Automation Workflow for Network Capacity Forecasting and Hotspot Prediction

Reactive capacity firefighting is a costly operational drain, leading to congestion, poor user experience, and emergency capital spend. This custom workflow automates proactive management by ingesting historical traffic, geolocation data, event calendars, and demographic trends into a time-series forecasting model. The system predicts future demand at a cell-sector granularity and identifies emerging hotspots weeks in advance. This shifts planning from a manual, periodic exercise to a continuous, data-driven process, directly reducing the risk of service degradation and the overhead of manual analysis.

Implementation integrates with network data lakes, RAN Intelligent Controllers (RIC), and Self-Organizing Network (SON) systems. The orchestrator, built on frameworks like LangGraph, manages the data pipeline, model inference, and action routing. High-confidence predictions trigger automated traffic steering or parameter tuning via RIC xApps. Scenarios requiring capital expenditure or complex rehoming are routed with a mitigation plan to a human planner via a ServiceNow or Jira ticket. Continuous observability tracks forecast accuracy and KPI impact, triggering model retraining to maintain precision as network and usage patterns evolve.

This workflow automates the complex, manual process of predicting network congestion by orchestrating specialized AI agents. It ingests diverse data streams—historical traffic, event schedules, demographic trends, and live network KPIs—to forecast demand and identify emerging hotspots weeks in advance. The operational upside comes from pre-emptive capacity adjustments, traffic steering, and optimized capital deployment, preventing service degradation and reducing costly emergency engineering interventions. Implementation requires tight integration with network management systems (NMS), planning tools, and data lakes.

The architecture is built for enterprise-grade control. Each agent's predictions undergo consensus scoring; only high-confidence, low-risk actions proceed autonomously. Actions requiring capital expenditure or major reconfiguration are routed through a human-in-the-loop approval gate within the NOC dashboard. The system logs all decisions, model inputs, and outcomes for auditability and continuous retraining. This ensures the workflow improves forecast accuracy over time while maintaining operational governance, ultimately delivering measurable ROI through reduced congestion-related OPEX and deferred CAPEX.

Phase 1 establishes the data foundation, ingesting time-series traffic logs, geospatial network topology, event calendars, and demographic trends into a centralized data lake. We instrument key performance indicators (KPIs) like PRB utilization and user throughput as baseline metrics. This phase delivers immediate visibility into historical patterns and sets the stage for predictive modeling, typically within 6-8 weeks. The focus is on data quality and pipeline reliability, as model accuracy depends entirely on clean, consistent inputs.

Phase 2 deploys the core forecasting and hotspot prediction models, built on gradient boosting or transformer architectures, orchestrated via LangGraph. The system generates daily forecasts and identifies 72-hour risk windows. High-confidence predictions automatically generate work orders in the Network Management System (NMS) or RAN Intelligent Controller (RIC) for capacity adds or traffic steering. Lower-confidence predictions route to a human-in-the-loop dashboard for engineer review. The final phase integrates continuous monitoring, model retraining loops, and business outcome tracking—tying predictions to reduced congestion tickets and deferred capital spend.

Deploying this workflow requires a multi-layered control architecture. A central orchestrator, built on frameworks like LangGraph or Temporal, manages the entire sequence: ingesting raw telemetry and external signals, executing the forecasting model, and routing hotspot predictions. All actions are logged to a tamper-evident audit system, with key decisions—such as approving a capital upgrade or a major traffic-steering policy—requiring human-in-the-loop validation via a dedicated console integrated with ServiceNow or Jira. This ensures financial and operational oversight before any automated change is committed to the network.

A phased rollout is critical for managing complexity and building trust. Phase 1 executes the workflow in 'monitor-only' mode, generating predictions and simulated actions for planner review without live network changes. Phase 2 introduces automated, low-risk actions like generating trouble tickets or minor parameter tweaks via a RAN Intelligent Controller (RIC). The final phase, contingent on proven accuracy and stability, enables high-impact automation such as triggering capacity upgrade work orders in SAP or dynamic spectrum reallocation. Each phase includes defined KPIs for forecast accuracy and operational savings, with rollback procedures clearly documented.

This workflow automates the ingestion and validation of heterogeneous capacity signals—network KPIs, event calendars, demographic feeds—before they feed predictive models. It applies data-quality scoring to flag missing or anomalous inputs, triggering automated enrichment from secondary sources or pausing the forecast cycle. This gate ensures models operate on clean, contextualized data, preventing garbage-in, garbage-out scenarios that lead to misguided capacity upgrades or missed congestion events, directly protecting capital efficiency.

Low-confidence predictions—often from novel event patterns or sparse data—are automatically routed to a human review queue in tools like ServiceNow or Jira. Approved forecasts proceed to trigger API calls to orchestration systems like a RAN Intelligent Controller for traffic steering or to procurement systems for capacity orders. This closed-loop design, with mandatory checkpoints, ensures operational safety while maximizing automation throughput, turning network planning from a monthly manual exercise into a continuous, governed process.