RAN Intelligent Controller Platform

The platform serves as the unified termination point for the three critical O-RAN interfaces, enabling a cohesive data fabric for optimization logic.

• A1 Interface: Receives policy guidance and enrichment information from the Non-RT RIC to steer near-real-time control loops.
• E2 Interface: Connects directly to O-CU and O-DU network functions for near-real-time control (10ms–1s latency) and telemetry collection.
• O1 Interface: Integrates with the SMO framework for FCAPS management—fault, configuration, accounting, performance, and security monitoring of RAN elements.

A centralized or distributed database that stores near-real-time RAN state data, UE context, and topology information. The R-NIB serves as the shared situational awareness layer for all hosted xApps and rApps.

• Topology Graph: Maintains neighbor relations, cell configurations, and physical network topology.
• UE Context Store: Tracks active user equipment sessions, mobility states, and bearer information.
• Performance Metrics: Aggregates real-time KPIs including throughput, latency, and radio link failure rates.
• Conflict Detection: Enables cross-xApp coordination by providing a single source of truth for network state before control commands are issued.

The RIC platform is built on containerized, Kubernetes-orchestrated infrastructure, enabling elastic scaling and continuous delivery of optimization functions.

• xApp/rApp Hosting: Each optimization application runs as an independent microservice with its own lifecycle, resource allocation, and failure domain.
• Service Mesh Integration: Provides service discovery, load balancing, and secure east-west communication between platform components.
• Horizontal Scaling: Automatically scales data collection pipelines and inference services based on network load and connected cell count.
• Rolling Updates: Supports canary deployments and A/B testing of new AI models without disrupting active control loops.

A high-throughput telemetry pipeline that aggregates performance measurements from distributed RAN nodes and distributes filtered, real-time data streams to registered consumers.

• Stream Processing: Uses message bus architectures (e.g., Kafka) to handle millions of measurement reports per second from thousands of cells.
• Subscription-Based Filtering: xApps register for specific E2 service model data, receiving only relevant KPIs to minimize processing overhead.
• Temporal Windowing: Provides sliding-window aggregations for AI model inference, enabling both instantaneous and trend-based decision making.
• Data Lake Integration: Archives historical telemetry to object storage for offline model training in the Non-RT RIC.

A coordination mechanism that detects and resolves contradictory control commands issued by multiple concurrently running xApps before they reach RAN nodes, preventing network instability.

• Policy Arbitration: Evaluates proposed actions against operator-defined priority rules and resource budgets.
• Joint Optimization: Combines compatible requests from multiple xApps (e.g., load balancing and energy saving) into a single coherent configuration delta.
• Rollback Safety: Maintains a history of applied configurations to enable automatic reversion if a coordinated action degrades KPIs.
• Conflict Logging: Provides auditable records of all detected conflicts and resolution decisions for post-hoc analysis.

The automated pipeline within the SMO and Non-RT RIC that manages the complete lifecycle of AI models, from data ingestion through training, validation, deployment, and production monitoring.

• Model Registry: Versions all trained models with metadata on training data, hyperparameters, and validation accuracy.
• Inference Deployment: Pushes optimized model artifacts to Near-RT RIC inference hosts with hardware acceleration support.
• Model Drift Detection: Continuously compares live inference accuracy against validation baselines, triggering retraining when degradation exceeds thresholds.
• A/B Testing Framework: Enables side-by-side evaluation of candidate models against production models using live traffic shadowing.