AirHop Auptim® AI/ML Energy Savings MIMO Adaptation rApp

Auptim ESMA rApp uses an AI-based traffic prediction model to dynamically reconfigure MIMO antenna configurations between a high capacity / high power and low capacity / low power states to maximize energy savings and ensure ample RAN capacity for the traffic load.

Since RAN power consumption contributes to roughly 70% of RAN operating expenses, reduction in power consumption by the Auptim ESMA rApp directly contributes to reduction in operating expenses of a RAN.

Auptim ESMA rApp monitors parameters associated with end user Quality of Experience (QoE), such as user accessibility and physical resource block utilization, and makes intelligent trade offs between the MIMO antenna configuration to maintain a high end user QoE, while reducing RAN power consumption.  In operation, user accessibility is maintained at nearly 100%, and PRB utilization headroom is sufficiently low enough to support rapid spikes in traffic demand.