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13 May, 2025

Choosing the Optimal AI Platform for RAN Optimization

This whitepaper explores how AI-powered automation is transforming the management and optimization of Radio Access Networks (RAN) for telecom operators. It focuses on the critical decision of whether AI models and rApps should operate on-site in a Service Management and Orchestration (SMO) system or on the scalable public cloud. The paper discusses the strengths and weaknesses of both approaches and the importance of dynamically splitting AI workloads between edge and cloud, especially with the advent of 5G.

Key Highlights:

  • AI-Powered Automation: Transforming RAN management with real-time, data-driven decisions for better performance and lower costs.
  • Deployment Choices: Comparing on-site SMO systems vs. scalable public cloud for AI models and rApps.
  • Edge Computing: Merging on-site and cloud capabilities with advancements in containerization and microservices.
  • rApp Lifecycle: Standardized development lifecycle for AI-driven RAN optimization.
  • Development Essentials: Simplified infrastructure, unified environments, streamlined AIOps/MLOps, robust data management, and efficient resource use.
  • Deployment Needs: Automated pipelines, dynamic resource allocation, load balancing, scaling, real-time metrics, and secure access.
  • On-Prem vs. Cloud: Benefits and drawbacks of on-premises and public cloud development and deployment.
  • Hybrid Approach: Combining on-premises and cloud strengths for flexible, scalable rApp development and deployment.
  • Strategic Decision-Making: Choosing the best approach based on innovation speed, scalability, regulatory requirements, and budget.

Get Expert Insights on AI-Powered RAN Optimization – Download Whitepaper .

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