5G networks generate an enormous amount of data every second.
Think about what is happening inside a mobile network:
- Thousands of UEs are connected to cells.
- Traffic changes continuously.
- Users move from one cell to another.
- Radio conditions change.
- Network resources need to be adjusted.
- Some cells become overloaded while others remain underutilized.
Traditionally, many network optimization tasks depended on predefined rules, configuration parameters, and manual engineering.
But what if the network could learn from its own data and make better decisions automatically?
This is where Artificial Intelligence (AI) and Machine Learning (ML) become important in O-RAN.
O-RAN is designed around open interfaces and intelligent control, with the RAN Intelligent Controller (RIC) playing a key role in enabling AI/ML-based optimization. The O-RAN Alliance describes AI/ML workflows involving both Non-RT RIC and Near-RT RIC.
Let's understand how this actually works.
What Is AI in O-RAN?
AI in O-RAN means using AI and ML techniques to analyze network data, predict network behavior, identify problems, and potentially automate network optimization.
A simple idea is:
Collect Data → Analyze Data → Train Model → Make Prediction → Take Action → Monitor Result
For example, imagine that a cell is becoming congested every evening.
An AI/ML model could learn the traffic pattern and predict when congestion is likely to happen.
The network can then take an appropriate optimization action.
This creates a more intelligent and automated RAN.
Why Does O-RAN Need AI?
Traditional network optimization often depends on fixed rules.
For example:
If traffic exceeds a certain threshold, change parameter X.
This can work, but real networks are much more complicated.
Traffic patterns can change because of:
- Time of day
- Location
- Events
- Weather
- User mobility
- Application behavior
- Network failures
- Radio conditions
AI/ML can identify patterns across multiple network metrics and help make decisions based on historical and real-time information.
The O-RAN architecture provides a framework for this intelligence through components such as the Non-RT RIC, Near-RT RIC, SMO, and AI/ML applications.
Where Does AI Run in O-RAN?
One of the easiest ways to understand AI in O-RAN is to look at the RIC architecture.
There are two important RIC components:
Non-RT RIC
The Non-Real-Time RIC deals with longer-timescale optimization and AI/ML workflows.
It can be used for:
Model training
Policy creation
Network analytics
Long-term optimization
AI/ML model management
The Non-RT RIC can provide policies and guidance to the Near-RT RIC through the A1 interface.
Near-RT RIC
The Near-Real-Time RIC is designed for faster RAN control and optimization.
It can use network information and applications to make decisions at a much shorter timescale.
For example, it could support use cases involving:
- Traffic steering
- Load balancing
- Mobility optimization
- Interference management
- Radio resource optimization
The Near-RT RIC interacts with RAN functions through the E2 interface.
A simple representation is:
Network Data → Non-RT RIC → AI/ML Model → Policy → Near-RT RIC → RAN Action
The result is a closed-loop optimization process.
What Are rApps and xApps?
If you are learning AI in O-RAN, you will frequently come across two terms:
rApp and xApp.
They are applications that run within the O-RAN intelligent architecture.
rApps
rApps are associated with the Non-RT RIC environment.
They can support tasks such as:
- Data analysis
- AI/ML model workflows
- Policy generation
- Long-term optimization
For example, an rApp could analyze several days of traffic data and identify a recurring congestion pattern.
xApps
xApps operate in the Near-RT RIC environment.
They can support faster optimization decisions based on current network conditions.
For example:
A cell is becoming overloaded → xApp detects the situation → traffic is redirected toward another suitable cell.
The exact implementation and timing depend on the use case and deployment architecture, but the basic idea is that rApps support longer-timescale intelligence while xApps support near-real-time control.
How Does an AI/ML Workflow Work?
Let's take a simple example.
Suppose a telecom operator wants to predict cell congestion.
Step 1: Collect Network Data
The network collects information such as:
Number of connected UEs
PRB utilization
Throughput
Latency
Packet loss
Traffic volume
Radio measurements
This data becomes the input for analytics and ML workflows.
Step 2: Prepare the Data
Raw network data isn't always ready for an ML model.
It may need:
Cleaning
Filtering
Normalization
Feature extraction
Labeling, depending on the ML approach
The goal is to create useful training data.
Step 3: Train the Model
The prepared data is used to train an ML model.
For example, the model could learn:
Traffic + PRB Utilization + UE Count + Time → Probability of Congestion
After training, the model can be evaluated before being used operationally.
Step 4: Deploy the Model
Once the model performs well enough, it can be deployed into the appropriate AI/ML workflow.
The model can then receive new network data and produce predictions.
Step 5: Make a Decision
Suppose the model predicts:
High probability of congestion in Cell A within the next 10 minutes.
An optimization application can use that information to determine an appropriate action.
Step 6: Take Action
Depending on the use case, the network could potentially:
- Adjust traffic steering
- Modify certain optimization parameters
- Balance users across cells
- Change resource allocation strategies
Step 7: Monitor the Result
This is an important part of intelligent networking.
The network should not simply make a decision and forget about it.
It needs to check:
Did the action actually improve the network?
This creates a feedback loop.
The O-RAN AI Closed Loop
The complete process can be simplified as:
Data
↓
AI/ML Model
↓
Prediction
↓
Optimization Decision
↓
RAN Action
↓
Network Performance
↓
New Data
↓
AI/ML Model
This is often called a closed-loop optimization approach.
The network continuously observes conditions, makes decisions, applies actions, and evaluates the results.
That is what makes an intelligent network different from a simple rule-based system.
Real-World AI Use Cases in O-RAN
AI/ML can potentially support many RAN optimization problems.
1. Traffic Steering
AI can analyze network conditions and help determine where traffic should be directed.
For example:
Cell A → High Load
Cell B → Available Capacity
An intelligent system could help steer suitable users toward Cell B.
2. Load Balancing
Different cells may experience very different traffic levels.
AI can analyze traffic patterns and help distribute users and resources more efficiently.
3. Energy Saving
Energy consumption is an important concern for mobile networks.
AI can help identify periods of low traffic and support intelligent energy-saving strategies.
O-RAN's recent work includes energy-saving improvements and specifically highlights intelligent capabilities across the SMO, Non-RT RIC, and Near-RT RIC.
4. Anomaly Detection
ML models can learn what normal network behavior looks like.
If network behavior suddenly becomes unusual, the model can flag a potential anomaly.
For example:
Normal behavior → KPI pattern A
Unexpected behavior → KPI pattern B
This can help engineers investigate possible network issues earlier.
5. Mobility Optimization
Users are constantly moving between cells.
AI can analyze mobility patterns and help optimize decisions related to handovers and traffic distribution.
6. Massive MIMO Optimization
AI/ML can also be applied to radio optimization problems.
O-RAN Release 5 specifically includes enhancements intended to support AI/ML-based Massive MIMO beamforming optimization.
A Simple Example
Imagine a stadium hosting a large cricket match.
Before the match:
Normal Traffic
↓
During the match:
Traffic increases rapidly
↓
AI detects the pattern:
High congestion probability
↓
Near-RT optimization application evaluates the network
↓
Traffic/resource optimization is applied
↓
Network monitors the result
↓
If performance improves:
Continue
If performance does not improve:
Re-evaluate and adjust
This is the basic idea behind intelligent RAN optimization.
Instead of relying only on static configurations, the network can use data-driven intelligence to respond to changing conditions.
Is AI in O-RAN Fully Automatic?
Not necessarily.
This is an important point.
AI does not mean that humans are removed from network operations.
Telecom networks are complex and highly critical systems.
AI-generated decisions need appropriate:
- Validation
- Monitoring
- Policies
- Security controls
- Performance evaluation
- Human oversight
There is also growing interest in explainable AI (XAI) because network engineers need to understand why an AI system made a particular decision. Research on explainable AI in O-RAN highlights trust and interpretability as important challenges for intelligent network operations.
What Are the Challenges?
AI in O-RAN sounds exciting, but implementing it isn't simply a matter of adding an ML model.
Several challenges remain.
Data Quality
AI models are only as good as the data used to train them.
Poor-quality or incomplete network data can lead to poor predictions.
Model Performance
A model that works well in a laboratory environment may behave differently in a real network.
Latency
Some RAN decisions need to happen quickly.
The location of AI processing therefore matters.
Model Management
Models need to be trained, tested, deployed, monitored, updated, and sometimes rolled back.
Security
AI/ML introduces additional security considerations, including protection of models, data, interfaces, and AI workflows.
Explainability
Network engineers may need to understand why a model recommended a particular action.
What's Next for AI and O-RAN?
The relationship between AI and RAN is likely to become even more important as networks move toward 5G-Advanced and 6G.
The O-RAN Alliance is already researching AI-native architectures, including how AI can become more deeply integrated into future RAN systems.
This moves the conversation beyond:
"How can AI optimize the RAN?"
toward:
"How can the RAN itself be designed to work with AI?"
That is a much bigger architectural change.
Final Thoughts
AI in O-RAN is not just about putting a machine-learning model somewhere inside a telecom network.
The real concept is a complete intelligence loop:
Collect → Learn → Predict → Decide → Act → Measure → Learn Again
The combination of O-RAN's open architecture, RIC framework, AI/ML workflows, and programmable applications creates a foundation for more automated and intelligent RAN operations.
For telecom engineers, this makes O-RAN particularly interesting because it brings together several technologies:
5G + Cloud + Open Interfaces + AI/ML + Automation
If you're starting with O-RAN, a good learning path is:
O-RAN Architecture → Interfaces → RIC → rApps/xApps → AI/ML Workflow → Real-World Use Cases
For more detailed telecom tutorials, architecture guides, and O-RAN learning resources, explore TechLTEWorld.
What do you think?
Which AI use case will have the biggest impact on future RAN networks — traffic steering, energy saving, anomaly detection, mobility optimization, or Massive MIMO optimization?
Share your thoughts in the comments.
