AI-For-RAN: Enabling Smart and Adaptive Radio Access Networks

AI-For-RAN

As we move towards 5G-Advanced and 6G, the integration of Artificial Intelligence (AI) with RAN is no longer optional — it's essential. One of the most powerful domains leading this shift is AI-RAN where AI-For-RAN will be going to become one of the Area where AI is embedded for the RAN to optimize and enhance its internal operations.

AI-For-RAN focuses on using AI models and algorithms to directly enhance RAN performance, particularly at the physical and MAC layers. This domain holds learning-based techniques to improve network efficiency, spectral usage, and real-time adaptability.

Core Use Cases & Functional Areas in AI-For-RAN

• Intelligent Channel Estimation

  AI/ML models, especially deep learning architectures like CNNs and LSTMs, are used to predict Channel State Information (CSI) more accurately under highly dynamic conditions like fast fading and high user mobility. This reduces pilot overhead and improves link robustness in dense urban deployments.

• Adaptive Forward Error Correction (FEC)

  Traditional static FEC schemes are being replaced by AI-driven adaptive FEC, which continuously learns and predicts error patterns based on SNR, BER, and channel type, dynamically selecting coding parameters to minimize retransmissions and latency.

• Smart Modulation and Coding Scheme (MCS) Selection

  Instead of relying solely on CQI reports, AI models predict optimal modulation and coding schemes using historical transmission outcomes and real-time channel conditions—enhancing spectral efficiency and user throughput.

• AI-Enhanced MAC Scheduling

  Reinforcement learning and supervised learning approaches enable schedulers to learn optimal scheduling policies under multi-user, multiservice environments. These AI-based schedulers balance QoS, latency, priority, and energy efficiency better than traditional rule-based methods.

• Neural PHY Layer Transceivers (Tx/Rx)

  Deep learning-based transceiver architectures replace conventional DSP blocks to enable joint encoding-decoding, modulation, and detection via end-to-end training. These systems self-optimize based on loss gradients and support nonlinear channel environments.

  
  

• AI-Enabled RAN Intelligent Controller (RIC)

  1. Near-Real-Time RIC uses ML models in xApps for functions like mobility management, beam management, interference mitigation, and dynamic cell activation.

  2. Non-Real-Time RIC applies long-term analytics and AI models in rApps for policy optimization, SON, and traffic prediction.

• Dynamic Beamforming & Beam Management

  AI algorithms trained on spatial, temporal, and mobility datasets predict user movement and dynamically adjust beam direction and shape, reducing beam misalignment and improving mmWave/hybrid beamforming reliability.

• Energy-Efficient Radio Resource Management

  AI identifies usage patterns and traffic predictability to intelligently turn off components (like antennas or processing chains) during off-peak hours, significantly contributing to green RAN operations.

  
  

• Proactive Handover and Mobility Management

  Predictive models anticipate user trajectory, velocity, and network load to trigger proactive handovers, avoiding dropped calls and ensuring seamless user experience even at cell boundaries.

Key Goals of AI-For-RAN:

• Performance Optimization -Implementing AI/ML models that learn from real-time and historical data to maximize throughput, minimize latency, and optimize power consumption.

  
  

• Site-Specific Learning -AI tailors models based on location-specific traffic profiles, interference patterns, and environmental factors — leading to hyper-personalized network tuning.

  
  

• Self-Optimization & Self-Healing -Using reinforcement learning (RL) and unsupervised learning, networks can automatically detect anomalies, reconfigure parameters, and even recover from faults without human intervention.

  
  

• Cross-Layer Intelligence -AI connects PHY, MAC, and RRC layers for holistic optimization — breaking the traditional silos in RAN design.

  
  

• Energy Efficiency Gains -AI algorithms selectively activate/deactivate baseband functions, antennas, and power amplifiers during low-load conditions — a critical enabler for sustainable green networks.

Why Does AI-For-RAN Matter?

As mobile networks evolve towards ultra-dense, ultra-low-latency, and ultrareliable systems (especially in 5G-Advanced and 6G), the complexity of managing radio resources, user mobility, interference, and QoS grows exponentially.

AI-For-RAN offers a transformative solution to these challenges by embedding intelligence within the RAN fabric.

1. Real-Time Closed-Loop Optimization

AI enables continuous monitoring of RAN metrics and immediate response actions in real-time — forming a closed-loop control system. This allows: Instant adjustments to beamforming, scheduling, or handover thresholds Dynamic adaptation to traffic bursts or user mobility Faster convergence to optimal parameters without human intervention

Example: A reinforcement learning-based MAC scheduler continuously learns traffic demand patterns and adjusts scheduling windows on the fly, improving overall network responsiveness.

2. Massive Spectral and Energy Efficiency Improvements

Conventional RAN systems rely on static rules for resource allocation and power control, which often underperform in dynamic conditions. AI learns and adapts: Optimal MCS (Modulation and Coding Scheme) per user and per cell Power scaling strategies based on real-time load and interference Dynamic antenna activation to minimize energy use during off-peak hours

Impact: This directly contributes to green networking goals, saving energy and maximizing spectrum reuse — a key target in 6G sustainability objectives.

3. Predictive Maintenance of RAN Components

AI models trained on historical equipment data (voltage, temperature, error logs) can: Predict component failures before they occur Trigger alarms and recommend proactive replacements or reconfigurations Reduce downtime and improve network reliability

Example: A baseband unit exhibiting rising error rates over time can be flagged by an AI model weeks before complete failure, allowing preventive action.

4. Support for Dense Heterogeneous Networks (HetNets)

With the deployment of small cells, macro cells, massive MIMO arrays, and multi-RAT access points, modern RANs are increasingly heterogeneous and dense.

AI handles this complexity by: Managing interference across overlapping cells Deciding optimal handovers in multi-layer deployments Balancing load across Wi-Fi, 4G, 5G NR, and future 6G links.

Use Case: An AI-powered RIC can coordinate traffic offloading from congested macro cells to underutilized small cells in real-time based on mobility and application type.

5. Enhanced User Experience Even at Cell Edges

AI-based beam management, link adaptation, and handover decisions significantly improve the user Quality of Experience (QoE), especially for: High-speed users (e.g., in trains or vehicles) Users at cell boundaries or in poor coverage zones AR/VR and real-time applications with strict latency requirements

Impact: Better throughput, lower latency, and fewer dropped connections, resulting in consistent service delivery — regardless of user location or device type.

References

• Deep Learning-Based Channel Estimation for Beamspace mmWave Massive MIMO Systems -IEEE Transactions on Vehicular Technology, 2020

• Machine Learning-Aided Forward Error Correction: Open Problems and Future Directions IEEE Communications Magazine, 2021

• Deep Learning-Based Adaptive Modulation and Coding for Physical Layer Optimization in 5G-IEEE ICC, 2019

• A Deep Reinforcement Learning Framework for Wireless Resource Scheduling in Multi-user Environments-IEEE Access, 2020

• An Introduction to Deep Learning-Based Communication Systems-IEEE Journal on Selected Areas in Communications, 2018

• O-RAN Architecture Description v7.0-O-RAN Alliance Specifications

• AI-Driven Beam Management for mmWave and THz Communications: Challenges, Opportunities, and Future Directions-IEEE Communications Magazine, 2021

• Green AI for 5G and Beyond: Efficient Power Control in Ultra-Dense Networks-IEEE Access, 2022

• Machine Learning for Mobility Management in Cellular Networks: A Survey-IEEE Communications Surveys & Tutorials, 2021

• 3GPP TR 38.816 – Study on Artificial Intelligence (AI)/Machine Learning (ML) for NG-RAN 3GPP Technical Report (Release 17) URL:https://www.3gpp.org/ftp/Specs/archive/38_series/38.816/

• AI-RAN: Artificial Intelligence – Radio Access Networks-White Paper by NVIDIA, 2023 URL: https://resources.nvidia.com/en-us/telecom/ai-ran-whitepaper (Highlights NVIDIA’s vision and implementation of AI-For-RAN, AI-And-RAN, and AI-On RAN architectures)