Artificial intelligence moves from a bolt-on optimization tool to the brain of the radio access network, reshaping how 5G is planned, operated, and healed in 2026.

Why 5G-Advanced needs AI in the RAN

As 5G matures, network complexity explodes. Operators must juggle massive MIMO arrays, dense small cells, dynamic spectrum sharing, private networks and a growing zoo of devices and slices. 3GPP’s Release 18, the first branded “5G-Advanced,” explicitly recognizes this complexity by introducing new features for AI-powered optimization across the radio interface, mobility, and energy management. ericsson.com

At the same time, AIOps has become more than a buzzword in telecom. A 5G Americas report on AI in wireless notes that intelligence is spreading across the stack, but the radio access network (RAN) remains the focal point because of its outsized impact on user experience, cost, and energy consumption. The report highlights the rise of RAN Intelligent Controllers (RICs), which host AI applications that continuously analyze network telemetry and issue near-real-time control actions. 5G Americas

RAN Intelligent Controllers and open architectures

In 2026, AI in the RAN is increasingly delivered through programmable control planes rather than opaque, embedded algorithms. Open RAN architectures specify standardized interfaces between the RIC and RAN components, allowing operators to deploy multi-vendor “xApps” and “rApps” for tasks such as load balancing, interference mitigation and slice admission control. 5G Americas

RAN Intelligent Controllers can run near-real-time control loops, reacting within tens to hundreds of milliseconds, or support slower supervisory functions such as long-term energy optimization. Operators feed the RIC with measurements gathered using enhanced data collection features introduced in Release 18, which expand the scope of self-organizing network and minimize the drive test capabilities to reduce manual optimization. Techplayon

From human-tuned networks to autonomous optimization

Traditional network planning and optimization relied on a combination of human expertise and offline simulations. In a 5G-Advanced world, the optimization loop becomes more like an autonomous driving stack. AI models continuously evaluate KPIs such as throughput, latency, mobility failures and energy use, then adjust parameters including handover thresholds, antenna tilt, carrier aggregation patterns and beamforming weights. arXiv

Vendors are racing to productize this vision. Samsung, for example, has highlighted an end-to-end AI-powered network platform that claims to provide full observability across planning, deployment, operations, and optimization, using AI to predict anomalies and automatically tune the network. Samsung. While the branding differs across suppliers, the underlying pattern is similar: AI agents embedded in the RAN continuously learn from live data and apply corrections with minimal human intervention.

Use cases: energy, experience and enterprise SLAs

In the early phase of 5G-Advanced, three AI RAN use cases stand out. The first is energy savings. AI agents can selectively power down carriers, adjust transmission power or reconfigure MIMO layers during off-peak hours while maintaining quality of experience, significantly reducing network OPEX and carbon footprint. Techplayon

The second is experience optimization. AI models predict congestion before it happens, proactively reroute traffic, or trigger edge offload so that latency-sensitive applications such as cloud gaming, XR collaboration, or telemedicine maintain strict performance targets. This requires tight coupling between AI RAN systems and the broader 5G-Advanced features for positioning, slicing, and exposure. ericsson.com

The third is enterprise service assurance. For industrial IoT and private 5G, customers expect deterministic behavior. AI-driven RAN controllers monitor slice-level KPIs for factory automation, digital twins or safety systems and dynamically reserve resources or reconfigure cells to uphold SLAs even when interference or failures occur. ericsson.com

Risks and governance for AI in the network core

Embedding AI deep inside the RAN also raises new concerns. Misbehaving models can cause large-scale outages or subtle degradations that are hard to trace. Operators must therefore develop robust validation pipelines, shadow-mode testing strategies and rollback mechanisms for AI policies, similar to how cloud providers manage canary deployments.

There are also questions of fairness and regulation. AI-based scheduling and admission control could inadvertently prioritize specific traffic, devices or neighborhoods if training data is biased. Regulators may expect operators to demonstrate explainability for AI decisions that affect critical services such as emergency calls, connected vehicles or public safety cameras. 5G Americas

Closing thoughts and looking forward

By 2026, many operators will still be in the early innings of AI RAN deployment, but the trajectory is clear: 5G-Advanced networks will increasingly manage themselves. The RAN becomes a living system that senses, learns, and acts at machine timescales. At the same time, human engineers focus on policy, architecture, and exception handling rather than day-to-day parameter tuning.

Over time, lessons from AI-native RAN will spill into future 6G design, where AI is expected to be even more tightly integrated into the air interface and control plane. For the next several years, however, the most important gains will be pragmatic ones: fewer dropped calls, better performance at cell edges, lower energy bills and a more sustainable path to scale for the densest and most demanding 5G deployments.

References

AI is Driving Innovation Across the Entire Wireless Cellular Landscape – 5G Americas. https://www.5gamericas.org/ai-is-driving-innovation-across-the-entire-wireless-cellular-landscape

5G Advanced, your network for the next wave of 5G – Ericsson. https://www.ericsson.com/en/5g/5g-for-service-providers/5g-advanced

5G-Advanced Overview – 5G Americas white paper. https://www.5gamericas.org/wp-content/uploads/2025/07/5G-Advanced-Overview.pdf

3GPP 5G Advanced Features – TechPlayon. https://www.techplayon.com/5g-advanced

Samsung’s Journey to AI-Powered Networks Beyond AI-RAN – Samsung Networks. https://www.samsung.com/global/business/networks/insights/blog/0909-the-next-frontier-samsungs-journey-to-ai-powered-networks-beyond-ai-ran

Jing Zang, Mobile Technology, Montreal, Quebec.
Peter Jonathan Wilcheck, Co-Editor, Miami, Florida.

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