​​The Impact of AI in Radio Access Network Optimization​

Reyna Reyes Delgado, RAN Engineer
15+ years of experience, driving LTE/5G network integration, optimization, and automation for global telecom operators, empowering seamless connectivity and high-performance networks.

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Introduction

The foundation of modern telecommunications, the Radio Access Network (RAN), has, in my opinion, reached a critical tipping point.

For decades, network operations relied on highly skilled engineers to manage this vital infrastructure. I’ve seen them manually adjusted power levels, optimized antenna tilts, and fine-tuned cell tower configurations, a meticulous process that demanded human expertise and patience.

But that era is over. The unprecedent complexity and scale of 5G have created a problem that is simply too large for manual processes:

1. Explosive Data Growth: Global mobile data traffic is accelerating rapidly, projected by 2030, 5G networks alone are expected to carry 80% of all mobile data traffic. According to Nokia's projections, we're looking at growth from 120 exabytes per month in 2023 to somewhere between 706 and 1,135 exabytes by 2033—up to a 9x increase in just a decade.

2. Unprecedented Density: 5G and Open RAN introduce massive-MIMO, dynamic spectrum sharing, and a complex mix of new cell types, multiplying the number of configurable parameters from hundreds to hundreds of thousands.

3. Real-Time Demands: New services like autonomous vehicles and low-latency gaming require the network to optimize itself in milliseconds, not the hours or days it takes an engineer to respond to a manual alarm.

The result is a widening gap between what the network is capable of and what human staff can manage. This performance gap leads to higher operational costs, greater energy consumption, and inconsistent customer experience.

The solution, as I see it, is the transformation to an AI-native network. Just as AI powers apps like Google Maps to instantly find the best route by analysing billions of data points, that same intelligence is now required to manage the RAN. This shift is about moving from a reactive, human-in-the-loop model to one of proactive, intelligent autonomy.

Why RAN Optimization Needs AI

RAN optimization plays a critical role in maintaining network performance, stability, and user satisfaction. It ensures high performance across key KPIs such as accessibility, retainability, call drop rates, throughput, low latency, and seamless mobility.

Picture this: A single modern 5G network can have thousands of small cells, each with Massive MIMO antennas broadcasting across multiple frequency bands.

• Engineers manage hundreds of parameters per cell site.

• Network conditions change every second, not just every hour.

• The RAN alone consumes up to 80% of total network energy, meaning every inefficiency cost real money and contributes to carbon emissions.

Consider a real-world scenario: A major concert draws 50,000 people to a stadium. Cell towers that normally serve 5,000 devices suddenly face a tenfold surge, with 50,000 users all trying to upload videos simultaneously. By the time engineers detect the congestion, analyse the data, and manually implement changes, the concert might already be over. Fans experience dropped calls, failed uploads, and frustration.

Traditional optimization is fundamentally reactive—it fights fires after they've started. This reliance on outdated methods is becoming completely untenable. We must ask: Are they obsolete? Can they truly support the projected 9x increase in traffic and handle dense, complex 5G architectures? Are they prepared for user demands that are vastly different from even a few years ago? The answer is a definitive No. Modern networks require intelligence that thinks ahead.

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Key Advantages of AI in RAN Optimization

AI doesn't just respond faster than human engineers. It thinks differently, seeing patterns that humans might miss and acting on predictions, not just reactions.

Here are some of the most impactful AI use cases in RAN optimization:

• Handling Massive Data Volumes: An AI system can process large-scale data from IoT devices, Massive MIMO arrays, and network slicing configurations. It analyses traffic load, interference, weather, and congestion to optimize parameters and allocate resources in real time.
  

• Proactive, Real-Time Optimization: Instead of waiting for alarms, AI predicts and prevents problems. During major events like the Super Bowl or World Cup, AI systems can detect congestion patterns early and automatically steer users to less loaded cells or frequency bands, without human intervention.

• Energy Efficiency and Sustainability: Telecom networks consume a massive amount of energy, around 2–3% of global electricity use. AI-driven energy management systems can dynamically power down a base station during off-peak hours, adjusting RAN power usage based on real-time demand, user behaviour, and network patterns.
  

• Anomaly Detection and Self-Healing: AI identifies patterns and anomalies, such as sleeping cells or signal drops, that traditional systems often miss. When detected, the AI can trigger self-healing actions—such as rerouting traffic or resetting faulty components—before customers even notice a problem.
  

• Seamless Multi-Vendor Coordination (Open RAN): In Open RAN environments, AI ensures smooth integration and coordination between equipment from different vendors, maintaining optimal network performance across diverse components. AI acts as a neutral orchestrator, ensuring all components (for instance, from Nokia radios to Ericsson software) work seamlessly together while maintaining optimal performance.

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Real-World Proof: AI-RAN Optimization in Action

• Ooredoo Qatar (FIFA World Cup 2022) - Flawless Performance at Scale

When Qatar hosted the World Cup, network failure wasn't an option. Ooredoo implemented Reailize's AI-driven Continuous Assurance platform to monitor and optimize their entire network infrastructure in real-time. The system processed over 800 terabytes of data and monitored more than 12 million voice calls, performing real-time anomaly detection, root-cause analysis, and predictive maintenance across RAN, core, IMS, and transport networks. In packed stadiums with tens of thousands of fans all posting, streaming, and calling simultaneously, the AI kept everything running flawlessly.

• Bell & Ericsson (Canada) - Link Adaptation Revolution

In a global first, Bell and Ericsson deployed AI-native link adaptation that continuously adjusts network parameters based on real-time signal quality and interference patterns. The system doesn't wait for problems, it optimizes proactively, millisecond by millisecond. The payoff? A 20% boost in downlink throughput and a 10% improvement in spectral efficiency. That means faster downloads, smoother streaming, and better service for millions of users without adding a single new cell tower.

• STC Group & Nokia (Saudi Arabia) - The Ultimate Stress Test (Hajj)

The annual Hajj pilgrimage is perhaps the world's most demanding mobile network event. Millions of pilgrims, many from different countries with different devices, converge on Mecca. In 2023, STC deployed Nokia's AI-powered Self-Organizing Network (SON) to handle the surge. The numbers are staggering: Despite a 40% traffic spike, the AI system executed over 10,000 autonomous corrective actions per hour. That's nearly three adjustments every second, 24/7. The result? 30% higher cell utilization, 10% better downlink throughput, and most importantly, seamless connectivity for millions of people during one of the most significant moments of their lives.

These examples show that AI is no longer theoretical. It is delivering measurable improvements in performance, efficiency, and resilience in the world’s most demanding mobile environments.

• NVIDIA & Nokia (USA) - Scaling AI-RAN for Global Spectral Efficiency

In a transformative $1 billion partnership, NVIDIA and Nokia are redefining how AI optimizes wireless networks. At the heart of this effort is NVIDIA ARC (Aerial RAN Computer), a GPU-accelerated, software-defined base station platform that integrates seamlessly with Nokia’s AirScale infrastructure. ARC enables real-time AI for RAN, using reinforcement learning to dynamically optimize beamforming based on traffic, mobility, environmental context, and even weather conditions. Early modelling suggests there may be measurable improvements in spectral efficiency, potentially allowing more data to be delivered within the same spectrum without a proportional increase in energy consumption. While these results are promising, the true impact will depend on integration outcomes and real-world network performance. This area remains important, given that wireless networks already account for nearly 2% of global power usage.

ARC also enables AI on RAN, turning base stations into intelligent edge nodes that host industrial AI applications right where data is generated. This shift brings cloud-scale optimization to the telecom edge, allowing networks to adapt in real time and deliver new services, while continuously improving efficiency, responsiveness, and spectral performance.

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Final Thoughts

The telecommunications industry stands at an inflection point. Networks are becoming exponentially more complex just as demands are skyrocketing. With 5G subscriptions expected to overtake 4G during 2027, this transition accelerates the need for networks that traditional optimization simply cannot manage.

My conviction is that AI is no longer just an enhancement; it has become essential. Networks optimized by AI are not only expected to perform better but also to adapt faster, make smarter decisions, predict problems, and lower costs while delivering superior service quality.

The operators leading with AI today aren't just keeping pace with market demands. They're defining what's possible in telecommunications, delivering experiences their competitors can't match, and building the foundation for technologies we're only beginning to imagine.

The core challenge is not the AI technology itself, but achieving the organizational and architectural readiness to implement it at scale. This preparation is critical because the network will never be 100% autonomous; even the most sophisticated AI will fail without human context. For instance, AI may suggest adding a new site to solve congestion, but without local knowledge, concerning building permits, antennae exceeding allowed SAR values, or planned decommissions, that suggestion is useless.

The question isn't whether AI will transform RAN optimization. The real question is It's whether your network will be leading that transformation or struggling to catch up. The future of telecommunications is intelligent, adaptive, and proactive. The future starts with a solid foundation.

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