Transforming 5G Economics: AI’s Dual Impact on Revenue Growth and Cost Efficiency

A lot has been said about the mobile telecom sector over the past few years, but not many of those comments have been positive. We all watched the story play out: billions of dollars were invested in 5G spectrum and infrastructure, but the coinciding revenues didn’t follow suit. The plan was always to monetize 5G in the enterprise, which is currently taking shape in the form of private 5G networks and operational technology transformation—a trend I’m optimistic about in the long term. But what about the large-scale consumer networks into which the industry invested billions?

Return on Invested Capital has long been the key metric for our industry. In the 2G days, we invested a ton of capital and delivered revenues in the form of voice services. In the 3G era, that capital outlay was returned in the form of SMS charges at 10 cents a pop. 4G was monetized via mobile broadband consumption. 5G hit a wall. Consumers are glued to their smartphones from the moment their built-in alarm app wakes them up until they finish up with their meditation app at bedtime, yet they don’t want to pay a penny more for the incredible connectivity and coverage operators provide.

Not surprisingly, because of this, there is tremendous financial pressure on mobile telecom operators. When those returns on the invested capital fail to materialize, there is pressure to reduce that CAPEX number. The new normal for the big 3 Tier 1 operators in the US is more than 10 billion dollars less than the level it was a couple of years back. Pressure on OPEX for operators isn’t a new 5G phenomenon, but the need for more automation and increased efficiency is under the spotlight more than ever. If the industry norm is to spend $5 in OPEX for every $1 in CAPEX, then constant pressure is inevitable.

Let’s look more closely at the CAPEX problem. How can the mobile telecom industry meet the insatiable demand for more coverage and faster speeds without continuing to invest capital at the previous rates? The answer is obviously that we need to do more with less, but how? In most cases, it turns out that mobile operators aren’t getting the full capacity they paid for. Billions were spent on Spectrum and Radio Access Networking (RAN) equipment, but the throughputs are often not what they should be because of interference. Interference can stem from various sources, and in cell sites with high levels of interference, throughput can be degraded by as much as 40%. Often, these are the most critical and dense sites in competitive markets. If we can remove or mitigate that interference, we can reclaim that 40% of capacity and avoid the additional CAPEX outlays.

When it comes to the OPEX side of the house, luckily AI has arrived just in time to save the day! I say that in jest because of all the hype, but I do believe AI and ChatGTP are part of the solution. As an industry, we were working on machine learning for 10 years before the ChatGPT craze. The technology has matured tremendously in that timeframe, and ChatGPT has mainstreamed the concept of daily AI usage. The promise of closed-loop, AI-driven automation and AIOps is now a reality. I’d argue that the technology for many use cases has been ready for some time, but it’s only recently that we’ve crossed the comfort threshold, thanks in part to ChatGPT. Not long –ago, telecom executives might have been fired for implementing an AI-based, closed-loop platform; now they might get fired if they don’t. The logic is straightforward in this area: more automation creates opportunities for greater efficiency and allows the reallocation of OPEX to higher-order activities and workstreams that can’t be automated.

What role does Spectrum Effect play in all of this? The team at Spectrum Effect foresaw this trend and has been working on AI and Machine Learning (specifically convolutional neural networks) before it became mainstream and cool. The team gathered data from more than 50 mobile operator networks across the globe and trained the Spectrum-NET AI models to learn the unique signatures left behind by each type of interference. When an operator deploys the Spectrum-NET platform, it maps out the exact levels, types, and locations of interference in the network. Where it is suitable, the AI engine then closes the loop by pushing changes into the network configuration to optimize the throughput and end-user Quality of Experience (QoE). The system continuously optimizes the network in a closed-loop manner, adjusting based on changes in interference levels (such as tropospheric ducting, which varies with weather patterns).

The state of the mobile telecom industry is clear, and the business requirements are well understood. Operators need to deliver more coverage and higher speeds at lower CAPEX levels, they need to show revenue growth, and they need to get more efficient and effective with their OPEX. Spectrum Effect is uniquely positioned to tick all three of these boxes:

• The Spectrum-NET platform optimizes existing spectrum and RAN investments, enabling operators to reclaim up to 40% of the CAPEX already invested in sites with high levels of interference.
• By mitigating interference conditions, Spectrum-NET improves QoE for end users. As coverage and QoE are the largest factors in churn, this QoE improvement reduces churn, giving top-line revenues a boost.
• Interference is one of the most challenging and impactful issues in the RAN, and mobile operators are already throwing a ton of OPEX at the problem, often using manual, inefficient methods and costly third-party services. Spectrum-NET significantly simplifies and automates RAN optimization with AI-based, closed-loop methods.

We have not encountered a network on the planet that hasn’t benefited from the Spectrum-NET platform, and this is especially true in 5G, where interference is typically more significant and impactful. Some of the top operators around the world are benefiting from Spectrum-NET today, and many more are planning their deployments. If your team isn’t already working with Spectrum Effect, now is the time to reach out and learn how we can help.

This is what we are doing today, and it’s quite compelling. Next time, I’ll share what we are working on, it’s even more exciting.