RAN Intelligence with xApps and rApps

1. Introduction

Radio Access Networks are becoming more software-driven, disaggregated, and automation-centric. To handle the complexity of dense deployments, Massive MIMO, spectrum fragmentation, and diverse traffic profiles, operators are moving toward intelligent control loops in the RAN.This is where O-RAN Alliance’s RAN Intelligent Controller (RIC) architecture—featuring rApps (non-real time, >1 second) and xApps (near-real time, 10 ms–1 s)—comes into play.

These applications allow operators to insert innovation into the RAN without modifying the underlying vendor equipment. They deliver adaptive control, automated optimization, and ML-driven decisions in a multi-vendor environment.

2. RIC Architecture Overview

This diagram illustrates the Open Radio Access Network (O-RAN) architecture with emphasis on the RAN Intelligent Controller (RIC) framework, which is the brain of O-RAN's intelligence and automation capabilities.

2.1 Non-RT RIC (Non-Real-Time RIC)

Operates at timescales of 1 second or longer, focusing on optimization, policy management, and machine learning model training.

Key Components - rApps (Non-RT RIC Applications):

• rApp 1 (Traffic Steering Optimization): Analyzes historical traffic patterns and network performance data to create long-term traffic steering policies

• rApp 2 (QoS Management & Policy): Develops quality of service policies based on service level agreements and network capacity

• rApp 3 (RAN Analytics & ML Training): Performs deep analytics on collected data and trains machine learning models for predictive optimization

Functions:

• Long-term network optimization (hours to days)

• Policy generation and conflict resolution

• ML model training using historical data

• RAN configuration management

• Multi-cell and multi-vendor coordination

2.2 Near-RT RIC (Near-Real-Time RIC)

Operates at timescales between 10 milliseconds to 1 second, enabling rapid control and optimization decisions.

Key Components - xApps (Near-RT RIC Applications):

• xApp 1 (Handover Optimization): Makes real-time decisions on user handovers between cells based on signal quality and load conditions

• xApp 2 (Traffic Steering): Dynamically adjusts traffic routing and load balancing across network resources

• xApp 3 (QoS Control): Controls resource scheduling and quality of service parameters in real-time

Platform Services:

• Conflict Mitigation Engine: Resolves conflicts when multiple xApps try to control the same resources

• Subscription Management: Manages E2 interface subscriptions for telemetry data

• Shared Data Layer (SDL): Provides common data storage for xApps to share information

• Message Router: Routes messages between xApps and RAN components

2.3 Critical Interfaces

A1 Interface (Non-RT RIC ↔ Near-RT RIC):

• Carries policy guidance from Non-RT RIC to Near-RT RIC

• Deploys trained ML models to xApps

• Provides enrichment information (external data like location, traffic forecasts)

• Enables the Non-RT RIC to guide the behavior of xApps without direct control

E2 Interface (Near-RT RIC ↔ O-RAN Components):

• The most critical interface for real-time RAN control

• Uses E2 Service Models (E2SM) to define specific control functions:

  • E2SM-KPM: Key Performance Measurement for monitoring

  • E2SM-RC: RAN Control for parameter adjustments

  • E2SM-NI: Network Interface management

• Enables xApps to:

  • Subscribe to real-time telemetry data

  • Control RAN parameters (handover thresholds, scheduler weights)

  • Receive UE-specific and cell-specific measurements

O1 Interface (SMO ↔ All O-RAN Elements):

• Management plane interface for FCAPS functions

• Configuration management

• Fault detection and recovery

• Performance data collection (feeds into rApps)

• Software lifecycle management

R1 Interface (rApps ↔ Non-RT RIC Framework):

• Data exchange between rApps and the Non-RT RIC platform

• Enables rApps to access network data and publish policies

2.4 Service Management & Orchestration (SMO)

The SMO layer sits above the RICs and provides:

• Overall network orchestration across multiple vendors

• Non-RT RIC Framework hosting for rApps

• Data collection and aggregation from all network elements

• Policy management and distribution

• Lifecycle management of network functions

2.5 O-RAN  gNB split Architecture

O-CU-CP (Centralized Unit - Control Plane):

• Handles RRC (Radio Resource Control) and PDCP control plane functions

• Manages UE connections and mobility

O-CU-UP (Centralized Unit - User Plane):

• Processes user data through SDAP and PDCP user plane

• Can be scaled independently for traffic demands

O-DU (Distributed Unit):

• Processes RLC, MAC, and high-PHY layer functions

• Closer to the radio for reduced latency

O-RU (Radio Unit):

• Performs low-PHY and RF processing

• Connected to O-DU via fronthaul interface

2.6 Interworking Flow - The Intelligence Loop

The diagram shows the complete closed-loop optimization cycle:

1. Data Collection: O-CU-CP, O-CU-UP, and O-DU send performance metrics via O1 to SMO

2. Long-term Analysis: rApps in Non-RT RIC analyze this data (≥1 second timeframe)

3. Policy Generation: rApps create optimization policies and train ML models

4. Policy Deployment: Policies and models flow to Near-RT RIC via A1 interface

5. Real-time Execution: xApps execute control actions via E2 interface (10ms-1s timeframe)

6. Immediate Control: RAN elements respond to xApp commands in <10ms

7. Feedback Loop: New telemetry data flows back through E2 → Near-RT RIC → A1 → Non-RT RIC → O1 → SMO

2.7 Key Benefits of this Architecture

• Multi-vendor interoperability: Open interfaces enable mixing components from different vendors

• Intelligence hierarchy: Separates long-term optimization (rApps) from real-time control (xApps)

• Programmability: xApps and rApps can be developed independently and deployed dynamically

• Closed-loop automation: Continuous optimization without manual intervention

• Scalability: RIC components can be scaled independently based on network needs

3. Real-Time Use Cases for rApps and xApps

3.1 xApps (Near-RT RIC) – Operational Use Cases

1. Mobility Optimization xApp

A Mobility Optimization xApp is a Near-Real-Time RIC application designed to improve the handover (HO) experience by continuously learning UE mobility behaviour and adjusting mobility control parameters on the fly. It operates in the 10 ms – 1 second control loop range and influences RAN decisions through the E2 interface.

How it works

Learns UE mobility patterns

The xApp collects and analyzes real-time RAN data such as:

• RSRP/RSRQ measurements

• PCI reports

• UE speed estimates

• Timing advance (TA) values

• Event A3/A5 triggers

• HO success/failure logs

• RLF (Radio Link Failure) counters

Using these, it forms per-UE or per-cell mobility profiles, identifying:

• fast-moving UEs (train, bus, car)

• slow-moving UEs (pedestrians)

• static users

• cluster-based mobility patterns (commuting zones, highways, building edges)

This classification drives decision-making.

Adjusts handover thresholds dynamically

Based on observed patterns, the xApp updates parameters such as:

Key HO Parameters Modified

• A3 Offset (dB): threshold for “neighbor becomes better than serving cell.”

• TTT (Time-To-Trigger): delay before initiating a handover event.

• CIO (Cell Individual Offset): cell-specific biasing to influence HO direction.

• A5 thresholds: for bad coverage + better neighbor scenarios.

• Hystereses: used to prevent oscillations.

Examples

• Increase A3 offset for fast-moving UEs → earlier handovers = fewer drops

• Increase TTT for slow pedestrians → reduces ping-pong handovers

• Adjust CIO between sectors → load-based mobility balancing

These decisions are applied via E2 control messages in near real time.

Reduces HO failures and ping-pong events

The primary purpose of the xApp is to stabilize handovers without requiring manual optimization.

How It Achieves Fewer Failures

• Predictive handovers for high-speed UEs

• Pre-emptive HO triggers when coverage holes are detected

• Reduction of late HOs by analyzing RLF/RRC re-establishment rates

How It Reduces Ping-Pong

• Context-aware TTT and hysteresis tuning

• UE-specific mobility classes (pedestrian, stationary)

• Power-based or geography-based clustering

Technical Workflow

1. Real-Time Data Collection (via E2SM-KPM & E2SM-RC)

The xApp subscribes to KPIs like:

• HO attempts/success/failure

• UE measurement reports

• Serving/neighbor RSRP deltas

• TA values → UE speed estimation

• UE mobility class indicators

2. Analytics & Classification

UEs are tagged as:

• Fast-moving (>60 km/h)

• Medium (5–60 km/h)

• Slow/static (<5 km/h)

Cell-level clustering helps detect hotspots, commuting zones, or problematic cell borders.

3. Decision Engine

An ML model or rule-based engine decides:

• When to modify HO parameters

• How much to tweak (offset + step size)

• Whether to apply UE-specific or cell-specific changes

4. Near-Real-Time Action

Updates are sent using E2SM-RC (RAN control) messages to:

• adjust CIO

• update A3/A5 thresholds

• tune TTT

• change HO control states

These changes happen on the scale of milliseconds to seconds.

Real-World Example

Urban High-Speed Mobility (Buses/Trams)

Observation:UEs moving along a major arterial road see rising RLFs due to late handovers.

How the xApp fixes it:

• Detects high-speed UE cluster → increases A3 offset by +3 dB

• Reduces TTT for UEs with high TA drift

• Pre-triggers HO before the UE hits the cell-edge dead zone

Result:

• HO failures reduced by 30–40%

• Interruption time improved for VoNR users

• Smoother mobility KPIs across the cluster

Benefits

• NRT granular control loops → rapid response to mobility changes

• Reduced manual drive-testing and parameter audits

• Adaptive behavior for multi-vendor, multi-band RANs

• Direct improvement in user QoE during mobility

2. Massive MIMO Beam Optimization xApp

A Massive MIMO Beam Optimization xApp is a near-real-time RIC application that dynamically adjusts beamforming parameters for 5G NR gNBs equipped with large antenna arrays (32T32R, 64T64R, 128T128R, etc.).This xApp operates on a 10 ms – 1s control loop and is designed to maximize spectral efficiency and coverage by intelligently shaping beams in response to real-time user distribution, interference conditions, and traffic patterns.

• Continuously evaluates beam patterns

The xApp subscribes to real-time measurement data through E2SM-KPM and observation models such as:

Inputs Monitored

• Beam-level CSI-RSRP / SSB-RSRP

• PRB utilization per beam

• SINR distributions

• UE location clusters (via angle-of-arrival or SRS reciprocity)

• Interference per beam index (ICI/ICI maps)

• Beam failure events / beam recovery attempts

• CSI reports (Type I/II codebook)

• Spatial covariance matrices from UL SRS

These metrics allow the xApp to reconstruct a real-time spatial heatmap of where active UEs are located and how beams are performing in each direction.

• Optimizes tilt, azimuth, and power

Beamforming Parameters Controlled

The xApp uses E2SM-RC (RAN Control) messages to adjust:

1. Electrical Tilt

• Vertical tilt adjustments change the elevation focus

• Useful for targeting users in stands, upper floors, or at ground level

2. Azimuth

• Horizontal steering to align beams with directional traffic flows

• Helpful in venues, streets, or stadium sectors

3. Per-beam Power Allocation

• Dynamic downlink power shaping

• Boosting cell-edge beams

• Reducing interference toward specific directions

4. Beamwidth and Shape

• Narrowing beams for high-density user clusters

• Widening beams when users are sparse

5. SRS-based Uplink Beam Selection

• Uses uplink SRS to refine downlink beam decisions via reciprocity

• Improves cell-edge throughput

Massive MIMO beams can sometimes leave coverage dips at sector boundaries, especially in dynamic environments. The xApp helps solve this by:

Enhancements Achieved

• Lifting SINR at the cell edge by optimizing beam tilt/power

• Reducing overshooting and unwanted interference

• Improving the probability of selecting an optimal beam

• Increasing scheduling fairness for edge UEs

Observed performance gains

Field data from optimization trials typically shows:

• 15–30% improvement in cell-edge throughput

• 10–20% reduction in dropped calls in congested areas

• Smoother user experience for XR/VoNR at edges

How It Works (Technical Flow)

1. Real-Time Measurement Collection

The xApp receives:

• Beam-level PRB usage every 100 ms

• UE beam association maps

• SRS-based angular domain reports

• Interference heatmaps

2. Spatial Analysis Engine

Performs:

• K-means or density-based clustering (DBSCAN) to find user clusters

• Angular spread + elevation angle estimation

• Identification of invalid/poor-performing beams

3. Decision Engine

Applies ML or rule-based logic:

• If crowd cluster shifts → steer beams accordingly

• If cell-edge SINR dips → increase power/tilt toward edge

• If interference rises → rotate/shape beams away from conflict zones

4. E2 Actions Sent to gNB

E2SM-RC commands such as:

• RAN parameter update for beam index i

• Per-beam power scaling updates

• Elevation/azimuth steering adjustments

5. Closed-Loop Monitoring

xApp continuously verifies if KPI improvements occur:

• BLER drops

• SINR rises

• Throughput increases

• Fewer beam failure recovery events

If not → roll back the changes automatically.

Example: Stadium or Event Venue

Scenario

A stadium hosts a concert with rapidly changing UE density.Different stands fill at different times, and user clusters move as crowds walk.

How the xApp Responds

1. Every ~100 ms, the xApp receives updated CSI/SINR/RSRP/traffic maps.

2. Detects that a large user cluster formed in the north-east stand.

3. xApp increases electrical tilt + rotates azimuth 10° toward the new cluster.

4. Power towards other beams is adjusted downward to reduce interference.

5. Additional narrow beams are formed to target high-density groups.

6. As crowds shift toward the stage during performance → beams auto-redirect.

Outcome

• Consistent throughput even under heavy load

• Improved experience for XR, live streaming, VoNR calls

• Stable KPIs despite massive user movement

3. Traffic Steering xApp

A Traffic Steering xApp is a Near-RT RIC intelligence module that dynamically shifts users across cells, sectors, and frequency layers based on real-time load conditions, radio quality, and device capabilities.Its primary goal is to prevent congestion, maximize multi-band utilization, and ensure consistent user experience, especially in high-density or fluctuating traffic environments.

• Balances load across cells and carriers

This xApp continuously monitors and evaluates:

Real-time RAN metrics

• PRB utilization (per cell / per carrier)

• RSRP & SINR distributions

• UE capabilities (band support, EN-DC, CA combinations)

• CQI / MCS distributions

• Buffer occupancy

• Active user count per layer

Using these inputs, the xApp detects imbalance such as:

• One cell/carrier heavily loaded

• Neighboring sectors underutilized

• mmWave or high-band carriers lightly loaded

• Overloaded mid-band MAC schedulers

Steering techniques used

• CIO tuning: biases UE reselection or measurement event thresholds

• A3 offset optimization: promotes HO toward less-loaded neighbor cells

• Layer prioritization: adjusts inter-frequency thresholds

• Dual connectivity placement: moves UEs between LTE anchor / NR secondary cells

• Cell reselection priorities (idle mode): to spread users across carriers

The result is better RRM (Radio Resource Management) distribution across the RAN.

• Reduces congestion in hotspots

High-traffic areas—train stations, offices, stadium surroundings—experience sudden load spikes. The xApp reacts by:

Real-time Congestion Detection

• PRB usage > 85%

• Scheduler queuing delays rising

• DL/UL throughput collapsing

• High BLER / MCS drop

• Disproportionate UE density in a beam/sector

Congestion Mitigation Actions

• Offload mid-band UEs to underutilized low-band or mmWave

• Redirect UEs to neighbor cells with available PRBs

• Expand or shrink cell boundaries via CIO/tilt changes

• Preferentially steer high-throughput-capable UEs to high-capacity layers

This ensures hotspots do not drag down overall sector performance.

• Coordinates between sub-6 GHz and mmWave layers

The xApp plays a critical role in multi-band integration, especially where mmWave coverage is patchy but extremely high in capacity.

Key coordination elements

• Identifies UEs capable of FR2/mmWave

• Measures mmWave beam quality (SS-RSRP, SNR, PDCCH decoding)

• Tracks fallback conditions (mmWave blockage, NLOS events)

• Estimates dwell time based on mobility patterns

When to steer to mmWave

• UE stationary or slow-moving

• High QoS service required (live video, XR)

• mmWave load < 40%

• Good beam condition (SNR margin > X dB)

When to avoid mmWave

• High mobility (cars, trains)

• Severe blockage predicted

• UE only briefly in mmWave coverage

This coordination ensures mmWave is used efficiently—maximizing capacity without unnecessary ping-ponging between layers.

Real Example: Offloading UEs from Overloaded Mid-Band to High-Capacity mmWave

Scenario

A dense shopping district sees heavy mid-band (3.5 GHz) congestion during evening hours.

• PRB utilization > 90%

• Throughput collapse for edge users

• mmWave nodes nearby have < 20% load

How the Traffic Steering xApp Works

1. Detects congestion in mid-band sectors (via PRB, BLER, MAC backlog).

2. Identifies UEs with mmWave-capable modems (NSA/SA).

3. Evaluates mmWave availability:

   • Beam SNR

   • LOS/NLOS probability

   • Blockage predictions

4. Applies A3 offset bias toward mmWave, raising inter-frequency neighbor priority.

5. UEs enter mmWave layer → high-capacity, wide-channel performance.

6. Mid-band load drops → scheduler regains stability.

7. xApp ensures fast fallback to mid-band when LOS is lost.

Observed Benefits

• Mid-band PRB load reduction by 20–40%

• Cell-edge throughput increased by 15–25%

• High-end users see 2–5× speed boost

• More stable VoNR and XR performance

4. RAN Energy Savings xApp

A RAN Energy Savings xApp is a Near-RT RIC application designed to reduce power consumption in the RAN while preserving coverage, mobility performance, and QoE.With Massive MIMO deployments, multi-band layering, and dense small-cell grids, energy costs have become a major OPEX driver. This xApp enables dynamic power management at millisecond-to-second granularity by intelligently activating or deactivating RAN resources based on real-time and predicted demand.

• Predicts low-traffic periods

The xApp continuously monitors and analyzes:

KPIs used to detect low-load

• PRB utilization (DL/UL)

• Active UE count per cell / layer

• Traffic volume (Mbps per sector)

• Paging requests

• Idle-to-active transition rates

• RRC CONNECTED/IDLE ratios

• Beam-level utilization (Massive MIMO)

Prediction engine

Although rApps typically handle long-term traffic forecasting, the xApp uses short-horizon prediction, such as:

• Time-of-day patterns

• UE arrival/departure rates

• Sudden drops in UL/DL throughput

• Short-term load regression models

This allows the xApp to preemptively reduce energy consumption before the network becomes idle.

• Shuts down carriers, MIMO layers, or small cells without degrading KPIs

Once low-traffic periods are detected, the xApp selectively deactivates portions of the RAN that are not required to maintain QoS and coverage.

Typical actions executed using E2SM-RC

1. Carrier Shutdown / Sleep

• Deactivates secondary carriers (e.g., NR 3.5 GHz, LTE CA layers)

• Retains anchor carriers to maintain coverage

• Reduces PA (power amplifier) use significantly

2. Massive MIMO Layer Muting

• Turns off unused antenna ports or MIMO layers

• Reduces power consumption in large array radios

• Useful during late-night hours in business districts

3. Small Cell Sleep / Standby Mode

• Switches small cells to low-power states

• Keeps macro cell(s) active for coverage

• Reawakens them when traffic returns

4. Beam Muting / Narrowing

• Reduces the number of active beams

• Saves RF chain and DSP power

Ensuring no degradation of KPIs

The xApp continually checks:

• RSRP/RSRQ thresholds

• coverage overlap

• mobility event frequency

• idle mode behavior

If KPIs dip below operator-defined limits → xApp immediately reverses the action.

• Wakes them up intelligently

Efficient wake-up is crucial to avoid:

• access failures

• RACH congestion

• slow coverage restoration

Wake-up triggers

• Rising PRB utilization

• Increasing RRC connection attempts

• Paging bursts

• UE movement detected through neighbor reports

• Event A1/A2 transitions

• Small-cell discovery SSB bursts from UEs

Fast wake-up mechanics

• xApp sends an E2SM-RC activation command

• Node transitions from Sleep → Warm → Active

• Scheduler and HARQ buffers resume

• Carrier becomes visible for UE measurements

For Massive MIMO:

• additional layers re-activate in parallel

• digital beamforming recalibrates on the fly

• previous spatial covariance maps restored

This ensures the cell/carrier reactivates seamlessly.

Real Example: Shopping Mall Small Cells Automatically Sleep at Night

Scenario

A large shopping mall is equipped with multiple NR small cells.During the day:

• high UE density

• heavy mid-band traffic

• need full MIMO layers

At night:

• building closed

• only security staff and occasional machine traffic remain

• traffic drops by >95%

xApp Behavior

1. Around 10 PM, xApp observes:

   • PRB utilization < 5%

   • ~0–3 active UEs

   • Idle transitions dropping

2. xApp checks coverage overlap:

   • Nearby macro cells provide sufficient baseline coverage

3. xApp places mall small cells into Sleep Mode.

4. Throughout the night, xApp monitors:

   • paging spikes

   • security camera data uplink bursts

   • UL noise floor

5. If activity increases (e.g., cleaning crew arrives):

   • xApp reactivates one small cell

   • additional cells wake up only if PRB utilization crosses thresholds

6. System returns to full active state before mall opening (~8 AM).

Outcomes

• Energy savings of 30–50% for small-cell cluster

• No coverage gaps thanks to macro-layer fallback

• Improved OPEX efficiency across daily cycles

Technical Workflow of RAN Energy Savings xApp

1. Real-time telemetry ingestion

xApp receives per-interval measurements:

• PRBs

• active UEs

• power consumption reports

• SINR maps

• beam utilization

2. Analysis engine

Evaluates:

• traffic decay rates

• coverage redundancy

• mobility risk

• neighbor cell support

• mmWave/FR1 fallback scenarios

3. Decision logic

Uses operator-configurable policies:

• minimum carrier availability

• allowed offloading thresholds

• coverage guarantees

• latency requirements

4. Execution

Sends control instructions to O-DU/O-CU using:

• E2SM-RC for RAN control

• beam/carrier/MIMO layer updates

5. Closed-loop feedback

Monitors KPIs and rolls back actions if needed.

Why Energy Savings xApps Matter

✔ Major OPEX reduction (one of the top operator cost drivers)✔ Enables greener networks, supporting sustainability goals✔ Dynamic, demand-driven resource allocation✔ Works across multi-vendor O-RAN deployments✔ Avoids performance degradation through closed-loop control

3.2 rApps (Non-RT RIC) – Operational Use Cases

The Non-Real-Time RIC (Non-RT RIC) is the analytical and policy-intelligence layer of the O-RAN architecture.

It operates at control loops > 1 second (often minutes to hours), performing heavy analytics, ML model lifecycle management, and long-term optimization.

rApps run inside the SMO (Service Management & Orchestration) framework and impact the RAN by generating policies, configuration updates, and ML models that guide the near-RT RIC and its xApps.

Below are key rApps used in modern intelligent RAN deployments.

1. ML Model Training rApp

• Performs long-term analytics

This rApp ingests a wide variety of performance, configuration, and mobility data, including:

• PM counters (24-hr to weekly logs)

• UE mobility traces

• Beam usage statistics

• Interference maps

• Energy consumption logs

• Traffic distribution profiles

It supports large time-scale learning that xApps cannot perform in real time.

• Trains ML models for mobility, interference, and energy patterns

This rApp performs batch ML training or periodic retraining of models such as:

• Mobility prediction models

• Congestion prediction models

• Traffic classification models

• Beam quality prediction models

• Energy-saving opportunity models

• Anomaly/scoring models

These models help predict how the RAN will behave in the near future.

• Pushes updated models to xApps

Once trained, the rApp distributes updated ML weights, thresholds, or policy parameters to xApps through the A1 interface, such as:

• Updated handover prediction model → Mobility xApp

• New interference map → Interference Management xApp

• Revised energy profiles → Energy Savings xApp

This ensures xApps operate with fresh intelligence.

Example

Nightly retraining of mobility prediction based on 24-hour UE movement data.The rApp processes all mobility logs at midnight, updates the mobility model, and pushes it to the Mobility xApp before the morning traffic peak.

2. RAN Configuration & Policy Automation rApp

• Automates feature activation/deactivation

This rApp applies long-term network policies such as:

• enabling or disabling features (e.g., CA, DSS, multi-TRP, massive MIMO modes)

• adjusting network-wide parameters

• configuring slicing, QoS, or RRM settings

• setting macro-level thresholds

It prevents manual misconfiguration and ensures consistent parameterization across thousands of cells.

• Creates policies for xApps (constraints & objectives)

This rApp does not control the RAN directly; instead, it defines:

• objectives (e.g., maximize cell-edge throughput)

• constraints (e.g., do not exceed defined power limits)

• service targets (latency, throughput, coverage)

• safety boundaries (avoid HO instability, avoid beam oversteering)

These are sent to xApps through the A1 policy framework.

Example

Setting a max allowable RLF (Radio Link Failure) rate for the Mobility xApp.If RLF crosses the threshold, xApp adjusts HO parameters until it returns to acceptable levels.

3. Traffic Forecasting rApp

• Predicts upcoming traffic surges

This rApp uses:

• historical traffic data

• weather data

• location patterns

• event calendars

• holiday/weekend behavior

• ML time-series forecasting (ARIMA, LSTM, Prophet, etc.)

It learns daily/weekly traffic cycles and identifies upcoming spikes.

• Instructs xApps to pre-allocate resources

Before congestion occurs, the rApp proactively tells xApps to:

• boost massive MIMO layers

• pre-activate mmWave sectors

• expand high-capacity carriers

• move sleeping small cells into warm mode

• prepare load-balancing strategies

This avoids reactive congestion collapse.

Example

Predicting crowd buildup for concert events or metro rush hour.If the rApp predicts a 6 PM surge near a transit hub, it instructs the Traffic Steering and Beam Optimization xApps to prepare high-capacity configurations in advance.

4. Anomaly Detection rApp

• Performs pattern analysis on PM counters

The rApp uses long-term RAN metrics such as:

• BLER, RLF, HO failures

• SINR/RSSI distributions

• Beam performance anomalies

• Latency spikes

• PRB imbalance across layers

• Unexpected TDD pattern discrepancies

It compares them against historical baselines or ML anomaly detection models.

• Detects RAN performance outliers

The rApp identifies:

• failing sectors

• faulty antenna ports

• sudden interference sources

• beam misalignment

• scheduling anomalies

• degraded backhaul conditions

• Instructs xApps to correct anomalies

Once an issue is found, the rApp triggers:

• Beam Optimization xApp → fix tilt/beam misalignment

• Traffic Steering xApp → shift load away from problematic cell

• Mobility xApp → update HO thresholds

• Energy xApp → disable problematic MIMO chains

These actions are delivered via A1-based policies to the near-RT RIC.

Example

Identifies a sector with abnormal BLER and triggers the Beam Optimization xApp.The rApp detects unusually high BLER during off-peak hours → suspects beam mis-steering → instructs xApp to re-optimize beams → SINR improves.

4. Data Flow Between rApps and xApps

Step-by-step flow:

1. rApp analyzes historical KPIs (hourly/daily trends).

   1. rApp pulls PM/KPI datasets from SMO data stores ( O1 / NWDAF / telemetry DBs ).

   2. Typical inputs: hourly PRB usage, HO attempts, RLF counts, SSB/CSI reports, beam stats, UE counts, per-cell energy logs.

   3. rApp runs batch analytics or retrains ML models (e.g., LSTM/Prophet for time-series, classification for mobility classes).

2. rApp creates a policy (e.g., HO threshold rules)

   1. Objective: human-readable goal (e.g., “reduce HO failure rate < 0.5%”).

   2. Constraints: safety bounds (max A3 offset = +6 dB, min TTT = 40 ms).

   3. Scope: target cells/clusters, UE classes, time window.

   4. Parameters: exact parameter values/changes or ML model handle.

   5. Priority & TTL: priority level and time-to-live for the policy.

   6. Model reference: pointer to model version (if applicable).

3. rApp sends policy → Non-RT RIC → A1 interface → Near-RT RIC.

   1. rApp registers / authenticates with Non-RT RIC / SMO.

   2. Non-RT RIC uses the A1 interface to push the policy to the Near-RT RIC. The A1 exchange can be:

   3. PolicyUpdate (full parameter set) or

   4. ModelUpdate (model weights or model handle + endpoint).

   5. A1 supports policy types: decision policies, optimization objectives, model descriptors, and guardrails.

4. xApp receives the policy, then applies real-time decisions via E2 interface.

xApp behavior on receipt

• Validate policy (syntax, schema, signature, TTL).

• Apply policy to its decision engine — either:

  • Direct param injection (xApp converts policy to E2 actions), or

  • Model swap (xApp loads new model weights and starts inference).

E2 actions

• xApp uses E2SM-RC (E2 Service Model — RAN Control) or vendor-specific E2SM flavors to send control messages to gNB (O-CU / O-DU):

  • Examples: update A3 offset, set TTT, change beam pointer, alter per-beam power, carrier on/off.

• E2 messages are lightweight and optimized for near-real-time control.

5. gNB adapts parameters (offsets, beams, scheduling).

The O-CU/O-DU (or vendor gNB stack) receives E2 commands.

It validates commands against local constraints and vendor-specific capabilities.

Scheduler/RRM updates internal tables (e.g., adjust A3 offset table, change PRB mapping, update beam table entries, enable/disable carriers).

6. RAN metrics return via E2 feedback.

After actions, gNB sends updated KPIs via:

• E2 KPM/MP reports back to Near-RT RIC (subscribed measurement frequency).

• O1 telemetry flows to SMO/Non-RT RIC for long-term logging.

7. rApp continually updates ML models and policies.

Continuous learning

• rApp ingests post-action telemetry (from O1 and NWDAF).

• Evaluates model performance (A/B test, online metrics).

• If drift detected or KPI goals unmet, retrain or adjust hyperparameters.

Policy lifecycle

• Policies can be automatically adjusted (auto-tuning) or require human operator approval based on governance settings.

• Non-RT RIC maintains policy history, rollbacks, and audit trails.

5. Deployment in Real Field Environments

Deploying xApps/rApps in real networks involves multiple steps. Here's how operators typically do it:

Step 1 — Environment Preparation

• Kubernetes cluster for RIC platforms (Near-RT and Non-RT RIC)

• SMO platform provisioning

• Integration of O-DU/O-CU with E2 agent

• Network slicing, telemetry, and monitoring setup

Common tools:

• ONAP, O-RAN OSC, VMware Telco Cloud, Nokia Digital Operations Center

Step 2 — Onboarding xApps and rApps

Applications are packaged as containers (Helm charts / Docker images).

Procedure:

1. Operator uploads application package to SMO

2. SMO validates interfaces (A1/E2SM compatibility)

3. rApp/xApp lifecycle management registered

4. App deployed via K8s container orchestration

 Step 3 — Integration with RAN

• Configure A1 (policies, ML models)

• Configure E2 (control messages to RAN nodes)

• Validate compatibility with vendor O-DUs and O-CUs

Challenges:

• Vendor-specific E2SM profiles

• Different scheduler behaviors

• Latency constraints for closed-loop control

Step 4 — Field Testing & KPI Validation

KPIs commonly measured:

• Handover success rate

• Throughput (DL/UL)

• Cell-edge performance

• Interference levels

• Energy consumption

• RLF/RRC drop rate

Operators typically do a 3-stage rollout:

1. Lab testbed

2. Field trial (limited area)

3. Commercial deployment

Step 5 — Continuous Optimization

• rApps retrain ML models periodically

• xApps update real-time decisions based on new policies

• Operators feed data into analytics dashboards (Grafana, Prometheus)

6. Example Real Deployment Scenario

Scenario: Mobility Optimization in Dense Urban Cluster

1. Data Collection:

   UE traces show high HO failures on a set of streets.

2. rApp Training:

   rApp learns optimal HO offsets based on:

   • UE speeds

   • Time-of-day patterns

   • RSRP/RSRQ/beam reports

3. Policy Creation:

   rApp generates mobility policy such as:

   “For UEs traveling > 40 km/h, expand A3 offset by +1 dB between 6–9 PM.”

4. Policy Transfer:

   Sent to Near-RT RIC via A1.

5. Near-RT xApp Execution:

   xApp applies rules in near real-time each time UE reports measurement events.

   
   

   Outcome:

   • HO failures drop by 32%

   • Ping-pong events reduce by 25%

   • User-perceived throughput improves

This chain shows the intended synergy between rApps (policy-level intelligence) and xApps (fast control loops).

7. Summary

RAN Intelligence powered by rApps and xApps is becoming a foundational part of 5G Advanced deployments. These modular applications allow operators to bring ML-driven optimization into the RAN without touching gNB firmware, enabling:

• Predictive mobility

• Dynamic traffic steering

• Smarter beamforming

• Real-time interference mitigation

• Energy efficiency automation

With the rise of disaggregated networks and Open RAN adoption, rApps/xApps will be central to next-generation RAN automation and will also serve as the stepping stone to 6G’s goal of fully autonomous networks.

8. References

1. O‑RAN Alliance — Specifications page (architecture, RIC, interfaces)https://www.o-ran.org/specifications o-ran.org

2. "Data-Driven Design of 3GPP Handover Parameters with Bayesian Optimization and Transfer Learning” (arXiv)https://arxiv.org/abs/2504.02633 arXiv

3. “RIC-O: Efficient placement of a disaggregated and distributed RAN Intelligent Controller…” (arXiv)https://arxiv.org/abs/2301.02760v1

4. “Conditional Handover in 5G: Principles, Future Use Cases and FR2 Performance” (arXiv)https://arxiv.org/abs/2204.01283 

5. “Handover Parameters Optimisation Techniques in 5G” (MDPI Sensors, 2021)https://www.mdpi.com/1424-8220/21/15/5202

6. “Mobility Performance Analysis of RACH Optimization and Handover in 5G NR” https://arxiv.org/pdf/2309.09840.pdf

7. ETSI TS 103 982 V8.0.0 “O-RAN Architecture Description (O-RAN.WG1.OAD-R003-v08.00)”https://www.etsi.org/deliver/etsi_ts/103900_103999/103982/08.00.00_60/ts_103982v080000p.pdf

8. AWS “Open RAN Architecture on AWS – O-RAN Evolution” Whitepaperhttps://docs.aws.amazon.com/whitepapers/latest/open-radio-access-network-architecture-on-aws/o-ran-evolution.html