The role of artificial intelligence (AI) in boosting performance and energy efficiency in cellular network operations is rapidly becoming clear. This is…
The role of artificial intelligence (AI) in boosting performance and energy efficiency in cellular network operations is rapidly becoming clear. This is especially the case for radio access networks (RANs), which account for over 60% of industry costs.
This post explains how AI is transforming the 5G RAN, improving energy and cost efficiency while supporting better use of RAN computing infrastructure.
5G is in full motion
It is now over 4 years since the first 5G networks were launched. A GSMA study projects that there will be over 1.4 billion 5G connections by 2025. The deployment of standalone 5G networks globally is also beginning to increase. To learn more, see Operators Keep Pushing Forward on 5G Standalone Networks.
In the consumer market, 5G is the default upgrade for an ubiquitous cellular communications service. For the enterprise market, 5G has the optimal combination of high performance, mobility, flexibility, and security to provide the connectivity fabric for enterprise use cases (Figure 1).
NVIDIA is driving innovation in cellular networks with a fully programmable NVIDIA Aerial SDK for building and deploying GPU-accelerated 5G virtual radio access networks (vRANs). This is providing the building blocks for a standard public 5G network for a telco or a private 5G network implementation with AI-on-5G.
AI is shaping the current state—and evolution—of 5G
The role of AI in cellular network operations is growing at a fast pace. AI delivers value through the terabytes of data collected every day—from network elements to customer interactions—and through the resulting insights. These insights are related to managing increased network demand, combating cyberthreats, optimizing services, and improving the customer experience.
AI is currently applied across different domains in cellular networks such as RAN, core network, operations support systems (OSS), business support systems (BSS), and cloud infrastructure. These AI-enabled functionalities emerged in 4G, are becoming entrenched in 5G, and will become native in 6G.
While AI will permeate the entire value chain of the industry, its impact on the RAN will be the most profound, particularly given the disproportionate share of industry capex and opex the RAN accounts for. Accordingly, both O-RAN and 3GPP have identified and are working on AI initiatives that improve the performance, flexibility, scalability, and efficiency of the RAN. To learn more, see Embracing AI in 5G-Advanced Towards 6G: A Joint 3GPP and O-RAN Perspective.
AI is transforming the RAN in four key ways: energy savings, mobility management and optimization, load balancing, and Cloud RAN. Read on for more details about each.
The rapid growth of 5G deployment has coincided with a rapid increase in energy costs globally, leading to concerns about high operational costs and carbon emissions. There is the additional concern that some 5G deployments may face hard limits on how much power can be supplied, even if the owners are willing to pay. These concerns create a powerful incentive to increase operational efficiency to achieve higher power efficiencies from current and future network deployments. See Take the Green Train: NVIDIA BlueField DPUs Drive Data Center Efficiency for more details.
The industry has been using reactive and inflexible rule-based techniques to conserve energy, such as switching on/off cells based on different thresholds of cell load. However, AI offers a proactive and adaptive approach, enabling telcos to predict energy efficiency and load in future states. AI also provides better integration between the RAN and virtualized core network functions (with User Plane Function, for example) through offloading networking, security, and RAN tasks to NVIDIA GPUs and DPUs.
Mobility management and optimization
Mobile communications systems have the distinct ability to support handovers of devices from one access point to another. This provides service continuity, supports mobility, and optimizes performance. Expectedly, disruptions, delays, and frequency of handovers add up to inefficiencies in network performance.
Using AI to optimize paging and predict the next cell for handovers offers a significant opportunity to improve performance for advanced features. Such features include sophisticated dual connectivity options, conditional handover, and dual active protocol stack (DAPS) handover.
AI prediction will rely on insights about the possible movement of the device. To achieve this, the Network Data Analytics Function (NWDAF) from 3GPP SA2 provides data from the core network, applications, and the OSS to improve handover performance, predict device location and performance, and steer traffic to achieve quality network performance.
NVIDIA continues to innovate around the NVIDIA Aerial SDK to support these new expectations for data collection and the use of AI for network management.
Handovers enable mobile networks to steer traffic to balance the load across different cell sites and to improve the use of spectrum, RAN, transport, and core infrastructure. This load-balancing decision is achieved by optimizing handover parameters and decisions using current or historical load information.
However, this task is becoming more challenging due to the use of multiple frequency bands and interworking with different RANs. Current rules will increasingly struggle to cope with fast time-varying scenarios with high mobility, and dynamic traffic patterns with a large number of connections.
AI models perform better and can predict load to optimize critical tasks using the collection of RAN data. This will improve network performance and user experience. This is a key driver in the development of proprietary AI tools and the current push for some industry standardization to unlock this opportunity at scale.
Cloud RAN: Colocating RAN and AI in the cloud
While Cloud RAN is not a core application of AI in itself, it is the logical extension of the preceding load-balancing discussion. In this case, a softwarized and cloud-enabled RAN can be colocated on the same cloud infrastructure with AI workloads. Boosting RAN use beyond the typical average 25% of many sites, this will support telcos to extract efficiency gains from an asset that gulps at least 60% of industry capex.
The suitability to share the same computational resources with other AI workloads, the availability of such orthogonal AI workloads, and the ability to use AI to switch dynamically between the different workloads are key to unlocking this opportunity.
This Cloud RAN vision will begin in the 5G era and then mature in the 6G era, as the RAN as a workload in the cloud becomes the ultimate destination. By pooling baseband computing resources into a cloud-native environment, the Cloud RAN solution delivers significant improvements in asset use for both Cloud Service Providers (CSPs) and telcos.
CSPs can run the RAN as a workload alongside their AI workloads within their existing data center architecture while telcos can increase RAN operational efficiency by more than 2x for an estimated >25% impact on EBITDA.
NVIDIA is shaping this evolution with the Cloud RAN solution based on NVIDIA Spectrum switch, NVIDIA H100 CNX converged accelerator, and NVIDIA Aerial SDK. To learn more, see Unlocking New Opportunities with AI Cloud Infrastructure for 5G vRAN.
The role of AI in the RAN is only part of the overall role of AI in telecom network operations. In addition to the RAN, NVIDIA is working with partners on AI-powered operations to use data insights from telco data to create new revenues and improve operational efficiency. Visit the NVIDIA Telecommunications page to learn more.