Via EdgeIR.comBy Rob Hughes, Head of Wireless Marketing for 1Finity, a Fujitsu company. 

As data demands escalate, today’s networks are growing and evolving at a rapid pace. Mobile networks, in particular, have changed substantially in response to expectations of readily available hyperconnectivity at our fingertips everywhere we go.

At the same time, mobile network operators (MNOs) are facing a confluence of pressures and challenges. Market consolidation, increasing competition, rising supply chain costs and shrinking profits all contribute to an urgency to offer profitable new services while reducing total cost of ownership (TCO).

Growing reliance on artificial intelligence (AI) offers promise for MNOs to address these challenges. The important question is not whether AI belongs in mobile networks, but how operators will turn AI into both performance gains and new revenue.

The Power of AI

With the evolution to 5G, the mobile radio access network (RAN) has seen significant changes. Increased throughput speeds have accelerated the adoption of video streaming and encouraged a wide range of content rich applications that have driven increased demands on the network.

As a result, MNOs are looking for solutions to improve spectral efficiency and capacity while reducing TCO. Indeed, we have seen quite a bit of positive enthusiasm around harnessing the power of AI automation to streamline management and improve RAN performance. With AI optimization, MNOs can boost network performance to deliver an outstanding customer experience.

Taking this a step further, there is genuine interest among MNO pioneers to fully integrate AI resources into the RAN infrastructure and implement AI-RAN technology. With the technical breakthrough offered by AI-RAN, MNOs can further enhance RAN performance by increasing network throughput, reducing energy consumption and improving spectral efficiency.

While these network enhancements are exciting, even more enticing are the business opportunities presented by AI-RAN.

Pinpoint the Real Opportunity

AI-RAN offers significant monetization potential and competitive advantages for network operators. In a recent Future of AI survey by Heavy Reading analysts, however, only 38 percent of MNOs cited ‘the ability to offer AI as a service’ among the top three benefits they expected from AI-RAN.

For some network operators the evolution to AI-RAN is seen as a challenge to surmount, requiring effort and financial investment. Yet, AI offers the ability to deliver promising new services – particularly through AI-as-a-Service (AIaaS) and GPU-as-a-Service (GPUaaS) – that can create the new revenue streams needed to pay off 5G and AI-RAN investments.

In fact, the GPUaaS market is expected to exceed $25 billion by 2030 with an annual growth rate of 26.5 percent. That’s because cloud-based GPU resources are increasingly desirable for AI training, batch AI/data analytics, video rendering and simulation/engineering workloads.

Disrupt the Cycle

Historically, emerging network technologies have faced a “chicken and egg” dilemma regarding investment. Software developers typically hesitate to create content until infrastructure is widespread, while MNOs often delay deployment until there are sufficient revenue-generating applications to justify the cost.

The nature of AI-RAN infrastructure enables MNOs to disrupt this traditional cycle. This can be achieved by leasing excess compute capacity within the network, generating revenue without waiting for a broad base of applications that need the low latency or data sovereignty of AI-RAN to become available. Much like a high-end restaurant must maximize table turnover to offset premium real estate costs, MNOs can monetize their valuable AI-RAN resources to pay for themselves.

Because base station traffic fluctuates throughout the day, the compute capacity of a RAN distributed unit (DU) is built to handle the absolute peak traffic of the busiest day of the year, plus a margin for future expansion. Consequently, significant compute capacity remains idle most of the time.

Today that excess capacity sits idle, but that doesn’t need to be the case. Given the growing market for GPUaaS, MNOs have a promising opportunity to generate near-term revenue while they gradually introduce new AI services.

Fast-track to Revenue

Current GPUaaS and AIaaS solutions enable highly flexible billing models, including hourly or even minute-level options, without long-term obligations. This allows MNOs to sell GPU capacity contracts that fluctuate according to daily demand cycles. Moreover, MNOs maintain operational control by reserving the right to lease this capacity only when it is not required for their own internal needs, taking full advantage of idle capacity.

By leveraging AI resources to monetize excess capacity, MNOs can effectively fast-track to AI-RAN immediately, reaping the performance benefits while generating revenue to cover initial costs. As the MNO’s own demand for AI compute resources grows, they can then gradually scale back the amount of capacity available for lease.

The Road to AI-RAN

The transition to AI-RAN requires more than just new technology – it requires new business models, skillsets and mindsets. We are just at the beginning of a long journey that will bring a host of new innovations along the way. And as with any new technology, a small number of pioneers and innovators are pushing the envelope and deploying something new in order to spur AI-RAN advancements.

Although the pioneers are ready to implement AI-RAN now, the majority of deployments will be a year or two behind, but that’s par for the course. Network change can be slow to materialize, such as was the case with 5G Standalone (SA) networks, but adoption will happen in time.

Those MNOs willing to start the journey now will have more time to learn both the AI-RAN technology as well as the necessary changes in operations and business models. That head start will put them firmly ahead of the competition on the path to intelligent, next-generation AI-powered networks.

About the author

Rob Hughes is Head of Wireless Marketing at 1Finity, overseeing solution and marketing strategy for the company’s wireless portfolio. With over 20 years’ experience in telecommunications, he has a broad range of expertise in wireless, optical, wireline, enterprise, open networking, analytics and cable solutions. Rob has a unique perspective based on both business and technical experience. He studied Engineering at University of Victoria, British Columbia, and taught Marketing Strategy at the University of Texas.
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