CRICAC For Open RAN Energy Efficiency

T-Labs envisioned a customized solution for O-RAN energy saving using a hierarchical xApp-rApp tandem working at various timescales, that could cover large-scale networks, be scalable, and support real network data that Deutsche Telekom would provide.

The idea behind this xApp/rApp cooperation is the synergy it brings. While the xApp provides fast, localized control with significant energy savings, the rApp can enhance dynamic adaptability and stability, especially during anomalies. Using an App tandem would, for example, result in fewer cell on/off ping-pongs compared to a single App operation.  Concerning energy savings: while major energy savings are achievable with xApp alone, fine-grained optimizations require rApp integration, which of course significantly increases design complexity and requirements on the target RIC platform.

To show robustness in scale, the algorithm-tandem was tested against various conditions and network topologies, showing the stability of energy savings and network QoS performance. The PoC was performed at the i14y Lab, proving the lab to be “the optimal reference setting to showcase our solution”, as the project partners pointed out.

“We are delighted to have collaborated with T-Labs on this exciting project to develop a customized xApp-rApp tandem and working alongside Keysight and Juniper Networks to conduct the corresponding PoC at the i14y Lab,” said Marcin Dryjański, CEO of Rimedo Labs. “Leveraging network data from Deutsche Telekom allowed us to validate the algorithms under real-world network conditions and prove the algorithm's scalability in a large, heterogeneous network. During the PoC phase, the xApp-rApp tandem successfully demonstrated its intended functionality and compliance with O-RAN interfaces, within the framework of commercially available solutions.” 

Given the project’s outstanding success, delivering “an important contribution to overarching network efficiency”, as Thomas Lips (Deutsche Telekom) added, we can conclude two key points:

• This seems to be a very promising approach for more energy efficiency in Open RAN 
• The use of realistic network data: the proper adaptation of the realistic mobile network data allowed verification of the algorithm in various parts of the network under real conditions. The availability of real network data is an invaluable resource in such projects.