By Iain Gillott, Senior Research and Technical Advisor, WIA
As discussed in the first blog in this series, Radio Access Networks and Artificial Intelligence (RAN-AI) comes in two flavors that work together to bring multiple benefits:
- AI in the RAN, where AI capabilities are embedded inside the RAN components (radio, antennas, etc.,) and are designed to support use cases at the cell site; and
- AI on the RAN, where the AI processing takes place outside the RAN infrastructure but uses data from the AI in the RAN, as well as other inputs and data sets.
This article explores in more detail some of the ways that AI is being used in the RAN, specifically:
- Network Optimization
- Energy Efficiency
- Enhanced Spectrum Management
- Edge Intelligence and Distributed RAN (virtual RAN/Open RAN).
AI-Driven Network Optimization
AI enables Self-Organizing Networks (SON), traffic prediction, and load balancing, all of which can improve the quality of service (QoS) for the end user, improve the efficiency of radio resources, and reduce the amount of manual intervention required by network managers.
Self-Organizing Networks have been in various states of implementation since the launch of LTE. AI-enhanced SON systems can continuously ingest data from RAN nodes and user devices to make real-time decisions about:
- Self-configuration, where the AI auto-configures new equipment.
- Self-optimization tunes handover parameters, antenna tilt and load-balancing strategies.
- Self-healing reacting to situations when a cell site goes down, detecting outages quickly and reconfiguring neighboring cells accordingly.
AI is very good at time-series analysis, making it well-suited for predicting traffic volumes across the network, allowing AI models to: pre-emptively increase capacity in certain cells; reassign users to less congested sectors or spectrum bands; and activate or deactivate carrier components. The result is reduced latency and optimized spectral efficiency, helping operators meet Service Level Agreements (SLAs) even during peak usage.
AI models are also used to improve the cell handover (a typical source of call disconnects) – rather than use static measurements. AI-driven handover models predict when a user is about to move between cells, selecting the optimal timing and target cell for the handover. Benefits include fewer dropped calls, smoother transitions and reduced ping-pong handovers.
AI for Energy Efficiency
Energy consumption is one of the most pressing concerns in mobile network operations today as the RAN is responsible for most of the wireless network’s energy consumption. The goal of AI is to provide a powerful toolkit to curb this consumption without compromising service quality. Several strategies are used:
- Dynamic Power Management, where AI models predict when demand will drop and then dynamically reduce transmission power, shut down secondary carriers or bands and throttle power adaptively. AI can also be used to determine which combination of frequency bands delivers the most energy-efficient performance based on current network conditions in networks using carrier aggregation.
- Cell Sleep and Wake-Up Strategies are some of the more advanced AI energy-saving techniques enabling certain cells or components to be put in sleep mode or deactivated during low-traffic periods, such as overnight.
- AI for Massive MIMO Energy Management allows AI to dynamically control which MIMO antenna arrays are active depending on user density and data demands by deactivating unused antenna elements, optimizing beamwidth and using real-time evaluation of signal-to-noise ratios.
AI-Enhanced Spectrum Management
Efficient use of spectrum is fundamental to RAN performance, especially as operators contend with limited frequency allocations, fragmented spectrum assets and growing interference. AI is a key enabler in optimizing spectrum usage, ensuring maximum throughput and minimal service degradation in increasingly crowded spectral environments. AI-assisted strategies include:
- Dynamic Spectrum Allocation and Sharing: AI enables real-time, intelligent decision-making for dynamic spectrum access, especially in environments that include a mix of licensed, shared and unlicensed bands. AI also facilitates cross-technology coexistence, ensuring LTE, 5G New Radio (NR) and Wi-Fi can operate within shared or adjacent bands by monitoring activity patterns and dynamically assigning time-frequency slots to avoid contention.
- Interference Detection and Mitigation: AI models, especially those based on deep learning, can detect subtle interference patterns that may not trigger traditional alarms. These models then can classify sources of interference and optimize inter-cell coordination to mitigate interference.
- Channel Quality and SINR Optimization: Channel conditions vary rapidly due to user mobility, building materials, weather and other factors. AI models trained on radio conditions and user location data can predict variations in signal-to-interference-plus-noise ratio (SINR), proactively switch users to alternate channels or bands and optimize throughput.
- Spectrum Sensing and Cognitive Radio: In more advanced scenarios, AI enables the use of cognitive radio systems, where radios can continually and autonomously sense their electromagnetic environment, learn the behavior of other users and adapt transmission models and parameters.
Edge Intelligence and Distributed RAN (vRAN/ORAN)
The architectural shift to cloud-native, virtualized and disaggregated networks (such as virtual RAN (vRAN) and Open RAN (ORAN) has created both new opportunities and complexities. As functionality is distributed across Radio Units (RUs), Distributed Units (DUs), and Centralized Units (CUs), real-time optimization becomes more difficult due to increased latency and coordination challenges. AI solves this by enabling edge intelligence, deploying smart decision-making solutions closer to where data is generated and consumed.
Implementing AI models near to or inside the DU (which sits at the network edge) enables low-latency sensitive tasks such as beam selection and handover management; uplink and downlink scheduling; and traffic prediction and local congestion mitigation. By handling these workloads at the edge, the need to continuously communicate with the core is reduced, which reduces backhaul latency and enables faster network reactions, as well as reduces the load on the mobile core.
ORAN architecture introduced the RAN Intelligent Controller (RIC), a software framework responsible for controlling and optimizing RAN functions. The RIC can be deployed in both non-real time and near-real time configurations and host a range of AI applications that can forecast capacity, optimize radio strategies and scheduling, help mitigate interference and balance load across the RAN, as well as enforce mobility policies.
AI has become especially important in ORAN networks that use radios from multiple vendors, where integration to achieve optimum network performance can be an issue. AI enables multi-vendor performance optimization and orchestration of virtual network functions as the network load changes and end-user demands increase and decrease.
Benefits and Impact
As you can see, AI algorithms and models can be used across the RAN to optimize a variety of functions. The RAN has always been a key part of the overall efficiency of a mobile network and key to end user experience. As networks have become more complex, so the RAN has become both more important and harder to deploy, tune and manage. AI helps address these challenges and ultimately allows:
- Higher spectral efficiency, allowing more data to flow through the same bandwidth.
- Improved user experience, with reduced interference and fewer dropped sessions.
- Operational flexibility, enabling MNOs to support more use cases within the same spectrum portfolio.
Future 6G networks are expected to embed AI as a native feature, with edge-based training, inference, and closed-loop control fully integrated into the network fabric.
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