TELECOM LAB PROJECT
Virtualized resource management in O-RAN
This project address the challenges arising from the virtualization of both the RAN (Radio Access Network) and key core network components within the O-RAN ecosystem. As these virtualized elements increasingly coexist on the same COTS (Commercial Off-The-Shelf) servers, they introduce additional complexities in resource management, latency optimization, fault detection, and dynamic configuration.
By addressing these challenges, we aim to create a cohesive and adaptive network infrastructure, unlocking the full potential of O-RAN’s open and flexible architecture for next-generation communication systems. To achieve this, we are leveraging advanced AI/ML-based solutions to:
Ensure seamless integration.
Optimize performance.
Enhance reliability across these components.
USE CASE
Key Performance Indicator (KPI) prediction is a transformative use case in the Open RAN (O-RAN) ecosystem, leveraging AI-driven analytics to proactively monitor, forecast, and optimize network performance.
With the growing complexity of disaggregated and virtualized RAN environments, traditional reactive approaches to performance management are no longer sufficient. Predictive KPI analysis addresses this gap, enabling operators to enhance service quality, reduce downtime, and optimize resource utilization.
KPI prediction in O-RAN faces several significant challenges, such as:
The sheer volume and variety of data generated by disaggregated and multi-vendor RAN components. It can be difficult to manage, requiring robust systems for data collection, integration, and processing.
Timeliness, as predictions must be delivered quickly enough to enable real-time corrective actions and ensure minimal disruption to network performance.
High adaptability and generalization of AI/ML models used for prediction. They has to be capable of maintaining accuracy across diverse network conditions, configurations, and vendor implementations.
Overcoming these challenges is essential to fully unlock the potential of predictive analytics in the O-RAN ecosystem and achieve smarter, more proactive network management.
INNOVATION DIRECTION
Our innovation in KPI prediction focuses on developing advanced xApps that utilize state-of-the-art Long Short-Term Memory (LSTM) models to forecast critical network performance metrics.
LSTM models, known for their ability to capture temporal dependencies, are uniquely suited for analyzing time-series data generated by Open RAN (O-RAN) systems. By leveraging these models, our xApps can predict key metrics (latency, throughput, packet loss, resource consumption…) with high accuracy.
These predictive insights enable proactive network management, allowing operators to address potential issues before they impact performance. This approach not only enhances the efficiency and reliability of O-RAN systems but also supports the deployment of intelligent, adaptive and dynamic networks capable of meeting the demands of next-generation services.