Samsung Brings AI Across Every Layer of the Network to Power Next Generation Telecom Infrastructure

Artificial intelligence is rapidly becoming a defining capability in modern telecom networks. As operators continue to expand 5G and prepare for the transition to 6G, the scale and complexity of networks are increasing significantly. In this environment, automation, efficiency and adaptability are becoming essential. Samsung is positioning artificial intelligence as a core technology that can operate across every layer of the network to help operators manage this complexity while unlocking new capabilities.

Much of the industry conversation around AI integration in telecom networks has recently focused on the concept of AI-RAN. Within the AI-RAN Alliance, this is commonly described through three dimensions: AI for RAN, AI on RAN and AI with RAN. These categories describe how artificial intelligence can enhance radio network performance, support new edge-based services and enable the coexistence of AI workloads and network functions on shared infrastructure.

Samsung is actively involved in this industry effort, but its strategy extends beyond the radio layer alone. The company is promoting a broader approach to AI-powered networks that combines end-to-end software-based architecture with distributed computing capabilities. In this model, artificial intelligence is not limited to a specific part of the network. Instead, it is embedded across the entire infrastructure, from the radio access network to the core network and operational management systems.

A key element of this approach is Samsung’s focus on software-based and virtualised network architectures. Virtualised RAN deployments running on commercial off-the-shelf servers provide a flexible platform where both network workloads and AI functions can operate together. This allows operators to introduce AI capabilities without needing to completely redesign their infrastructure.

Through its network automation platform, Samsung is applying AI to a wide range of operational tasks. These include predicting traffic patterns, identifying anomalies in network performance, optimising radio parameters and balancing loads across spectrum bands. By analysing large volumes of operational data, AI systems can automatically adjust network behaviour to maintain performance and improve efficiency.

Energy optimisation is another area where AI-driven techniques are being applied. As mobile networks expand and traffic patterns fluctuate throughout the day, intelligent algorithms can determine when certain network features can be adjusted or scaled down to reduce power consumption without affecting user experience. These types of capabilities are becoming increasingly important as operators focus on both operational efficiency and sustainability.

Samsung is also exploring how artificial intelligence can improve radio performance directly within the protocol stack. Machine learning techniques can enhance channel estimation at the physical layer, allowing the network to reconstruct radio signals more accurately even in challenging environments. At higher layers, AI can support link adaptation by identifying optimal modulation and coding schemes for each user based on real time radio conditions. Even connection management processes can benefit from AI driven optimisation, improving both device battery efficiency and network resource utilisation.

Beyond improving the network itself, Samsung is also examining how telecom infrastructure can support AI workloads. Modern base stations and edge compute platforms contain significant computing resources. When network traffic demand is low, some of this capacity can remain unused. By running AI inference tasks on the same infrastructure, operators can make better use of these resources while supporting new services.

Edge based AI applications are particularly relevant in industrial environments. Real time video analytics, safety monitoring and automated quality inspection are examples of workloads that benefit from processing close to the data source. Running these applications on infrastructure that already supports radio functions reduces latency and avoids sending large volumes of data to central cloud platforms.

Samsung describes this convergence between communications infrastructure and computing capabilities as a shift towards networks functioning as distributed data centres. In this model, the network becomes both a connectivity platform and a processing environment capable of supporting AI driven applications. The concept combines two complementary perspectives: building networks that support AI workloads and using AI to improve how networks operate.

This architectural shift also has implications for the hardware layer of telecom infrastructure. Traditional mobile network equipment has relied heavily on specialised system-on-chip designs. However, the rapid development cycle of general purpose processors and accelerators is encouraging a more flexible approach. Samsung’s virtualised infrastructure strategy allows operators to deploy workloads on a mix of CPUs and GPUs, drawing on technologies from companies such as Intel, NVIDIA and Arm Ltd.. This enables operators to scale AI capabilities across different parts of the network depending on where computing power is needed.

As telecom networks evolve towards cloud native and software driven architectures, the role of artificial intelligence will continue to expand. By embedding AI across radio, core and operational layers, Samsung is highlighting how networks can move beyond traditional connectivity and become intelligent platforms capable of continuous optimisation.

With 5G Advanced deployments underway and early discussions around 6G gathering momentum, the integration of AI into telecom infrastructure is likely to accelerate. Samsung’s strategy suggests that the future network will not simply transport data, but will increasingly analyse, optimise and process it within the network itself, transforming the way operators design and operate their infrastructure.

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O-Cloud Driving the Future of Open RAN

For mobile operators, O-Cloud provides the cloud-native platform needed to automate RAN functions, integrate multi-vendor solutions, and optimise network performance at scale.

The Open Cloud, or O-Cloud, is the physical and software foundation that lets operators deploy open RAN in a cloud-native way. At its core an O-Cloud node bundles CPUs, memory, storage, network interfaces, board management controllers and optionally accelerators such as FPGAs to offload computationally heavy tasks. An O-Cloud is not a single machine but a set of resource pools that may be distributed across a centralised site and multiple edge locations. Together those pools provide the compute, storage and timing environments required to host O-RAN central units and distributed units, the management and orchestration layers, and observability and lifecycle services that keep everything running reliably.

Technically oriented operators will want to think of O-Cloud as an integrated stack of requirements rather than a single product. It must support bare metal deployment and tight integration with container orchestration for cloud-native network functions. It needs multi-cluster lifecycle management with automated scaling, network isolation primitives and deterministic CPU and memory control for real-time workloads. Features such as SR-IOV, PCI pass-through, DPDK support, huge pages and device plugins for FPGA or GPU enable low-latency packet I/O and hardware acceleration where needed. Time synchronisation is another non-negotiable; GNSS, PTP and SyncE support are all necessary for distributed scenarios that demand precise phase and timing alignment. On the management side the O-Cloud must provide board and host management, configuration and fault management, secure software update mechanisms and an infrastructure inventory that reports capabilities and resource availability to the orchestrator.

Why O-Cloud matters is best understood by mapping those technical building blocks to operational problems that operators face today. Mobile networks are now a mesh of legacy architectures, virtualised elements and new cloud-native components. Manual processes and basic automation are no longer sufficient to optimise service quality, scale to demand, or keep costs under control. The O-RAN Service Management and Orchestration framework, coupled with the O-Cloud, enables standards-based infrastructure management and cross-organisational data collection. That combination is the prerequisite for sophisticated automation and the application of AI techniques across the RAN.

A concrete operational capability driven by O-Cloud is application homing. When the orchestrator has an accurate inventory of O-Cloud resources and real-time telemetry, it can place workloads on the cloud instance that best meets the latency, throughput and cost profile required by an application. In practice this means low-latency services can be hosted on edge clusters with real-time OS and hardware acceleration while less-sensitive workloads run in centralised clusters that maximise cost efficiency. The O2 interface between the SMO and O-Cloud is what makes this possible by allowing registration and continuous inventory updates so the orchestrator always has an up-to-date picture of available resources.

Multi-cloud orchestration is another operational lever. In a disaggregated ecosystem operators can choose cloud providers or on-prem platforms based on geography, performance, pricing or regulatory requirements. Effective orchestration distributes workloads across these environments to optimise resource usage and resilience. For example, automatic lifecycle management and rolling updates let operators patch and upgrade clusters with minimal service disruption, while integrated observability ensures fast detection and remediation of performance degradations. These capabilities not only improve quality of experience for subscribers but also reduce operational effort and human error.

Security, isolation and regulatory compliance are built into the O-Cloud requirements. A Zero Trust approach that hardens host operating systems, enforces strict service-to-service authentication and integrates automated security testing into CI/CD pipelines is essential. Multi-tenancy and isolation at compute, network and storage layers are critical where regulators demand separation for emergency services, hospitals or public safety. Managing hardware accelerators and firmware securely is also part of the platform responsibilities.

Deutsche Telekom’s independent O-Cloud proof of concept demonstrates how these technical concepts translate into reality. Working with Red Hat and IBM, Deutsche Telekom used Red Hat OpenShift as the CaaS (container-as-a-service) layer to provide the container runtime, cluster lifecycle tools and GitOps-based operations. Red Hat’s automation tooling was used to implement zero-touch provisioning so hardware could be rapidly onboarded and configured. IBM Consulting supported the integration, system design and lifecycle automation that enabled rapid deployment, monitoring and observability across the lab environment. The trial, completed within six months, validated automated lifecycle management, observability and the integration of hardware and software with open interfaces.

The trial’s outcomes reinforce the argument that a properly engineered O-Cloud enables multi-vendor interoperability and operational freedom. Automation and monitoring requirements were met with minimal human intervention. Flexible infrastructure and vendor independence give operators choice and the ability to continuously select best-in-class components. Crucially, the PoC showed that inventory and capability reporting combined with orchestration can enable application homing and cross-cloud resource optimisation in practice, not just in theory.

Looking ahead, real-world O-Cloud deployments will need to balance competing priorities. Operators must plan migration paths that preserve legacy services while incrementally introducing cloud-native elements. They must invest in platform automation, observability and security, and build operational processes that exploit rather than fight cloud-native lifecycle paradigms. Energy and cost management must be considered at the infrastructure layer so that scaling decisions also reflect carbon and capex/opex objectives. Finally, industry collaboration and lab-to-field testing will remain important to refine standards, validate interoperability and mature the tooling around SMO, the O2 interface and multi-cluster management.

O-Cloud is therefore both a technical specification set and an operational philosophy. It ties precise platform requirements to the automation, orchestration and vendor independence that open RAN promises. Deutsche Telekom’s PoC shows that the blueprint works in practice and that the path to cloudified, multi-vendor RANs is feasible when infrastructure, orchestration and automation are designed together. For operators seeking to deliver programmable, resilient and cost-effective 5G and beyond, investing in O-Cloud capabilities will be a defining step on the road to modern RAN operations.

Related Posts: 

• Operator Watch Blog: Deutsche Telekom’s Open RAN (ORAN) Plans
• Free 6G Training: 3GPP and O-RAN ALLIANCE Discuss 6G Coordination
• The 3G4G Blog: O-RAN Introduction for Beginners
• The 3G4G Blog: Accelerating O-RAN Adoption Through Open Source
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How Samsung is Leveraging vRAN to Match Traditional RAN (T-RAN) Performance

As mobile networks evolve, virtualized RAN (vRAN) and Open RAN architectures are gaining traction. Even operators who were initially sceptical are increasingly exploring and deploying these innovative solutions to meet the growing demands for flexibility, efficiency, and sustainability. Samsung, among other key players, has been at the forefront of large-scale vRAN and Open RAN rollouts across North America, Europe, and Asia.

The adoption of O-RAN-compliant, Open vRAN architectures has demonstrated the potential to deliver performance on par with—or even superior to—traditional RAN systems. While trials and commercial deployments have validated their capabilities, scaling these solutions introduces challenges, such as integration complexities, security concerns, and organizational disruptions. To address these hurdles, operators and vendors alike are focusing on building robust ecosystems, fostering collaboration, and driving continuous innovation.

As adoption expands, operators are reaping an array of benefits from vRAN and Open RAN architectures:

• Faster site activations: Accelerated deployment timelines facilitate quicker service rollouts.
• Enhanced resource utilization: Flexible resource sharing improves overall network efficiency.
• Energy savings: AI-driven solutions enable dynamic power management, reducing energy consumption.
• Operational agility: Advanced monitoring and adaptive systems boost performance and responsiveness.

Last week, @Vodafone and Samsung successfully demonstrated their first 5G Open RAN call in Romania at Vodafone's Innovation Hub at @upb1818 #etti. The companies achieved excellent performance with 5G #OpenRAN powered by Samsung's #ORAN-compliant #vRAN 3.0 solution. #togetherwecan pic.twitter.com/15moYfj0gP

— Samsung Networks (@SamsungNetworks) June 4, 2024

Vendors and partners are tackling the complexities of scaling vRAN and Open RAN through collaborative efforts, with Samsung introducing several solutions to improve performance and address integration challenges:

• Containerized Virtual Cell Site Router (vCSR): The integration of vCSR within the virtual Distributed Unit (vDU) minimizes hardware requirements by utilizing server processing power more efficiently.
• Energy-saving features: AI-powered tools like Samsung’s Energy Saving Manager (ESM) enable traffic-aware adjustments, such as dynamic power amplifier (DPA) levels, sleep modes for radio units, and CPU power optimization, demonstrating significant energy reductions in large-scale deployments.
• AI/ML-powered automation: Comprehensive platforms, such as Samsung’s CognitiV Network Operations Suite (NOS), incorporate advanced analytics and automation, enhancing network optimization, troubleshooting, and reducing total cost of ownership (TCO).

The transition to Open vRAN is not just a technological evolution but a paradigm shift in network architecture. These systems prioritize flexibility and programmability, empowering operators to achieve business objectives that extend beyond cost savings, including faster service rollouts, better customer experiences, and improved energy efficiency.

While Samsung’s contributions in this domain are notable, the larger industry trend toward open and virtualized networks reflects a collective push to shape the future of mobile connectivity. Collaboration across the ecosystem is essential to address challenges and unlock the full potential of these transformative technologies.

Embedded below are some nice explainers and presentations on Open vRAN from Samsung:

As the industry continues to evolve, vRAN and Open RAN are set to play a pivotal role in driving the next wave of 5G innovation and growth.

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NTT Docomo Expands Open vRAN Options in its 5G OREC

We have done quite a few blog posts on NTT Docomo on our blogs (see related posts at the bottom) as they always share a lot of latest useful and relevant information. In a blog post last year, we talked about their 5G Open RAN Ecosystem (OREC). 

Back in September, NTT Docomo announced that it will be adding a fourth type of flexible and highly scalable virtualized base station (vRAN) to its verification environment for Open RAN that will allow equipment and components of various vendors to integrate via standardized specifications. The press release said:

The new virtualized base station will combine NEC Corporation's open virtualized RAN software, Red Hat Openshift, Qualcomm Technologies, Inc.'s inline accelerator cards and Hewlett Packard Enterprise's servers. This will be DOCOMO's first time to use the Qualcomm® X100 5G RAN Accelerator Card and the HPE ProLiant DL110 Telco server, the latter optimized specifically for Open RAN workloads to improve system performance and power efficiency.

Since February, DOCOMO has been providing global carriers with access to its Shared Open Lab environment to allow them to mix and match the equipment and components of various vendors in order to verify performance with virtualized base stations. In addition to three types of virtualized base station equipment/system configurations that carriers have been accessing so far, the new configuration will become available for verification in the Shared Open Lab starting in 2023.

As the increasing global availability of 5G continues to raise interest in Open RAN, DOCOMO has been a leader in helping telecoms worldwide to prepare to introduce this highly versatile new technology. In February 2021, DOCOMO was joined by 12 global equipment vendors in establishing the 5G Open RAN Ecosystem (OREC) to promote Open RAN, which supported the development of the virtualized base station being announced today.

DOCOMO and its OREC partners plan to continue adding new equipment/system combinations before and after commercializing virtualized base stations, which is expected within the current fiscal year ending in March 2023.

Through the promotion of Open RAN, DOCOMO looks forward to continuing to enhance the efficiency and flexibility mobile communication networks around the world.

In fact their OREC page has this and a lot more details here.

At Telecom Infra Project's Fyuz 22 conference, NTT Docomo and other operators shared their vision, progress and approach to Open RAN. You can watch the panel discussion here.

Related Posts:

• Telecoms Infrastructure Blog: NTT Docomo's 5G Network is based on 'Open RAN' Principles
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