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GENAR: GenAI-enabled anomaly detection for 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

Generative AI-enabled anomaly detection and corrective intent-based application generation 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-driven mobile networks overcome these challenges by reducing noise, uncovering hidden patterns, improving anomaly detection, minimizing downtime, and proactively implementing preventive mechanisms in the form of network applications to address these issues in the future. Our proposed solution for this Catalyst 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 Agentic flow to ensure network observability and provides contextual insights . 3. Cloud Services: Incorporates Amazon S3 for storage and Glue for seamless data integration, creating a robust, cloud-native architecture. 4. Aira's GenAI-based App Gen will generate appropriate applications based on RCA and corrective intents to prevent anomaly/issue occurrence. This Catalyst approach plays a crucial role in autonomous networks by helping with actionable incident detection, automated RCA, faster resolution, and permanent fixes for chronic network issues as a Network application. 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.