xApp

xApps are designed as discrete, containerized microservices that can be independently developed, deployed, and scaled. This architecture enables:

• Vendor-agnostic development using any programming language or AI framework
• Independent lifecycle management with isolated upgrade and rollback paths
• Horizontal scaling of specific functions without affecting the entire RIC platform
• Resource isolation preventing a faulty xApp from crashing the controller Each xApp runs in its own container with dedicated CPU, memory, and network resources, communicating exclusively through the RIC's shared data layer and E2 interface termination.

xApps consume real-time RAN data exclusively through the standardized E2 interface, which connects the Near-RT RIC to O-CU and O-DU network functions. Key data consumption patterns include:

• E2 REPORT: Subscribing to periodic or event-triggered KPI measurements from RAN nodes
• E2 INSERT: Receiving unsolicited notifications about critical network events
• E2 CONTROL: Issuing commands to modify RAN behavior such as handover thresholds or scheduling weights
• E2 SERVICE UPDATE: Discovering the capabilities and exposed services of connected RAN nodes The E2 interface abstracts vendor-specific implementations behind a standardized API, enabling xApps to operate across multi-vendor RAN deployments.

xApps execute control logic within a strict latency budget of 10 milliseconds to 1 second, distinguishing them from the slower, policy-oriented rApps in the Non-RT RIC. This near-real-time constraint enables:

• Per-TTI optimization: Adjusting scheduling decisions at the transmission time interval granularity
• Rapid anomaly response: Detecting and mitigating cell degradation within sub-second windows
• Dynamic beam management: Reconfiguring massive MIMO beam patterns based on instantaneous UE distribution
• Fast load balancing: Redistributing traffic across carriers before user QoE degrades The latency requirement demands optimized inference pipelines, often using lightweight models or hardware acceleration for AI/ML workloads.

Multiple xApps running concurrently can issue contradictory control commands targeting the same RAN parameters. The RIC platform provides a conflict mitigation framework that:

• Detects overlapping control targets by analyzing the scope of each xApp's E2 CONTROL messages
• Resolves conflicts using operator-defined priority policies or AI-driven arbitration
• Prevents network instability by blocking or modifying commands that would cause oscillatory behavior
• Logs all resolutions for auditability and policy refinement For example, a Mobility Robustness Optimization xApp and a Load Balancing xApp might both attempt to modify the same handover offset. The conflict mitigator ensures only one coherent adjustment is applied.

xApps read and write state information through the RAN Network Information Base (R-NIB), a shared data layer that provides:

• UE context information: Including measurement reports, serving cell, and bearer configurations
• Cell-level KPIs: PRB utilization, active users, throughput, and latency metrics
• Topology data: Neighbor relations, gNB configurations, and antenna parameters
• Enrichment data: Policy guidance and AI model inferences from the Non-RT RIC via the A1 interface This shared context eliminates data silos between xApps and enables coordinated optimization strategies. All access is governed by a publish-subscribe model with fine-grained authorization controls.

xApps serve as the inference hosts for AI/ML models trained in the Non-RT RIC and deployed via the A1 interface. The model lifecycle within an xApp includes:

• Model loading: Retrieving the latest trained model artifact from the SMO model catalog
• Inference execution: Running predictions on streaming E2 data with strict latency guarantees
• Performance monitoring: Reporting inference accuracy and drift metrics back to the Non-RT RIC
• Model rollback: Reverting to a previous version if degradation is detected Common model types hosted in xApps include reinforcement learning policies for scheduling, gradient-boosted trees for anomaly detection, and neural networks for channel prediction.