Artificial intelligence is helping telecoms operators improve their 4 G networks’ RAN speed by 15 percent.

During the pandemic, more individuals than ever rely on telecoms networks to function, play, and stay linked. To ensure that their current networks have adequate capacity to cope with demand, operators are doing all they can.

Gorkem Yigit, Analysis Mason ‘s principal analyst, said:

“Video streaming continues to rise at a high pace year on year, aggravated by the pandemic and the subsequent lock-downs,

Yeah, 5 G captures the spotlight, but the bulk of this traffic is borne by 4G. Thus, though 5 G infrastructure investment continues, operators need smart ways to optimize and enhance current 4 G network capabilities in the short to medium term, keeping their CAPEX to a minimum.

Traffic management technology from the Openwave subsidiary of Swedish company Enea has been introduced by 8 out of 10 of the world’s largest operator groups. Many of these have since been revised to provide capabilities for machine learning.

Openwave reports that, based on its estimates, during lockdowns, some operators faced a 90 percent rise in peak throughput.

In the RAN (Radio Access Network), which exists between consumer devices such as wireless devices and the core network of an operator, machine learning helps to anticipate and recognise congestion.

John Giere, Enea Openwave ‘s Chief, commented:

Manual configuration and network investment are needed for traditional mobile data management; it is no longer fit for purpose.

The shot in the arm they wanted was provided to existing 4 G networks by Machine Learning. Without external probes or modifications to the RAN, it can run dynamically, providing additional capacity at a time when operators most need it.

In congested areas, the use of machine learning has increased the 4 G RAN ability of operators by 15 percent, providing more proof of how AI technology can be used to rapidly fix real-world issues.



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