Optimizing radio access networks with self-healing AI

Get these wrong and you get dropped calls (a call cut off mid-conversation) and congestion (too many users fighting for the same slice of spectrum).

Traditionally, engineers set these parameters manually and adjusted them slowly, over weeks. A stadium event moves too fast for that.

The Self-Optimizing Network Idea

The industry has pushed toward the Self-Organizing Network (SON) for over a decade. SON is a set of functions that let the network configure, optimize, and heal itself with less human input.

Classic SON used fixed rules: "if congestion exceeds X, do Y." That works for predictable patterns. It struggles with novel, fast-changing situations like a sudden crowd surge.

This is where AI, and reinforcement learning in particular, changes the game.

Reinforcement Learning in One Paragraph

Reinforcement learning (RL) is a type of machine learning where an agent learns by trial and error. It takes an action, observes the result, and receives a reward (a numeric score of how good the outcome was). Over many cycles it learns a policy: a strategy that maps situations to the actions most likely to maximize reward. Think of it like a thermostat that not only reacts to temperature but learns your habits and the building's quirks to keep you comfortable at the lowest energy cost.

For the RAN, the "action" is adjusting a parameter. The "reward" is something like fewer dropped calls and higher throughput.

Walkthrough: The Stadium Event

Here is how a self-healing RAN handles the Saturday final. This is a composite of techniques described in vendor and standards documentation, not a claim about one specific operator.

1. The Baseline

Before the event, three cells cover the stadium and its approaches. On a normal day they carry light traffic. Their parameters are tuned for a quiet neighborhood.

2. Demand Surges

Fans arrive. Connected devices in the coverage area jump sharply. Video uploads spike. The cells covering the seating bowl start to congest.

An old rule-based system might simply cap data rates. That protects call quality but frustrates users.

3. The RL Agent Acts

The RL agent runs in the RAN Intelligent Controller (RIC), a software component defined by the O-RAN Alliance, an industry group standardizing open, programmable RANs. You can read about the architecture at the O-RAN Alliance site.

The agent observes the state every few seconds: load per cell, dropped-call rate, signal quality, handover failures. Then it acts:

• Adjusts antenna tilt on a nearby macro cell to redirect coverage toward the concourse, offloading the packed bowl cells.
• Shifts handover thresholds so phones at the edge of a congested cell move to a less loaded neighbor sooner.
• Reroutes traffic by nudging devices onto different frequency bands or nearby cells with spare capacity.

Each adjustment produces a measurable result. The agent keeps the changes that improve the reward and reverses the ones that do not.

4. Self-Healing

Midway through the event, one radio unit degrades. Its throughput collapses. The system detects the anomaly, and neighboring cells increase power and adjust tilt to cover the gap. Users in that sector see a brief dip, not an outage. This is the healing in self-healing: automatic compensation for a failed or degraded component.

By full time, the event passed without a control-room intervention. The agent restores normal parameters as the crowd disperses.

🎬 [VIDEO: "What is Open RAN?" — youtube.com — A short, clear explainer of the open RAN architecture and why programmable controllers matter]

Why RL and Not Simple Rules?

Three reasons operators reachreachThe number of unique people exposed to your message in a given period. Unlike impressions, reach counts each person once, no matter how often they see it.View full definition → for learning-based approaches over static rules:

The state space is huge. Dozens of parameters across hundreds of cells interact. A change in one cell affects its neighbors. Hand-writing rules for every combination is impractical.

Conditions shift constantly. A stadium, a highway at rush hour, and a business district at lunch all behave differently. An RL policy adapts to context instead of forcing one rulebook everywhere.

Speed. Machine cycles beat human reaction time. The agent can test and adjust within seconds.

A Simplified View of the Reward

To make this concrete, here is what a reward function might look like in pseudocode. This is illustrative, not production code.

The weights (1.0, 50.0, 10.0) encode business priorities. An operator that considers dropped calls the worst outcome for customer satisfactioncustomer satisfactionCustomer Satisfaction Score, a direct measure of satisfaction captured right after a specific interaction or experience, usually on a short rating scale.View full definition → weights that penalty heavily. Tuning these weights is a strategic decision, not just a technical one.

The Business Case

Why do operators invest in this?

Customer retention. Dropped calls and poor data are top drivers of churn (customers leaving for a competitor). Fixing them at the busiest moments protects revenue.

Operating cost. Manual RAN optimization is labor-intensive. Automation frees scarce radio engineers for higher-value work.

Energy. RL can power down or idle underused equipment during quiet periods, then bring it back for events. Energy is one of an operator's largest running costs, so this matters both financially and for sustainability targets.

Capital efficiency. Squeezing more capacity from existing towers can defer the cost of building new ones.

Risks and Guardrails

Self-healing AI is powerful, and it is not "set and forget." Professionals should understand the failure modes.

Reward Hacking

If the reward function is poorly designed, the agent may find shortcuts that boost the score while hurting the real goal. For example, an agent rewarded only for throughput might starve edge users to feed a few high-volume ones. Careful reward design and monitoring are essential.

Training Safely

You cannot let a raw, untrained agent experiment on a live network serving emergency calls. Operators train agents in digital twins: high-fidelity simulations of the network. The agent makes its early mistakes in the simulation, not on real customers.

Human Oversight

Regulators and operators generally require guardrails: hard limits the agent cannot cross (for example, minimum coverage for emergency services), plus the ability for a human to pause or override. AI here augments engineers, it does not replace accountability.

Explainability

When the network makes a decision, engineers need to understand why, especially after an incident. Many teams pair the RL agent with logging and monitoring so every action and its rationale can be reviewed. For a solid grounding in responsible AI practices, the NIST AI Risk Management Framework is a useful free reference.

Where This Is Heading

The trend for 2026 and beyond is tighter integration between AI and open, programmable networks. As O-RAN adoption grows, more operators can plug third-party AI applications (called rApps and xApps) into the RIC, creating a marketplace of optimization tools rather than one locked vendor stack.

The stadium example is a preview. The same self-healing logic applies to disaster recovery, seasonal tourism surges, and the everyday grind of keeping millions of connections smooth.

Key Takeaways

• The RAN is tunable, and AI tunes it faster than humans can. Reinforcement learning adjusts antenna tilt, handover thresholds, and traffic routing in seconds, based on live rewards.
• Self-healing means automatic compensation. When a radio fails, neighboring cells adjust to cover the gap, turning a potential outage into a brief dip.
• The reward function encodes business priorities. Weighting dropped calls, throughput, and energy is a strategic choice, not just an engineering one.
• Guardrails are non-negotiable. Digital twins for safe training, hard limits for emergency coverage, and human override keep automation accountable.
• Open, programmable RANs (O-RAN) are accelerating adoption by letting operators mix and match AI optimization apps instead of relying on a single vendor.