Written & Interviewed by : LITEON Editorial Team (Corporate Brand Value Development Center)
Technical review :LITEON Center of Core Competence
Traditional RAN systems were primarily built to provide reliable mobile connectivity, but many deployments relied on tightly coupled hardware, software, and network functions in a single-vendor environment. This model gave operators the ability to control performance at scale, but it also decreased the flexibility of the radio layer as new AI engines, edge applications, or third party control functions required direct access to network data. AI-RAN architecture converts this closed design to an open, programmable, and software-defined infrastructure.
Open RAN does not suddenly make all deployments cheaper or more efficient. The value depends on the quality of integration, the maturity of the interface, the validation of the system, and the capability to operate it. By opening selected interfaces in radio, distributed, centralized, and management functions, Open RAN provides the technical conditions for AI-RAN edge computing and AI-Native control.
ATIS, ETSI, and TTA are also gradually adopting O-RAN standards, showing that Open RAN is moving toward broader regional standardization. Looking ahead, the base station will no longer be just a connectivity endpoint. AI-RAN, built on the foundation of Open RAN, will turn the radio network into an intelligent infrastructure layer where data, computing, and policy-driven optimization can operate closer to users, devices, and enterprise applications.
The AI-RAN Alliance describes AI-RAN development through three connected directions.
These three directions are not to be seen as separate products or as independent deployment options. These are layers of the same AI-RAN architecture and they may coexist as networks become more open, intelligent, and service aware. For telecom operators, private-network owners, and system integrators, this evolution is a move from connectivity-first infrastructure to AI-Native platforms. AI-RAN edge computing can make radio nodes do more than transmit signals, but also make faster local decisions, provide more dynamic resource allocation, and offer new enterprise services that depend on low-latency access to network intelligence.
In the context of a cloud-centric network model, data often travels from an endpoint to the radio network and then back over transport links to a distant cloud environment before a decision returns. This delay is acceptable for normal data services. However, in the case of AGV coordination, industrial control, autonomous mobility, intelligent transport, positioning, and real-time security, each additional hop in the processing chain can lead to response time, consume uplink capacity, and reduce system predictability. Here, AI-RAN architecture changes the role of the radio network from a forwarding layer to a local decision support layer.
The ITU IMT-2020 requirements set minimum user-plane latency targets of 4 ms for eMBB and 1 ms for URLLC, which highlights the value of computing placement for time-sensitive services.
For appropriate deployments, placing selected computing resources closer to the radio edge can lower latency than cloud-only processing. Internal discussions have identified case studies where response time can be reduced from 15 ms to 3 ms depending on the workload type, network design, computing placement, and deployment conditions. The point is not that every AI-RAN site will hit the same number, but that AI-RAN edge computing gives operators and enterprises a feasible way to deal with time-sensitive tasks closer to where radio data is generated.
This hybrid model is central to an AI-Native telecom strategy, as it aligns the workload to the most efficient point of execution. The decision should be based on three practical issues.
Along these lines, LITEON’s role is to enable this flexible infrastructure layer so that AI workloads can be distributed across devices, edge radio nodes, and cloud environments, instead of having to force every use case to fit one fixed computing model.
LITEON’s involvement in the AI-RAN ecosystem begins at the Radio Unit layer. Here, radio-frequency behavior, synchronization, fronthaul stability, and software control influence network performance. LITEON concentrates on RU-level engineering and the tight coordination of radio hardware and software behavior. That is important since an effective AI-RAN architecture requires the radio layer to deliver usable data, respond predictably to control logic, and maintain stability across Open RAN environments.
Building on its deep expertise in front-end radio transmission and hardware-software optimization, LITEON completed AI-RAN certification in approximately three months and became the first certified partner in this category. This achievement reflects LITEON’s ability to combine RU engineering depth with efficient validation, energy-aware design, and cross-platform compatibility for AI-RAN deployment.
LITEON further bolsters this position by optimizing and opening software APIs to allow AI engines to access relevant radio-network information and make more precise adjustments. Thanks to better access to radio data and control parameters, AI applications are available for performance tuning, service-quality management, and energy-aware operation. LITEON’s RU design is in line with the AI-RAN ecosystem and enhances the role of the radio unit as a validated AI-ready infrastructure layer that can operate across multiple DU platforms and support more flexible multi-vendor Open RAN deployment.
As 5G networks are deployed in more dense private, industrial, and urban environments, the challenge is no longer to deploy isolated base stations, but to orchestrate many sites as a single operational system. LITEON’s SMO (5G O-RAN Service Management and Orchestration , SMO) provides the management layer for remote configuration, monitoring, deployment support, troubleshooting, and large scale operation. At the same time, the RIC (RAN Intelligent Controller, RIC) enables smarter control through standard interfaces that surface selected base-station information for analytics, third-party applications, and policy-driven network behavior.
Coordination is critical for AI-RAN edge computing, which requires managing local intelligence without fractured, site-by-site operations. LITEON’s approach is not just to optimize a base station in isolation, but can enable network-level energy solutions, such as powering down some sites in off-peak times and having nearby sites fill in for the service coverage. That is how SMO and RIC help operators move towards an AI-Native operating model, where radio performance, application needs, and energy policies can be managed across a larger network environment.
AI-RAN is progressing beyond lab validation, but commercial adoption will still depend on disciplined cost control and phased deployment planning. High end GPU infrastructure can raise the entry price, particularly where operators need to balance new computing capacity against site power, space, cooling, and application maturity. So, it is likely that wider adoption will follow the validation, controlled field deployment, and scale-out phases. With smaller and more diverse AI compute platforms entering the market, customers can start with specific use cases and then extend the same AI-RAN architecture to larger network environments. According to GSMA, mobile operators will invest $1.2 trillion in capex during 2025–2030. Thus, AI-RAN deployment must be planned around phased returns and use cases.
LITEON’s roadmap is important as intelligent infrastructure does not end at the base station.
This product logic enhances LITEON’s position in AI-RAN edge computing, thanks to connecting network-side intelligence and endpoint-side access.
The path to 6G is as an evolution, not a replacement for 5G. Work on IMT-2030 standards is ongoing and 5G will be the main commercial foundation as operators enhance openness, virtualization, edge intelligence, and network optimization. Future networks may need to support more extreme scenarios, e.g., high-mobility connectivity, dense sensing, and application-aware resource control. Here, AI-RAN provides a bridge for operators to AI-Native networks that can adapt radio behavior, computing placement, and service quality around real application needs.
The ITU’s IMT-2030 framework outlines research targets of 50-200 Gbps peak data rates, 0.1-1 ms radio-network latency, and 500-1,000 km/h mobility. When considering this fact, it is apparent that future networks will need infrastructure that is more adaptable.
AI-RAN is a move from radio networks built for connectivity to smart infrastructure that can support AI processing, edge services, and coordinated network control. Open RAN provides the interface openness for this transition. Low-latency computing, adaptive workload placement, and cross-site coordination determine the real-world value for operators and enterprises.
Meanwhile, LITEON supports this breakthrough with its RU and RF expertise, integrated hardware-software design, open APIs, multi-vendor compatibility, SMO, RIC, and energy-aware network management, and is positioned as a practical infrastructure partner for scalable AI-enabled deployment. As telecom infrastructure moves toward openness, software definition, and AI enablement, LITEON empowers customers to build the radio and edge foundation for responsive services, efficient operations, and future-ready network expansion.
Learn more about LITEON’s 5G AI-RAN solutions.