Deutsche Telekom Leads Multi-Vendor Trial Demonstrating Open RAN’s Potential

Deutsche Telekom has reaffirmed its dedication to the advancement of Open RAN technology by unveiling the specifics of a groundbreaking multi-vendor trial. This trial showcases the remarkable potential of Non-RT RIC (RAN Intelligent Controller) and rApp (third-party applications) concepts in automating and optimizing disaggregated Radio Access Networks (RAN).
Deutsche Telekom brandThe Non-RT RIC introduces intelligence, agility, and programmability to disaggregated RANs, enabling the deployment of third-party applications (rApps) capable of closed-loop automation and optimization of RAN components and resources. However, integrating Non-RT RIC, rApps, and SMO (Service Management and Orchestration) from multiple vendors presents unique challenges that demand innovative solutions.

In collaboration with industry leaders such as AirHop, Juniper Networks, VIAVI Solutions, and VMware, Deutsche Telekom recently completed a Proof of Concept (PoC) for RAN closed-loop optimization within its lab environment. The multi-vendor setup was based on ONAP and O-RAN specifications.

During the PoC, the partners successfully executed two use cases:

Physical Cell Identifier (PCI) Optimization: This focused on the detection and resolution of PCI confusion and collision scenarios.

Energy Savings Dynamic Multi-Carrier Management (ESMC): Utilizing an Artificial Intelligence and Machine Learning (AI/ML) model, this use case determined the optimum time to enable/disable sleep mode on capacity cells, thus conserving energy while maintaining the user’s quality of experience (QoE).

Initial tests were conducted in a real end-to-end lab setup using a small O-RAN network, validating end-to-end configuration and performance management integration for a real network environment. More complex network setups were tested using an O1 network emulator (RIC tester) to assess rApp logic and benchmark RIC components.

Key contributions from the partners included:

Deutsche Telekom’s self-developed SMO framework and Non-RT RIC solution.

Integration of Juniper Networks and VMware’s Non-RT RIC products into Deutsche Telekom’s SMO framework.

AirHop’s integration of two rApps for PCI optimization and ESMC with each Non-RT RIC.

VIAVI’s provision of the RIC tester to emulate the O1 interface.

While the multi-vendor framework posed integration challenges, the PoC demonstrated the promise of adopting the SMO, Non-RT RIC, and rApp framework. This approach allows for the separation of optimization algorithm development, supporting platform development, and system integration, enabling different components from various parties to create a truly disaggregated RAN optimization concept.

Petr Ledl, Vice President and Head of Network Trials and Integration Lab at Deutsche Telekom, stated, “With this PoC, we set out to assess the technical integration complexity of the components delivered by each party, the level of customization required, to gauge the maturity of products and to identify potential future standardization requirements.”

Understanding RAN Intelligent Controller (RIC) and Apps

A RAN Intelligent Controller (RIC) is a vital software-defined component within the Open RAN architecture responsible for controlling and optimizing RAN functions. It facilitates the swift integration of third-party applications (Apps) that automate and optimize RAN operations on a large scale. The RIC enhances operational efficiency, reduces introduction time, and ultimately lowers the total cost of ownership (TCO) for mobile operators while improving the quality of experience (QoE) for customers.

The non-real-time RIC (Non-RT RIC) is a crucial part of the Service Management and Orchestration (SMO) framework, centrally deployed within service provider networks. It empowers control and policy guidance over RAN elements and resources with a precision greater than one second, all while facilitating the operation of rApps. Additionally, it harnesses the power of AI and machine learning, utilizing long-term network data and enrichment data from external applications to drive AI/ML-driven applications for RAN optimization.