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GENAR - Generative AI enabled anomaly detection in RAN

URN C25.0.825

Topics 5G, AI (Artificial Intelligence), Autonomous networks

Transforming telecom networks with generative AI accelerated RAN anomaly detection to boost network performance, streamline operations, and ensure exceptional service.

Project companies

Modern RAN generates intricate KPI data like throughput, latency, call success rate, reliability, and energy efficiency. Traditional AI-based anomaly detection struggles with noise, false positives, and handling vast multivariate data, often missing relationships and failing to identify novel anomalies. Next-gen AI overcomes these challenges by reducing noise, uncovering hidden patterns, and improving anomaly detection, minimizing downtime. Our Solution 1. AI-Powered Anomaly Detection: Capgemini's EIRA framework, integrated with Amazon SageMaker’s MLOps, delivers spatial-temporal event correlation, anomaly detection, and RCA with continuous model updates. 2. Generative AI: Using Amazon Bedrock, it predicts anomalies, simulates scenarios, and provides contextual insights. 3. Cloud Services: Incorporates Amazon S3 for storage and Glue for seamless data integration, creating a robust, cloud-native architecture. Impact on Autonomous Networks It plays a crucial role in autonomous network by helping in actionable incident detection, automated RCA, faster resolution and reduction of unnecessary actions in real-time, thus ensuring higher performance and reliability. It helps networks to make true autonomous decision-making, enabling CSPs to achieve level 4 autonomy and higher. Business Value 1. Reduces Mean Time to Detect (MTTD) and enhances RCA efficiency. 2. Boosts network reliability and minimizes disruptions. 3. Offers a scalable, future-proof solution for modern RAN challenges.