Enea machine learning delivers 15% RAN capacity increase

September 30, 2020 //By Jean-Pierre Joosting
Enea machine learning delivers 15% RAN capacity increase
With COVID-19 pandemic renewing operator focus on extracting maximum capacity from 4G networks as data usage soars, machine learning is critical.

Enea Openwave has announced that its RAN Congestion Manager (RCM), which incorporates machine learning capabilities, is increasing mobile operators’ 4G RAN capacity by 15% in congested locations. This has enabled customers to cope with the double threat of increased data usage and a slowing down of 5G rollouts, brought on by the COVID-19 pandemic and resulting lockdowns globally. It has given 4G networks a new lease of life without any additional hardware investment.

According to multiple reports, many operators globally have slowed down the pace of their 5G network rollout, in the wake of COVID-19. This has forced a reassessment about how they can extract maximum value from their 4G network assets in the medium term. Also, during lockdowns, some operators faced a surge of over 90% in peak throughput, based on figures from Enea Openwave deployments worldwide.

See also: Machine algoritms could enable small, mobile quantum networks

8 out of 10 of the world's largest operator groups have now deployed Enea Openwave Traffic Management technology with a number of them upgrading to incorporate its machine learning capabilities, to enable optimal bandwidth utilization and improve 4G Quality of Experience (QoE). The machine learning capabilities dynamically predict and identify congestion in the RAN, enabling operators to take immediate remedial action.

See also: Intel, NSF fund machine research for wireless systems

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