Title: Intelligent Networking, AI and Machine Learning for Telecommunications Operators
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Great progress in industry adoption, but challenges remain
Table of Contents:
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Key drivers for Intelligent Networking:
- Network Planning and Design:
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- Generative AI for precise small cell placement
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- , MIMO antennas, beamforming, and
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- optimized backhaul connections
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- Shared AI and 5G Infrastructure: Leveraging unused 5G RAN infrastructure resources for training and inference, enhancing AI capabilities and network efficiency.
- Code Generation for Network Protocols: Enabling co-pilot functionality for generating software implementations of network protocols specifications, facilitating protocol development and deployment.
- Capacity Forecasting for Access, Edge, and Core Networks: Utilizing AI for accurate load prediction on each RAN site to optimize network capacity and avoid unnecessary upgrades.
- Automated Closed Loop: Employing AI models trained on operational data to ensure network assurance through automated processes.
- Predictive Maintenance: Utilizing AI to forecast equipment failures, enhancing maintenance efficiency and network reliability.
- Network AIOps: Implementing AIOps methodologies to automate and streamline network operations, improving overall efficiency.
- Self-Organizing Networks (SON): Harnessing AI-based algorithms for autonomous optimization and management of network resources in Self-Organizing Networks.
- Technical Assistant/Customer Service: Real-time guidance from AI-based tech assistants, enhancing customer service and field operations efficiency.
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- Traffic Management: Dynamic rerouting of traffic based on AI analysis to efficiently utilize network resources and improve user experience
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1 Executive Summary - Key Takeaways and Overview
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