From the course: AI in RAN (Radio Access Network): Transforming Mobile Networks

Unlock this course with a free trial

Join today to access over 25,300 courses taught by industry experts.

Non-real-time RIC and rApps: Long-term network optimization

Non-real-time RIC and rApps: Long-term network optimization

From the course: AI in RAN (Radio Access Network): Transforming Mobile Networks

Start my 1-month free trial Buy for my team

Non-real-time RIC and rApps: Long-term network optimization

“

What if every cell tower could cut its energy bills by 18% overnight? With OpenRAN's non-real-time intelligent controller, that is not a dream, it is automated reality now. Let's see how smart R apps turn idle radios into savings. Excess network is the cost monster of telecom. 70% of total capital expenditure comes from the radio excess network. In fact, 60% of operational expenses also comes from the radio excess network. Within that, nearly 30% of the operational cost is just the electricity which is being consumed by radio excess network. If you see, in nutshell, around 18% of total OPEX belongs to the electricity only, which is quite a huge amount. Now operators may have different bands for capacity and coverage, but we need to focus on the optimum capacity utilization to reduce this operational expenditure. And how do we do that? We use a smart sleep mode activation by using the non real time intelligent controller. What happens in this case, In case of Rapps, the Rapps monitor…

Contents