Why AI-Powered RAN Is an Energy Efficiency Breakthrough - Seite 2

The sites and cells deployed in a network may be classified as underlaid (coverage) and overlaid (capacity). When an overlaid capacity cell is turned off, the traffic load existing in the overlaid cell is offloaded to underlaid (coverage) cells. The underlaid (coverage) cell monitors traffic conditions and key performance measures like access to the network, service quality, retainability and mobility to allow the overlaid (capacity) cell to go into sleep mode and turn on the sleeping cells when required. Once overlaid cells are turned off for energy saving reasons, the reference signal interference in the network is also reduced, improving UE throughput, and reducing operating expenditure. This phenomenon of sleeping cells with generic and static configured parameter thresholds may result in coverage loss or not using the spectrum efficiently with no optimum energy saving. This capability of cell sleep and wake up detection should be adaptive to network traffic conditions, radio resources availability, user density, service usage, user experience and overall network performance to provide best energy efficiency.

One size does not fit all

It is a known fact that the radio access network (RAN) accounts for nearly about 80- 85 percent of overall energy consumption. Depending on the geographical location and varying data traffic loads, it would be wise to put some of the capacity cells into sleep and wake them based on the traffic demand. The image below shows a cluster of cells with different energy consumption in accordance with user and network activity and depicts how not all cells need the same energy to meet the traffic demand.

This calls for having a customized approach for each capacity cell to be in a sleep or awake state. An ML/AI based approach expands the potential for such energy-saving opportunities across the network at cell level. One of the solutions is enabling dynamic thresholds configuration for cell sleep mode for coverage & capacity.

AI based dynamic thresholds for cell sleep mode

Enabling and controlling the cell sleep mode based on the physical resource block (PRB) utilization and RRC connection-based thresholds, without impacting the customer experience, needs a careful monitoring of key performance scenarios like network availability, reliability, traffic pattern, services offered and spectrum usage, while considering same of neighboring cells as well. To achieve this, it is important to determine the utilization of each cell layer for the next few days and, most importantly, to determine the impact on customer experience for the same period.