Samsung explains how operators can build AI-ready networks on existing software infrastructure to minimise hardware costs.
Operators that pursued virtualisation early are saving on operating costs while sitting on infrastructure capable of running AI workloads today without a complete redesign. Automation integrates into this system, using observability to feed intelligence back into the network and turn these assets into proactive and self-optimising systems.
Ji-Yun Seol, Executive VP and Head of Product Strategy, Networks Business at Samsung, explains: “The evolution from 5G to 5G-Advanced and 6G hinges on three interconnected pillars: virtualisation for flexible networks, AI integration across all network layers, and automation towards autonomous networks.”
Decoupling functions from hardware
Traditional telecoms infrastructure relied on network functions tightly coupled to dedicated hardware. A router was a physical box, baseband processing occurred on purpose-built equipment, and security appliances were separate devices. While this model provided reliable performance, new capabilities required additional hardware, space, and power.
Virtualisation changes this dynamic by decoupling network functions from purpose-built hardware and running them as software on commercial off-the-shelf (COTS) servers. This infrastructure switch to general-purpose compute platforms delivers cost savings, flexibility, and AI readiness.
The industry adopted this architecture in stages. Core network virtualisation came first, allowing operators to run packet gateways and subscriber databases on standard servers. Virtualised RAN (vRAN) followed, separating baseband processing from radio hardware to run as cloud-native software.
How Samsung is consolidating infrastructure for AI capabilities
Samsung has utilised vRAN software and modern processors to advance this architecture. Cloud-native design allows operators to run multiple elements on a single server: core functions, RAN processing, virtualised switch and cell site routers (vCSR), and virtualised security.
Running these functions together reduces the physical server footprint. It cuts capital expenditure and operational complexity while lowering energy consumption.
Previously, transport functions at cell sites and central data centres relied on dedicated equipment, which limited options and made changes difficult. Disaggregating hardware and software allows operators to move these functions onto whitebox switches and routers of their choice.
The virtualised cell site router offers a specific example of these benefits. It delivers total cost of ownership savings through a flexible and containerised architecture. Routing software can be co-located with the virtualised Distributed Unit on the same server, converting the cell site from a collection of fixed hardware into a dynamic and software-defined node.
Unified data for AI-ready networks
An end-to-end software-driven network provides a foundation for AI due to its inherent data visibility and dynamic control.
Traditional networks trap data in hardware-based silos. In contrast, fully virtualised networks act as a unified data source for AI, enabling orchestration that enhances performance and optimises infrastructure. This infrastructure provides the agility to adopt AI-driven services and create new monetisation opportunities.
Fast-paced innovation in processors, including CPUs and GPUs, frees up core resources to run AI applications directly on existing telco infrastructure. Samsung suggests that operators can integrate these processors into software-driven networks to expand AI capabilities flexibly. This allows the network to evolve at the speed of innovation rather than the timelines of hardware procurement.
The future evolution from 5G to 6G will depend on the connection between virtualisation, AI integration, and automation.
See also: Ericsson, FET, and OPPO validate performance-based 5G slicing
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