Traffic Steering Policy

Policies are defined by high-level business objectives rather than low-level radio parameters. An operator specifies an intent such as 'maximize energy efficiency while maintaining a minimum user throughput of 10 Mbps,' and the AI inference engine translates this into dynamic handover thresholds, Cell Individual Offsets (CIO), and frequency priorities in real time. This abstraction eliminates manual per-cell tuning and ensures the network continuously adapts to meet the declared goal.

A single policy simultaneously balances competing Key Performance Indicators (KPIs) using a weighted reward function. The engine evaluates trade-offs between:

• Load balancing index: Equalizing PRB utilization across cells
• Spectral efficiency: Maximizing bits per second per Hertz
• Handover success rate: Minimizing radio link failures during transitions
• Energy consumption: Reducing power amplifier output during low load The policy defines the Pareto-optimal frontier for these objectives, allowing the network to make context-aware sacrifices when necessary.

Unlike static RSSI-based handovers, AI-driven policies incorporate rich contextual features to decide when and where to steer traffic. The inference engine ingests:

• Predicted cell load from time-series forecasting models
• UE mobility state: stationary, pedestrian, or vehicular
• QoS Class Identifier (QCI) of active bearers
• Channel Quality Indicator (CQI) reports
• Beam-level metrics in massive MIMO deployments This prevents unnecessary handovers for stationary users and prioritizes steering for high-mobility UEs approaching congested cells.

The policy executes as a continuous control loop within the Near-Real-Time RAN Intelligent Controller (Near-RT RIC). An xApp subscribes to E2 node KPIs on a 10ms to 1s interval, runs the inference model, and enforces steering decisions via the E2 interface. The loop includes:

• Observation: Ingest real-time telemetry and UE measurements
• Orientation: Compare current state against policy intent
• Decision: Compute optimal traffic distribution
• Action: Adjust CIO, reselection priorities, or trigger directed handovers
• Evaluation: Monitor outcome and feed back into the model

Policies define hierarchical rules for inter-RAT and inter-frequency mobility. A typical policy stack includes:

• Coverage layer: Anchor UEs on sub-1 GHz FDD for reliability
• Capacity layer: Steer high-throughput UEs to mid-band TDD (n78/n79)
• Offload triggers: Move UEs to unlicensed spectrum (NR-U) or Wi-Fi when QoS permits
• Energy-saving state: Migrate all traffic away from a cell scheduled for deep sleep The AI engine respects service continuity constraints, ensuring voice calls on 5G remain on gNB until EPS fallback conditions are met.

When multiple policies overlap—for example, an energy-saving policy and a QoS-assurance policy—the system employs a priority-based arbitration mechanism. Each policy carries a precedence value and a scope definition (cell-level, slice-level, or UE-group-level). The Near-RT RIC resolves conflicts by:

• Applying the highest-precedence policy for overlapping scopes
• Merging non-conflicting steering actions where possible
• Logging overridden actions for auditability This ensures deterministic behavior even when multiple xApps are operating concurrently on the same RIC platform.

A policy framework that moves beyond simple load metrics to consider the specific requirements of each data flow. A QoS-aware steering policy differentiates between a latency-sensitive URLLC slice and a best-effort eMBB flow, ensuring that load balancing actions do not violate Service Level Agreements (SLAs).

• Maps 5QI (5G QoS Identifier) values to steering decisions
• Prioritizes GBR (Guaranteed Bit Rate) flows during congestion
• Prevents handover of critical flows to cells with insufficient resources

The low-level actuation mechanism for a traffic steering policy. The policy's high-level decision to shift load translates into specific adjustments to Radio Resource Control (RRC) parameters. This includes tuning the A3 event offset, which triggers a handover when a neighbor cell's signal strength exceeds the serving cell's by a defined margin.

• Cell Individual Offset (CIO): biases handover toward or away from a specific neighbor
• Hysteresis: prevents ping-pong handovers by adding a margin to the triggering condition
• Time-to-Trigger (TTT): defines how long the condition must persist before execution

A granular evolution of traffic steering enabled by massive MIMO in 5G NR. Instead of shifting traffic between cells, the policy directs users to different beams within the same cell. This allows for spatial load balancing, moving a user from a congested beam center to a less loaded beam edge without a full handover.

• Uses Synchronization Signal Block (SSB) beam measurements
• Adjusts beam-specific power offsets and coverage angles
• Requires sub-second policy execution loops in the Near-RT RIC