As telecommunications networks expand and evolve to deliver ever more and increasingly complex services, so their energy consumption grows too. Already, energy use by telcos in developed countries accounts for up to three percent of all electricity produced.
On a competitive market, and with energy costs rising, CSPs are already facing challenges of maintaining profit margins, so the financial costs of this are significant. Yet the high energy consumption of telcos also has an environmental impact. What’s more, the financial and environmental costs are only likely to increase. High data speeds mean more frequencies are designated for radio network use, and the combination of the Internet of Things and demand for location-based services is driving ever denser and more complex network infrastructure. Add to that the growing use of data centers and their associated energy consumption, and it’s clear that telcos must find a way to bring down the financial and environmental costs and impact of their electricity use.
The answer lies in automation for the optimization of energy consumption in a way that doesn’t compromise service levels.
The role of artificial intelligence and machine learning
Traditional energy-saving measures for telcos relied on incredibly labor-intensive and time-consuming monitoring of usage and demand which does not allow flexibility or take into account real-time situations. Now, it’s possible to migrate virtual logic functions between physical devices. This brings a number of advantages such as reduced energy consumption, the ability to temporarily shut down certain physical locations and prioritize those locations using green energy, and the optimization of carbon footprint management measures.
With AI and ML, operators can go a step further. Intelligent automation means networks can constantly draw upon historical and real-time data to configure and optimize themselves. They do this based on pre-defined rules and policies which can balance energy consumption and costs, network and service quality, and the need to prioritize carbon footprint reduction.
This doesn’t mean a complete end to human intervention in network optimization and management. AI decisions must initially be monitored and assessed, and the ML process may need to be guided. However, a properly implemented system can be relied upon to always select the most energy-efficient network configuration in any given scenario. For telcos, this means that there is no reason to reserve physical resources – and the network can be allowed to switch them off rather than leave them idling.
In terms of costs – both financial and environmental – this can have a significant impact for telcos, for their customers who place great importance on green initiatives, and on the world we live in.
Find out more about using automation to achieve network management optimization, make savings and reduce your telco’s carbon footprint, in the new white paper and short video from Comarch’s 2023 excellence in telco campaign. Get your free copy and watch here.