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AI-Enabled Solutions Are the Key to Help Cellular Operators Reach Carbon Reduction Targets

Today, everything is connected – from phones to watches to cars – modernizing industries, and cellular connectivity changing the way we live, work, and learn. But, at the same time carriers are building out the infrastructure to support this enhanced connectivity, they are setting aggressive decarbonization goals, including public Net Zero ambitions.

What that means for communications service providers (CSPs) is that they can no longer view performance as the sole criterion for success. The telecommunications network of today must be powerful and nimble – and it needs to be sustainable, too. And, AI is the key to creating and managing energy efficiency, reducing carbon footprint while continuing to deliver the high-quality service customers demand.

RAN Represents the Bulk of Energy Usage – And an Opportunity for Savings

The telecom industry is in a unique position when it comes to decarbonization. Advanced connectivity is key to enabling carbon reduction in other industries, and there is also a tremendous opportunity for the telecom industry to lead by example by cutting its own emissions. 

The Radio Access Network (RAN) equipment accounts for 80% of the energy use for an operator, based on a benchmarking study by the GSMA. Yet the GSMA also reports 62 operators, representing 61% of the industry (by revenue), have committed to a science-based carbon reduction target, pledging to reduce direct and indirect emissions by 2030.

As networks become increasingly complex, managing energy usage can’t be done manually. Software that monitors for periods of low usage to shut down unnecessary equipment is a good first step, but purely reactive solutions won’t be enough to manage traffic demands while cutting emissions.

AI is the missing ingredient that will help CSPs gain insights to inform ongoing energy savings, while also driving real-time efficiencies through operational orchestration.

AI is what will take network energy management from reactive to predictive, making decisions based on the network’s needs.

For example, AI functions can decide, based on data, what resources will be needed in the coming hours and days, and if all the capacity in the frequency bands within the RAN will be needed. They can then turn off or on different frequency bands or other resources according to predicted demand.

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Because, AI programs dynamically learn, adapt, and act accordingly, these tools enable operators to control cells dynamically and in turn, serve dynamic traffic patterns instead of just peak traffic.

Taking things to the next level, CSPs will be able to use AI and machine learning predictions to build digital twins of the RAN environment, allowing them to test and develop energy-saving features without any risk to their actual, live network. 

AI Can Identify and Enable Savings Beyond Network Operations

And it’s not just the day-to-day network management where AI tools can help reduce energy usage. They can also help operators diagnose and resolve issues remotely, getting things right the first time to reduce unplanned downtime – as well as achieving carbon reduction through fewer maintenance truck rolls. And, with AI insights, CSPs can identify where new cell sites or other resources should be deployed, creating efficiencies with deployments and equipment buildouts.  

Another area where AI can enable energy savings is in the passive equipment at individual cellular sites, things like climate control or air conditioning.

AI solutions can help companies manage the energy infrastructure on-site more intelligently. They can also help in areas where demand response programs are enabled, or when government regulations or tariffs are in place to control peak demand.

For example, AI-powered applications can switch to battery power during times when tariffs are higher (peak load shifting), or when the grid power usage reaches a certain power grid alternating current (AC) limit.

There is a real urgency behind the need for CSPs to make operations more energy efficient, as the telecom industry works to meet self-declared sustainability targets as well as government mandates. Yet, there can be an understandable hesitancy to enact some energy-saving solutions, for fear of disrupting networks or reducing the quality of service. Luckily, AI can be a solution for both these issues – giving CSPs the tools they need to manage complex network functions in the most efficient manner possible, while also collecting and analyzing data to test and refine new applications in a virtual environment, such as a digital twin. Cellular networks need to get smarter to meet sustainability expectations without affecting customer experience – and AI is the key to making it work.

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