How Elasticsearch machine learning helps telcos reduce power consumption in 5G networks

As telcos deploy 5G networks, power consumption has become a significant concern. These networks require a large number of base stations and powerful hardware components to offer higher data rates and improved capacity. As a result, telcos are exploring various solutions to reduce power consumption in 5G networks. Machine learning from Elasticsearch^® is one such solution that can help telcos reduce power consumption in several ways.

Intelligent network planning

Machine learning can help telcos analyze data from existing 5G networks, such as user traffic patterns and base station performance, to optimize network planning. By predicting future traffic patterns and base station usage, telcos can build more efficient and power-friendly networks. This can help reduce capital expenditure on new hardware and save on operational expenditure for energy costs.

Pattern recognition and dynamic adjustment

Using Elasticsearch, telcos can collect and analyze data from various sources, including user devices, base stations, and core networks, to identify patterns of low usage during specific times or at certain locations, such as airports. By employing machine learning algorithms, Elasticsearch can identify and predict these patterns, enabling telcos to proactively adjust network parameters, such as signal strength and available resources like radio cells, to optimize power consumption without compromising network performance. This approach contributes to a decrease in operational expenditure on energy costs, further benefiting the telco industry.

Energy-efficient hardware selection

Machine learning can help telcos evaluate the power consumption of different hardware components, such as base stations and network switches. By analyzing the power consumption of those components under various workloads, telcos can select the most energy-efficient hardware for their network. This can help reduce capital expenditure on new hardware and save on operational expenditure for energy costs.

Predictive maintenance 

Machine learning can help telcos predict equipment failures and maintenance requirements. By detecting potential equipment issues early, telcos can perform proactive maintenance and reduce the number of power-consuming equipment replacements. This can help reduce capital expenditure on new hardware and save on operational expenditure for energy costs.

Energy management

Machine learning can help telcos optimize energy management in their data centers. By analyzing power usage patterns and identifying energy-saving opportunities — such as optimizing cooling systems, understanding reactive power influence and source, or using renewable energy sources — telcos can reduce power consumption. This can help minimize operational expenditure on energy costs.

Conclusion

Machine learning from Elasticsearch can help telcos reduce power consumption in 5G networks while maintaining network performance, improving the user experience, and saving on costs. By adopting these solutions, telcos can create more sustainable and efficient 5G networks for the future, while also reducing capital and operational expenditure on hardware and energy costs. With the growing demand for 5G networks, machine learning from Elasticsearch offers a promising solution for telcos looking to meet this demand while also reducing power consumption and costs, and ultimately, reducing their CO₂ footprint and being more environmentally conscious.

Watch Improving visibility into modern telco networks or learn more about Elastic and Telecommunications.