RADCOM Drives Cost Savings for 5G Network Operations with AI-Powered Analytics for Automation

RADCOM introduced its latest Artificial Intelligence (AI)-driven use case to drive automated network management while saving costs and enhancing the end-user experiences. RADCOM’s Virtualized Network Operations Center (vNOC), enabled by the latest version of the RADCOM ACE solution, redefines network operations through extensive automation and 5G analytics.

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The vNOC powered by AI-driven analytics resolves customer-affecting issues through anomaly detection and automatic root cause analysis, drastically improving the first-time resolution rate. This helps drive down costs and make network teams more efficient while improving customer services. AI-driven analytics can also intelligently monitor and enhance premium services like roaming, private networks, video streaming Fixed Wireless Access (FWA), Internet of Things (IoT), and Voice over New-Radio (VoNR) while saving significant operational costs.

“5G network complexity has increased the need for automation and advanced AI-driven analytics,” said Rami Amit, Chief Technology Officer, and Head of Product at RADCOM. ”We continue to innovate and develop our RADCOM ACE solution by adding more automation and AI-powered insights without manual intervention. Our AI-powered analytics helps operators automate their network and service operations centers and save CAPEX/OPEX through proactive optimization.”

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The latest version of RADCOM ACE delivers comprehensive Machine Learning (ML) based 5G network diagnostics, extensive root cause analysis automation, and proactive network insights, identifying and resolving network anomalies to boost NOC/SOC operations. It offers operators an ML-based approach for the Network Operations Center/Security Operation Center (NOC/SOC), highlighting network degradations and drastically reducing time to resolution while streamlining workflows for engineering teams.

By using General Adversarial Networks (GAN), RADCOM ACE unlocks the power of generative AI for network analytics and improves the proactive analytics that feeds the vNOC. The solution generates synthetic data as an alternative to real network data to train and strengthen AI models for advanced 5G use cases and deliver new use cases.

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