UST RAN Capacity Forecasting: AI driven RAN Capacity Forecasting

UST RAN Capacity Forecasting is an AI-driven network planning solution that helps communications service providers predict demand, identify future congestion risk, and make smarter capacity investment decisions across the radio access network. The solution analyzes historical and real-time RAN data, traffic trends, subscriber growth, mobility patterns, seasonality, events, and network configuration changes to generate accurate multi-horizon forecasts at the cell, sector, site, cluster, band, and slice levels.

Designed for telecom network planning, capacity management, and operations teams, UST RAN Capacity Forecasting moves operators from manual, backward-looking planning cycles to predictive, model-driven capacity intelligence. By combining AI/ML forecasting, data engineering, advanced analytics, and GenAI-driven insights, the solution helps CSPs anticipate congestion weeks or months in advance, prioritize capacity actions, optimize CAPEX deployment, and protect customer experience.

The solution supports critical planning workflows, including AI-driven network traffic forecasting, coverage and capacity planning, solution degrow, CAPEX prioritization, and material planning. It can be deployed as a professional service engagement and aligned to AWS-based data, analytics, and machine learning environments for scalable telecom AI use cases.

Key Features

• AI-driven RAN traffic forecasting: Uses machine learning models to forecast 4G and 5G traffic demand across busy-hour, daily, weekly, and longer-term planning horizons.
• High-resolution capacity intelligence: Generates forecasts at the cell, sector, site, cluster, band, and slice levels to uncover localized capacity hotspots.
• Multi-source data correlation: Analyzes radio resource KPIs, user metrics, signal quality indicators, temporal data, historical traffic, RAN OSS data, customer data, and optional external factors.
• Advanced feature engineering and model tuning: Applies clustering, volatility segmentation, normalization, time-based feature creation, algorithm R&D, fine-tuning, and validation.
• Time series, regression, and ensemble modeling: Uses AI/ML techniques to model traffic behavior and forecast future network demand.
• Continuous learning and rolling updates: Supports monthly forecast updates so models adapt as network behavior, subscriber patterns, and service demand evolve.
• Capacity planning dashboard: Visualizes traffic forecasts, actual-versus-predicted trends, anomalies, site-level and cell-level views, forecast accuracy, and prioritization.
• Planning recommendation support: Guides actions such as sector or cell additions, site or spectrum additions, coverage expansion, capacity upgrades, solution degrow, and spectrum refarming.
• GenAI-enabled insights: Provides site performance snapshots, network health diagnostics, event-aware planning, and actionable recommendations.

Key Benefits

• Customer experience protection: Identifies congestion hotspots before they degrade network performance, service quality, or subscriber experience.
• CAPEX optimization: Directs investment toward the sites, sectors, cells, and clusters with the highest forecasted capacity need.
• Lower cost-to-serve: Replaces manual planning cycles with faster, more consistent, data-driven capacity decisions.
• Faster network planning: Gives planning teams predictive insights, early warning alerts, and clear prioritization for capacity actions.
• Higher forecast accuracy: Correlates traffic demand with subscriber growth, mobility trends, seasonality, events, device behavior, and network changes.
• Revenue and churn protection: Helps operators maintain service continuity in high-demand areas where experience issues can affect retention.
• Operational consistency: Reduces planner-dependent decisions and standardizes forecasting across regions, technologies, and teams.
• Smarter 4G and 5G evolution: Helps CSPs plan capacity, coverage, spectrum, degrow, and material investments with greater confidence,