Predictive AI That Forecasts Congestion and Dynamically Steers 5G Traffic: Before It Impacts Users
Intelligent Predictive Traffic Steering rApp
The Predictive Traffic Steering rApp shifts network management from reactive to proactive. By deep-diving into cell-specific signaling and resource KPIs, the rApp forecasts traffic surges and congestion hotspots minutes before they manifest. It doesn't just observe, it generates dynamic, high-precision policies that redistribute eMBB load, ensuring the network is always one step ahead of user demand.
Forecasting cell congestion with minute-level precision using temporal AI, giving the network a "Look-Ahead" window to prepare for surges before they impact users.
Automated creation of cell-specific steering policies that direct the exact UEs causing congestion to adjacent cells.
Cross-analyzing RRC attempts, PRB utilization, and SINR together to identify the root cause of load, not just the symptom.
Continuous "Analyze → Predict → Act" cycle running within the Non-RT RIC, no human intervention required.
"From reactive monitoring to proactive intelligence: the network, always one step ahead of user demand."
– Traffic Steering rAppPredictive intelligence and dynamic steering for eMBB traffic
Advanced temporal AI predicts cell-specific KPI trajectories using RRC attempts and PRB growth patterns.
Targets the source of congestion: offloads high-demand Edge UEs with low spectral efficiency to adjacent cells with superior link quality.
A Deep Reinforcement Learning agent trained on diverse, high-integrity datasets, learning non-linear KPI relationships to generate reliable steering actions.
Identifies the specific UEs rendering congestion and generates per-UE redirection policies, preserving primary cell integrity.
Forecasts surges minutes before they manifest: the network prepares instead of reacts.
DRL decisions maximize spectral efficiency across the cell cluster, not just a single cell in isolation.
Runs inside the Non-RT RIC as a true closed-loop rApp, O-RAN compliant by design.
Prediction, targeting, and autonomous decision-making, working as one
Beyond simple averaging, the rApp uses advanced AI to predict the trajectory of cell-specific KPIs. By identifying patterns in RRC attempts and PRB growth, the system provides a "Look-Ahead" window, allowing the network to prepare for eMBB surges before they impact user experience.
Rather than broad UE management, the rApp targets the source of congestion. It identifies specific traffic profiles, such as high-demand Edge UEs with low spectral efficiency, and generates targeted policies to offload these high-cost users to adjacent cells with superior link quality, preserving the primary cell's integrity.
The heart of the system is a robust Deep Reinforcement Learning (DRL) agent. Trained on a diverse, high-integrity dataset, the DRL agent learns the complex non-linear relationships between 5G KPIs. It acts as the decision engine, consistently generating reliable steering actions that maximize cluster-wide spectral efficiency without human intervention.
Continuous Analyze → Predict → Act cycle, deployed natively within the Non-RT RIC. Each action feeds observed outcomes back into the DRL agent, so the policy engine improves continuously with real network conditions.
Move from reactive network management to proactive, AI-driven traffic steering, with dynamic policies that keep your RAN one step ahead of every surge.