer to the Radio

The objective is increasingly to create a continuous decision cycle. Network conditions can be measured, an AI application can identify a potential optimisation, the change can be applied and its effect can then be measured again. This creates a closed loop rather than a process where network data is simply reviewed before an engineer decides what to do.

Research is also moving toward more adaptive resource allocation. A September 2026 study proposed a fuzzy logic based xApp for dynamic physical resource block allocation in O RAN network slicing. The research addresses fluctuating traffic and different service requirements across eMBB, URLLC and mMTC, demonstrating how resource allocation can respond to changing network conditions instead of depending entirely on static policies.

Another IEEE INFOCOM 2026 study tested OnlineCritic xApp for RAN slice resource management under non stationary traffic and changing channel conditions. Its simulations showed up to 7.4% higher average utility and 13.1% higher reliability compared with proportional physical resource block allocation. These results are simulation based rather than commercial network measurements, but they provide evidence of the direction of research into adaptive RRM.

AI is Expanding Resource Allocation Beyond Throughput

A conventional optimisation system may prioritise aggregate throughput, but modern networks increasingly need to balance several objectives simultaneously. A service requiring very low latency may need a different resource allocation strategy from a high bandwidth application, while energy sensitive workloads can introduce another constraint.

This makes AI Radio resource management particularly relevant to network slicing. AI models can consider traffic demand, channel conditions and service requirements together, potentially allowing physical resource blocks, power and scheduling opportunities to be adjusted according to changing priorities.

Research is also addressing the problem of multiple intelligent applications operating at the same time. A 2026 ITU Journal study examined an ML driven network orchestrator designed to resolve conflicts between multiple xApps in the Near Real Time RIC. The researchers identified overlapping control commands and competition for network resources as potential problems when several applications attempt to influence the RAN simultaneously.

Key Takeaway: AI assisted resource allocation can improve utility and reliability under changing traffic and channel conditions, although the reported results are based on simulation rather than commercial network deployment.

The progression toward AI Radio resource management is therefore creating a more adaptive approach to scarce radio resources. Instead of optimising each parameter independently, intelligent applications can increasingly consider several network objectives together and adjust allocations as conditions change. This also means that orchestration and coordination will become more important, since multiple AI applications must operate within defined limits without producing conflicting actions.

The O RAN Alliance’s continuing work reinforces this direction. Its 2026 specification activity includes AI and machine learning workflows, massive MIMO optimisation, spectrum efficiency improvements and interfaces supporting intelligent network functions.

The result is a RAN in which resource management is becoming increasingly software driven and adaptive. AI Radio resource management is moving closer to the operational control layer, but its success will depend on whether these systems can deliver useful decisions quickly, maintain reliability and operate safely within the strict constraints of live mobile networks.

AI is Moving RRM Toward Continuous Optimisation

Radio resource management is moving from fixed optimisation rules toward more adaptive systems that can respond to changing traffic, interference, channel conditions and service requirements. The growing use of AI in scheduling, link adaptation, beam management and network slicing is giving operators new ways to manage limited radio resources while balancing performance, reliability and energy efficiency.

The next challenge is making these systems reliable enough for continuous network control. AI models must produce decisions within strict latency requirements, work with accurate and consistent network data, and remain predictable when conditions change. They also need safeguards to prevent conflicting or incorrect automated actions from affecting live networks.

This makes AI Radio resource management increasingly important to the development of intelligent RAN architectures. The objective is not simply to automate existing processes, but to create systems that can continuously evaluate network conditions, allocate resources and adjust their decisions as requirements evolve. As RAN intelligence develops, the ability to combine AI with real time control could become an increasingly important part of network performance and resource efficiency.

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AI RAN Moving Network Management Toward Real Time Optimisation

API TIT — Thu, 03 Sep 2026 07:06:42 +0000

Radio access networks have traditionally relied on predefined rules, engineering thresholds and manual intervention to manage changing traffic and radio conditions. That approach remains important, but the increasing complexity of 5G networks is creating demand for systems that can respond continuously to changing conditions. Artificial intelligence is beginning to address this gap by analysing network data in real time and supporting decisions around resource allocation, scheduling, link adaptation, beamforming and energy use.

This is shifting AI RAN network optimization from a longer-term technology concept toward a practical network-management capability. Rather than waiting for engineers to identify performance problems and adjust parameters, AI-based systems can analyse large volumes of network information, identify patterns and determine how particular conditions are likely to affect performance.

The commercial motivation is increasingly clear. GSMA Intelligence’s 2026 Network Transformation research found that 85% of operators identified operating-expenditure efficiencies as a priority business objective for deploying AI in their networks. The figure was almost three times the proportion focused on using AI to deliver new services, indicating that near-term operator interest is primarily centred on improving network economics and operational performance rather than generating entirely new revenue streams.

Market forecasts also indicate that AI is becoming a significant part of future RAN investment. Dell’Oro forecasts cumulative AI RAN revenue of approximately $35 billion between 2026 and 2030. Its analysis expects the initial market to be centred on AI-for-RAN applications, single-purpose deployments, non-GPU architectures, distributed RAN and 5G, rather than immediately shifting toward large-scale GPU-based RAN.

AI is Changing How RAN Performance is Managed

The difference between conventional optimisation and AI-driven optimisation lies in how network decisions are generated. Traditional systems generally depend on predefined rules that determine how a network should respond to specific conditions. AI models can instead learn from historical and real-time network data, allowing them to identify relationships between radio conditions and performance outcomes.

Link adaptation is one example. Radio conditions can change rapidly as users move, interference fluctuates and traffic patterns shift. AI models can use these changes to predict appropriate transmission parameters rather than relying entirely on static rules.

Recent commercial-network testing demonstrates the potential. A 2026 validation of an AI-native scheduler for link adaptation reported close to a 10% increase in spectral efficiency and up to a 15% increase in downlink throughput compared with legacy rule-based methods. These results came from a specific network validation and should not be treated as an industry-wide benchmark, but they demonstrate how AI can influence RAN performance at the operational level.

The role of AI RAN network optimization also extends beyond individual radio parameters. AI can support traffic forecasting, anomaly detection, energy management and broader resource allocation, allowing multiple network variables to be considered together.

Key Takeaway: Operators are primarily evaluating AI through its potential to improve network efficiency and operating economics, reinforcing the shift toward AI-driven network optimisation.

The next stage will be to make these decisions increasingly adaptive. AI RAN network optimization can move network management from identifying performance issues after they occur toward predicting network behaviour and adjusting resources before conditions deteriorate. As these capabilities mature, the value of AI will increasingly depend on how quickly and reliably it can translate network data into measurable improvements in performance, efficiency and service quality.

AI is Improving Performance, Efficiency and Network Autonomy

The value of AI in the RAN is increasingly being measured through specific operational outcomes rather than broad claims about automation. Network operators are testing AI models for link adaptation, beamforming, traffic management, energy optimisation and other functions where conditions can change continuously. This is moving AI RAN network optimization toward a more measurable stage, where improvements can be assessed through throughput, spectral efficiency, energy consumption and resource utilisation.

The shift is also being reflected in the development of the O-RAN architecture. The O-RAN ALLIANCE’s Release 5, completed in 2026, introduced enhanced AI and machine learning workflow services connecting the Non-Real-Time RAN Intelligent Controller and Near-Real-Time RAN Intelligent Controller. The release also added capabilities for massive MIMO optimisation and energy saving, providing a standardised architecture through which AI and machine learning applications can influence RAN operations.

Real Time AI is Improving Radio Performance

Link adaptation provides one of the clearest examples of what this technology can achieve. Radio conditions can change rapidly because of interference, user movement, traffic congestion and variations in signal quality. Traditional approaches use rule-based algorithms and predefined parameters to determine how the network should respond.

Recent commercial-network testing shows how AI can change this process. A 2026 validation of an AI-native scheduler operating within a 5G commercial network reported improvements of up to approximately 25% in spectral efficiency and 50% in downlink user throughput compared with conventional technology. Across all evaluated locations, the average improvement was approximately 10% for both measures. The results came from a specific commercial-network validation and should not be treated as an industry-wide benchmark, but they demonstrate the potential for AI to make real-time RAN decisions based on changing radio conditions.

The technical challenge is that AI decisions must be produced quickly enough to remain useful. RAN functions can operate under extremely tight timing requirements, meaning an accurate model may still be unsuitable if inference introduces excessive latency. This makes model size, hardware acceleration and execution efficiency important alongside prediction accuracy.

Energy Efficiency is Becoming Another AI Use Case

Performance is not the only objective. Mobile networks consume substantial amounts of energy, and traffic levels can vary considerably throughout the day. AI can help identify periods of lower demand and adjust network resources accordingly while maintaining service requirements.

The O-RAN Release 5 specifications specifically include network energy-saving improvements, alongside support for O-RU-specific energy-saving functions. This shows that energy management is becoming part of the standardised intelligent RAN architecture rather than an isolated optimisation application.

Commercial deployments are also testing this approach. Nokia reports that AI-based energy-management systems have achieved reductions in power consumption while maintaining network performance, demonstrating the potential for AI to balance network capacity and energy use dynamically. These results are deployment-specific, but they reinforce the broader move toward using AI to manage resources according to real-time demand.

AI is Moving Toward Closed Loop Network Control

The next stage is moving beyond recommendations toward automated action. A conventional analytics system may identify congestion and alert an engineer. A more advanced AI system can predict the problem, determine an appropriate response and apply the change automatically within predefined operational limits.

This is where the RAN Intelligent Controller becomes important. The O-RAN architecture separates longer-timescale optimisation in the Non-Real-Time RIC from near-real-time control through the Near-Real-Time RIC, creating a framework for applications that can influence network behaviour at different timescales. Release 5 further strengthens this architecture through AI and machine-learning workflow services.

Recent industry development is also moving toward agentic approaches in which multiple AI agents can monitor network conditions, analyse potential actions and coordinate changes. The objective is not simply to automate an individual parameter but to create a continuous decision cycle in which the network can respond to changing conditions with less manual intervention.

Key Takeaway: Commercial-network testing is showing measurable performance gains from AI-based RAN optimisation, although the scale of improvement depends on the application and network conditions.

The implications extend beyond individual optimisation functions. As AI RAN network optimization becomes more integrated with RAN controllers and orchestration systems, operators can potentially coordinate performance, capacity and energy decisions rather than managing each variable independently. This could make network management more adaptive, particularly as traffic patterns become less predictable and new AI-driven services create additional demand.

However, greater autonomy also increases the importance of safeguards. AI models must operate within defined performance and reliability boundaries, and operators need visibility into why a model has made a particular decision. Ericsson’s current AI in RAN development reflects this requirement by introducing augmented observability and more detailed information for monitoring AI performance and model behaviour.

The transition is therefore not simply about putting AI into existing network functions. AI RAN network optimization is becoming a broader operating model in which network data, AI inference and automated control are increasingly connected. The long-term objective is a RAN that can continuously adapt its resources to changing conditions while maintaining performance, reliability and energy efficiency.

AI RAN is Moving Toward Adaptive Network Control

The role of AI in radio access networks is moving beyond analytics and performance reporting toward continuous network control. As operators gain access to more real time network data, AI systems can increasingly identify changing conditions, predict their impact and support adjustments to network resources.

The next stage will depend on how reliably these systems can operate within strict latency, performance and energy constraints. AI models must respond quickly, remain stable under changing radio conditions and work alongside existing network-management systems without creating additional operational complexity.

The broader direction is toward networks that can continuously adjust capacity, radio parameters and energy use according to demand. AI RAN network optimization will therefore become less about isolated AI applications and more about creating an adaptive operating model in which prediction, decision making and network control are increasingly connected.

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U Mobile Partners with OpenAI to Expand AI in Telecom

API TIT — Thu, 13 Aug 2026 13:25:00 +0000

Malaysian telecommunications operator U Mobile has entered a strategic collaboration with OpenAI to bring AI in telecom to the forefront of its business. The agreement covers internal operations, customer-facing services, enterprise activities, network management and cybersecurity, marking a broad push by the operator to embed artificial intelligence across its organisation.

Under the collaboration, U Mobile will receive early access to OpenAI’s latest frontier models. Amazon Web Services will provide the cloud infrastructure and technical support needed to develop and scale applications powered by those models. The companies have not disclosed which OpenAI models will initially be deployed, nor have they shared a rollout timeline or investment figure. It remains unclear which applications will move from testing into production first.

Initial work is expected to cover internal process automation, software development, data analytics and data science. U Mobile also plans to assess AI in telecom network operations, cybersecurity, brand activities and digital content. The operator’s chief information officer, Neil Tomkinson, said the company believes AI is most powerful when it amplifies human potential. He added that U Mobile intends to use AI to support employees in areas including decision-making and day-to-day work.

OpenAI described the agreement as its first collaboration with a telecommunications provider in Malaysia. Fred Groen, OpenAI’s head of mid-market GTM for Asia Pacific, said the partners would look at how AI can support every part of U Mobile’s business, from helping staff work more effectively to enabling enterprises to innovate and strengthening cybersecurity.

U Mobile Expands AI Across Telecom Operations

The collaboration forms part of U Mobile’s broader strategy to operate as an AI-enabled telecommunications provider. The company is building a next-generation 5G network in Malaysia and intends to combine its 5G Advanced infrastructure with AI tools used across operational and commercial functions. U Mobile has not disclosed which specific network management functions it intends to automate or support using OpenAI models. The announcement also does not disclose performance targets, projected cost savings or deployment volumes.

AWS will support the development and scaling of AI applications under the collaboration. OpenAI models are also available through Amazon Bedrock, though the announcement does not state whether U Mobile deployments will use that service or disclose the technical architecture it plans to adopt.

OpenAI Joins U Mobile’s Enterprise Innovation Platform

OpenAI will become an anchor partner of U Mobile’s Enterprise Innovation Platform, joining existing technology partners including AWS, Huawei Malaysia, Palo Alto Networks and Qualcomm. The platform allows businesses, startups, developers and academic institutions to develop, test and validate AI applications. U Mobile introduced the Enterprise Innovation Platform in January 2026 and officially launched its physical EIP Hub on 6 August 2026.

Built around U Mobile’s 5G Advanced infrastructure, the Hub provides an environment where organisations can develop and demonstrate applications before wider deployment. Use cases demonstrated at the launch included AI-based visual monitoring, digital twins using 5G, IoT and AI for asset management, 5G-connected drones for inspection and surveillance, and robotics. The platform is open to enterprises, SMEs, startups, academia and public-sector organisations, including those that are not existing U Mobile customers. OpenAI’s frontier models will be available through the platform for exploration by these groups.

U Mobile and OpenAI will also explore future AI-based products, services and partnerships aimed at consumers and businesses, though no specific products, launch dates or commercial terms have been disclosed.

AI Applications Extend to Network Management and Cybersecurity

AI in telecom cybersecurity is another area covered by the agreement. The two companies plan to examine the use of AI for threat detection, incident response and cyber resilience. They will also look at measures for the secure deployment of AI within U Mobile and its enterprise ecosystem. OpenAI has been developing cybersecurity capabilities for tasks including vulnerability discovery, analysis, patch development, testing and remediation. The announcement does not state which OpenAI cybersecurity models or tools U Mobile intends to use, and no joint cybersecurity project involving U Mobile, OpenAI and Palo Alto Networks has been announced.

U Mobile chief executive officer Wong Heang Tuck and OpenAI’s Fred Groen marked the launch of the collaboration at an event attended by Malaysia’s communications minister Fahmi Fadzil. The adoption of AI in telecom continues to gain momentum across the Asia Pacific region, and this collaboration positions U Mobile among operators actively exploring how artificial intelligence can reshape network operations and enterprise services.

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Zayo and NVIDIA Partner to Drive AI Network Expansion Across North America

API TIT — Fri, 07 Aug 2026 13:34:53 +0000

Zayo Group has announced a collaboration with NVIDIA to significantly scale network infrastructure connecting artificial intelligence facilities across North America. The initiative centres on deploying new fibre capacity along critical corridors that link AI factories, data centres, and cloud campuses, representing a substantial push in AI network expansion designed to meet surging demand for high-bandwidth connectivity.

The collaboration was unveiled at NVIDIA’s AI Summit in San Jose, where Zayo confirmed it would leverage NVIDIA’s networking technologies alongside its own extensive fibre footprint to build out dedicated AI transport corridors. The effort responds to a rapidly growing need for network pathways capable of handling the enormous data flows generated by AI training and inference workloads.

Why New Fibre Corridors Are Essential for AI Infrastructure

The rise of large-scale AI factories has created unprecedented demand for network capacity between compute facilities. Traditional internet and enterprise traffic patterns are being reshaped by AI workloads, which require ultra-low-latency, high-throughput connections between clusters of graphics processing units spread across multiple sites. This AI network expansion addresses a bottleneck that threatens to constrain the buildout of AI infrastructure if connectivity does not keep pace with compute deployment.

Zayo operates one of the largest independently owned fibre networks in North America, spanning more than 141,000 route miles. The company said it is now directing significant capital toward expanding capacity on routes that serve the highest concentrations of AI data centres. These corridors connect key markets where hyperscale operators, cloud providers, and AI developers are clustering their facilities.

Dan Caruso, chief executive of Zayo, said the partnership with NVIDIA reflects the reality that networking is becoming as critical as compute in the AI supply chain. He noted that Zayo’s fibre assets position the company to deliver the physical infrastructure layer that AI factories depend upon, and that the collaboration will accelerate the pace at which new capacity reaches the market.

Technical Details of the Fibre Expansion

Under the initiative, Zayo plans to deploy additional dark fibre pairs and lit services across high-demand AI corridors. The company will integrate NVIDIA’s networking technology stack to optimise data transport between AI facilities. This includes support for back-end AI cluster networking, where massive volumes of data must move between GPUs during model training.

The AI network expansion also encompasses upgrades to existing fibre routes, increasing the density of available fibre strands on pathways that connect established data centre campuses. Zayo confirmed that the buildout would span multiple phases, with initial deployments targeting corridors in the western and central United States where AI facility construction is most concentrated.

Kevin Deierling, senior vice president at NVIDIA, said that scaling AI infrastructure requires a network fabric that extends beyond the walls of individual data centres. He described the collaboration with Zayo as an important step in ensuring that connectivity keeps pace with the rapid expansion of AI compute capacity across the continent.

The initiative also aligns with broader industry trends. Demand for fibre connectivity between data centres has accelerated sharply as enterprises and hyperscalers invest heavily in AI capabilities. Network operators across North America are racing to secure and light new fibre routes, making AI network expansion one of the most active segments of telecommunications infrastructure investment.

Next Steps in the Rollout

Zayo said the first phase of new fibre deployments under the collaboration is expected to commence in the second half of 2025, with additional corridors to follow through 2026. The company indicated it would announce specific route details and capacity milestones as construction progresses.

The partnership positions both companies to address what industry observers regard as a critical constraint on AI growth. As compute power continues to scale inside AI factories, the connecting network must evolve in parallel. This AI network expansion effort represents a direct response to that challenge, with Zayo committing substantial fibre resources and NVIDIA contributing its networking expertise to ensure the infrastructure is fit for purpose.

Further updates on corridor-level deployments and technical specifications are expected in the coming quarters as Zayo advances its buildout programme alongside NVIDIA.

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Ooredoo Commits $800 Million AI Infrastructure Investment to Power Southeast Asian Computing Platform

API TIT — Fri, 07 Aug 2026 12:29:42 +0000

Qatar-based international communications company Ooredoo has announced plans to invest approximately $800 million to acquire a 49% stake in Zankore, an artificial intelligence computing platform under development in Indonesia. The move represents a significant AI infrastructure investment by the telecom operator, extending its digital footprint into the rapidly growing Southeast Asian market.

The announcement, made on 6 August, positions Ooredoo alongside partners Indosat Ooredoo Hutchison, Nokia and NVIDIA in building out the AI computing platform. Zankore is designed to provide AI computing capacity for cloud and enterprise customers, with ambitions to develop up to 1 gigawatt of NVIDIA DSX AI Factory capacity over time.

Ooredoo said the initial AI infrastructure investment is expected to generate around $600 million in cumulative earnings before interest, tax, depreciation and amortisation over the first five years, based on current estimates.

Platform Capacity and Technology Deployment

The first phase of the Zankore platform is expected to deliver approximately 200MW of AI computing capacity during the first half of 2027. This initial rollout will utilise NVIDIA GB300 NVL72 systems, according to the company.

Zankore will also deploy NVIDIA’s DSX MaxLPS technology, which Ooredoo said is designed to boost AI computing capacity by optimising power use across graphics processing units. The deployment of these systems underlines the scale of the AI infrastructure investment being directed toward the platform.

Indonesia was selected as the location for the project due to its growing digital economy and rising demand for AI computing infrastructure. Ooredoo indicated the platform is expected to expand across Southeast Asia over time, broadening the geographic reach of this AI infrastructure investment.

Strategic Rationale and Digital Infrastructure Expansion

Group CEO Aziz Aluthman Fakhroo said the investment forms part of Ooredoo’s broader strategy to expand its digital infrastructure business and increase its exposure to AI-related assets.

“Over recent years, we have systematically built one of the MENA region’s leading digital infrastructure portfolios. With Zankore, we are taking the next logical step by adding AI compute to that portfolio, extending our approach into the high-growth Southeast Asian market – one of today’s most dynamic AI infrastructure opportunities,” he said.

“We believe AI will define the next decade of value creation in emerging markets. Our role is to invest alongside the right partners, in scalable platforms that create long-term value for our shareholders while supporting the digital ambitions of the markets we serve,” he added.

Ooredoo’s existing digital infrastructure portfolio spans connectivity, international fibre and subsea cable networks, and AI-ready data centres. The addition of Zankore introduces AI compute as a dedicated infrastructure layer, creating what the company described as an integrated digital infrastructure platform covering networks, data centres and AI computing capacity.

The AI infrastructure investment effectively extends Ooredoo’s portfolio beyond its traditional connectivity and data centre operations into the high-demand AI computing segment, a market that continues to attract substantial capital across emerging economies.

FTI Capital Advisors served as financial adviser to Ooredoo on the transaction.

Looking ahead, the first major milestone will be the delivery of the initial 200MW phase of AI computing capacity in the first half of 2027, with further expansion across the Southeast Asia AI market anticipated in subsequent phases.

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SK Telecom A.X K2 Strengthens South Korea’s Sovereign AI Strategy

API TIT — Fri, 31 Jul 2026 10:49:38 +0000

SK Telecom has introduced SK Telecom A.X K2, a 688-billion-parameter large language model designed to support South Korea’s sovereign AI ambitions and reduce reliance on foreign artificial intelligence platforms. Released with open weights and configuration files on Hugging Face, the model forms the centrepiece of the country’s government-backed independent AI foundation model programme.

The launch also supports South Korea’s broader AI for All initiative, which aims to expand access to AI services for citizens while strengthening the country’s domestic AI ecosystem.

SK Telecom A.X K2 targets enterprise AI with efficient model architecture

SK Telecom A.X K2 ranks among the largest publicly released mixture-of-experts (MoE) models, although the company’s emphasis is on efficiency rather than scale alone. The architecture activates only a subset of model parameters during inference, helping reduce computing requirements for large-scale deployments.

To improve long-context performance, SK Telecom introduced three proprietary architectural enhancements, including Sparse Gated Attention, which identifies and filters less relevant tokens to improve processing efficiency. The company also implemented Gated Norm and an attention output gate to enhance training stability and optimise inference performance.

According to SK Telecom, the model delivers an average 32.2-percentage-point improvement across 14 benchmark tests compared with its predecessor, A.X K1. The company also reported an 83.9-percentage-point improvement in long-context and agent-based evaluations, alongside stronger mathematical reasoning and scientific capabilities.

SK Telecom said SK Telecom A.X K2 performs at a level comparable with leading open-weight models such as Qwen3.5 and DeepSeek-V4 for reasoning and agent-oriented workloads.

Enterprise deployments span manufacturing, healthcare and defence

SK Telecom plans to deploy SK Telecom A.X K2 across multiple industries, including manufacturing, healthcare and defence, where organisations increasingly require secure AI platforms capable of processing sensitive data.

Manufacturing applications include production optimisation, predictive maintenance and quality inspection, while healthcare use cases cover literature analysis, drug discovery support and clinical documentation summarisation.

In the defence sector, the model is intended to support intelligence analysis, simulation workloads and multilingual translation within secure computing environments using domain-specific fine-tuning.

Beyond industry-specific deployments, SK Telecom also expects the model to enhance enterprise productivity through document drafting, meeting summarisation and internal knowledge search.

Open-weight release supports South Korea’s AI ecosystem

The Hugging Face release includes model weights and configuration files compatible with widely used inference frameworks, enabling developers and enterprises to build applications using the model.

Alongside the flagship model, SK Telecom announced three lightweight multimodal derivatives built on A.X K2 Light to process combinations of text, speech and images. Among them is A.X K2 Raon-Speech, a 21-billion-parameter speech foundation model developed with KRAFTON AI that combines speech recognition and speech generation capabilities.

The company said the open-weight release supports the Ministry of Science and ICT’s efforts to establish domestic foundation models as strategic national infrastructure while encouraging broader participation from Korea’s software ecosystem.

Although the launch marks a significant milestone for South Korea’s sovereign AI strategy, large-scale deployment of a 688-billion-parameter model will require substantial computing resources. The company has not disclosed details on the computing infrastructure, energy consumption or operational costs associated with running the model, factors that could influence adoption among smaller organisations.

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Telecom IoT Strategy Drives 5G, AI, Satellite and Smart Connectivity

API TIT — Fri, 31 Jul 2026 10:18:31 +0000

The global telecom IoT strategy 2026 landscape is shifting decisively beyond basic cellular connectivity. Seven of the world’s largest mobile operators like China Mobile, China Telecom, Vodafone, AT&T, Bharti Airtel, Verizon and Deutsche Telekom, are each pursuing distinct but converging IoT strategies built around 5G, artificial intelligence, satellite IoT, eSIM, edge computing and smart connectivity for utilities and enterprises.

The moves come as global cellular IoT connections reached 4.2 billion at the end of 2025, an 11 percent increase year-on-year according to Berg Insights. That figure accounted for roughly 32 percent of all mobile subscriptions worldwide, and Berg Insights forecasts cellular IoT connections will surpass 5 billion by 2027 and reach 6.5 billion by 2030, reflecting a 9.3 percent compound annual growth rate.

China Mobile and China Telecom Lead on Scale

China Mobile remains the world’s largest cellular IoT connectivity provider, closing 2025 with 1.48 billion connections after 5 percent annual growth. The operator is aligning its telecom IoT strategy 2026 with China’s national IoT development programme, which targets more than RMB 3.5 trillion (approximately US$505.8 billion) in core IoT industry value and 10 billion IoT terminal connections by 2028. The programme also calls for the formulation or revision of more than 50 advanced IoT standards within the same timeframe. China Mobile’s expansion opportunity spans industrial IoT, connected vehicles, smart cities, utilities and satellite-enabled IoT, with low-Earth-orbit satellite communications and direct satellite connectivity positioned to extend coverage beyond terrestrial cellular networks.

China Telecom ranked second globally with 746 million IoT connections, followed closely by China Unicom at 723 million. China Telecom is expanding IoT through its cloud-network integration, industrial digitalisation and 5G IoT capabilities. The operator’s established satellite communications infrastructure gives it a strong position in integrated satellite-terrestrial IoT applications, extending its reach into smart cities, connected transportation and industrial use cases.

China accounted for approximately 2.9 billion cellular IoT connections at the end of 2025, equivalent to around 70 percent of the global installed base.

Western Operators Pursue AI, Satellite IoT and Smart Connectivity

Vodafone retained its position as the largest Western IoT connectivity provider with 234 million connections. The operator’s smart connectivity approach now encompasses 5G Standalone, network slicing, AI and satellite coverage. Vodafone and Topcon Positioning Group are developing a mass-market positioning service capable of centimetre-level accuracy for connected vehicles, machinery and IoT devices, a significant improvement over the few-metre accuracy available from standalone satellite navigation.

AT&T is making AI-driven IoT network intelligence and edge computing central to its enterprise IoT push, with 160 million IoT connections. The operator’s IoT Network Intelligence platform, announced at CES 2026, provides businesses with greater visibility into connected-device performance. AT&T followed that by listing Connected Spaces as its first end-to-end IoT solution on AWS Marketplace, offering plug-and-play sensors and near-real-time monitoring of temperature, humidity, motion and energy consumption across sectors including retail, healthcare and property management. In March 2026, AT&T announced a collaboration with Cisco and NVIDIA on network-driven edge AI for enterprises, combining AT&T’s dedicated IoT core, Cisco Mobility Services Platform and NVIDIA AI infrastructure to bring AI inference closer to where IoT data is generated.

Verizon is building its telecom IoT strategy 2026 around ThingSpace, eSIM, satellite IoT, 5G RedCap, AI analytics and global IoT orchestration. Through partnerships with Singtel, Bell Canada and Telenor IoT, Verizon customers can manage international device connectivity through ThingSpace across up to 200 territories. Verizon reported 240 percent growth in the monthly average for eSIM connectivity on ThingSpace and holds 62 million IoT connections. Skylo provides satellite IoT capabilities where conventional cellular coverage is unavailable.

Bharti Airtel recorded the strongest annual growth among leading operators, reaching 69 million IoT connections. Its focus on smart utilities, smart metering and multi-network IoT connectivity in India includes dual-profile eSIM capabilities and smart meter systems that Airtel says can support millions of customers while delivering a 50 percent reduction in deployment time.

Deutsche Telekom Launches Multi-Orbit IoT Roaming

Deutsche Telekom has announced what it describes as the world’s first multi-orbit IoT roaming service, enabling IoT devices to switch between terrestrial mobile networks and satellite connectivity. The service combines NB-IoT and LTE-M with satellite providers including Skylo for GEO satellite coverage and Sateliot and OQ Technology for LEO satellite connectivity. Iridium NTN Direct is scheduled to become available to Deutsche Telekom business customers during the second half of 2026. Deutsche Telekom holds 69 million IoT connections.

Revenue Growth and Pricing Pressure

Global cellular IoT connectivity revenue grew 5 percent in 2025 to €14.5 billion and is forecast to reach approximately €21.5 billion by 2030 at an 8.1 percent CAGR. However, monthly IoT ARPU fell 7 percent during 2025 to €0.31 and is projected to decline further to €0.28 by 2030, reflecting persistent pricing pressure from competition and rapid growth in low-bandwidth 5G IoT and smart connectivity applications.

North America recorded 321 million cellular IoT connections at the end of 2025, while Western Europe had 313 million. South Asia, Latin America and Central & Eastern Europe each ranged between 93 million and 142 million connections.

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Robot Data Factory Proposal Advances ITU-T Standardization

API TIT — Fri, 24 Jul 2026 07:07:30 +0000

SK Telecom has taken a significant step toward advancing global standards for physical AI after its Robot Data Factory proposal was adopted as a new standardization work item by the International Telecommunication Union Telecommunication Standardization Sector (ITU-T). The initiative aims to establish standardized methods for producing, exchanging, and utilizing AI training data for intelligent robots, improving interoperability across robotics platforms.

Robot Data Factory Proposal Adopted by ITU-T

The Robot Data Factory proposal was accepted during the ITU-T international standardization meeting held in Geneva, Switzerland, from July 14 to 22. The newly adopted work item focuses on creating standards for managing AI training data used by advanced robotic systems, including humanoid robots.

The proposal seeks to define standardized frameworks for generating and utilizing robot learning data while addressing compatibility challenges that arise from varying data creation and exchange methods across different robot models and platforms.

Standardization Aims to Improve Robot Interoperability

High-quality motion data is essential for training robots to perform complex tasks such as object manipulation, movement, and tool operation. However, the lack of common standards has limited interoperability between robotics platforms and AI training environments.

Through the Robot Data Factory initiative, SK Telecom plans to standardize the roles of core platform functions, establish common data exchange methods, and define interoperability structures. These measures are intended to enable robotics systems from different manufacturers to connect, integrate, and expand more efficiently.

Digital Twin Expertise Supports Physical AI Development

The company is building on its experience in digital twin technology developed for manufacturing AI applications. Working with SK Hynix, SK Telecom is developing a digital twin platform that supports manufacturing processes while extending the technology into robot learning platforms capable of generating AI training data.

According to the company, this approach combines digital twin capabilities with AI to improve the development of intelligent robotics systems and support broader physical AI applications.

SK Telecom Targets Leadership in Global Robotics Standards

SK Telecom said the adoption of the Robot Data Factory proposal establishes a foundation for the company to participate more actively in international standardization activities for physical AI and robotics.

The company plans to continue strengthening its technological capabilities across robot learning platforms and AI models while contributing to the development of international standards that support interoperability within the global robotics ecosystem.

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