Engineers and researchers have made advances in embedding AI and machine learning directly within both the radio access network (RAN) and core network. These AI-native systems now support automated energy savings, spectral efficiency improvements, better mobility management, and predictive fault detection. These capabilities are emerging in live public and private 5G deployments as well as enterprise edge networks. So, now let us look into Recent Advances in AINative RAN and Core Network Automation along with Smart LTE RF drive test tools in telecom & RF drive test software in telecom and Smart 5g tester, 5G test equipment, 5g network tester tools in detail.
- Energy Efficiency via AI-Driven Control
Energy saving is a major target for network operators. AI models monitor network load, throughput, airtime, buffer status, resource block usage, signal quality, and user traffic trends. These inputs feed decision algorithms that adjust antenna tilt, deactivate unused carriers or baseband units, and power down cells during low usage periods. Field metrics show per-site energy consumption reductions in the 10–14% range without affecting service performance.
Open RAN environments add complexity but also visibility. AI models built on telemetry from infrastructure, platform, and RAN layers (e.g. in O-RAN) support predictive energy control while remaining auditable via explainable-AI methods.
- Spectral Performance Optimization
AI algorithms now continuously tune spectral resource allocation in real time. Techniques include dynamic spectrum sharing, traffic steering based on application priorities (e.g., video vs IoT), and interference coordination. Automated beam shaping, adjustment of antenna tilt and power, and dynamic carrier assignment deliver throughput gains—typically 5% higher downlink and up to 30% uplink improvements at cell edges.
These AI-driven optimizations adapt to changing traffic and user mobility and significantly improve spectral efficiency across public and enterprise 5G networks.
- Mobility and Handover Enhancement
Predictive handover management has been improved using machine learning models that analyze user movement, signal degradation, and congestion. In ultra-dense networks or vehicle-heavy environments, proactive handovers help reduce latency and dropped calls.
A recent framework (AEPHORA) demonstrates joint handover control and resource allocation optimized for vehicle-to-everything (V2X) use-cases. The framework predicts uplink/downlink resource demand and adjusts handover timing and resource allocation to save transmission power while maintaining service reliability within delay constraints.
Review papers in ultra-dense 5G/6G environments confirm that AI-driven mobility decision logic improves handover success and reduces failure rates using reinforcement learning and deep learning models.
- Fault Prediction and Automated Recovery
Fault detection in RAN components and core infrastructure has improved significantly. AI agents ingest telemetry from hardware, software logs, sensor readings, and performance metrics to detect anomalies or early signs of failure. The FALCON framework, designed for Open RAN setups, achieved over 98% accuracy in fault prediction by combining multiple telemetry layers.
Once a fault or anomaly is predicted, automated remediation routines can trigger reconfiguration or handover changes without manual intervention. This model supports near-real-time self-healing in radio network operations.
- Closed-Loop Automation and AIOps
The overall system architecture is moving toward closed control loops, often referenced as AIOps (AI for IT operations). The steps cycle through data capture, analysis, decision, action, and evaluation. Near-real-time (xApp) agents handle fast control loops and rApp agents run scheduled planning tasks to update cell configurations based on week-long trends.
This approach enables autonomous execution of tasks like load balancing, cell reconfiguration, power control, and scheduling optimization. Some operators have deployed near-full autonomy in routine network operations, reducing staff interventions and improving speed to respond.
Deployment in 5G Public, Enterprise, and Edge Networks
Public 5G networks across major markets now include AI-based RAN elements for energy control and mobility automation. Enterprises deploying private 5G or edge networks also adopt these features in campus or industrial sites where resilient performance and low-latency operation are critical.
Use cases that benefit directly:
- Outdoor public 5G networks serving mobile users, vehicles, and IoT devices.
- Private campus or industrial 5G deployments where power savings and optimized coverage matter.
- Edge networks supporting AR/VR, autonomous AGVs, or real-time remote monitoring.
Summary of Key AI-Native Network Automation Capabilities
Integration with Open RAN and Industry Standards
| Capability | AI Role | Benefit |
| Energy control | Predict traffic and adjust power states, antenna elements | 10–14% energy savings per site |
| Spectral performance | Real-time spectrum allocation, beam adaptation, traffic steering | 5–30% throughput gains at cell edge |
| Mobility / handover | Predictive handover control, mobility-aware resource allocation | Reduced call drops and latency |
| Fault detection & self-healing | Multi-layer telemetry, classification for fault prediction, automated recovery | Fewer outages, proactive maintenance |
| Closed-loop AIOps | xApps and rApps automate parameter tuning, load balancing, trend-driven updates | Reduced manual configuration, faster optimization |
Open RAN architecture and the RIC framework support modular deployment of AI agents as xApps and rApps. AI agents trained with data from multiple vendor components run atop standardized interfaces, allowing seamless automation across heterogeneous infrastructure.
Standardization efforts from 3GPP and the AI-RAN Alliance emphasize AI-enabled energy saving, load balancing, and mobility optimization use cases. Real-world pilots integrate AI-native RAN architectures with explainable-AI models to ensure traceability and governance.
These AI-native network automation advances boost network performance, reduce costs, and support sophisticated new services. Automation now handles low-latency control loops and long-term planning tasks, while AI prediction enables proactive tuning and recovery. These developments are transforming both public and private 5G deployments into intelligent, self-managing systems.
About RantCell
RantCell is a software-based network measurement tool that transforms Android phones into test devices for 2G, 3G, 4G, and 5G networks. It helps telecom engineers, system integrators, and regulators monitor network quality across outdoor and indoor environments. Whether you’re validating 5G rollout, mapping signal blackspots, or analyzing user experience KPIs, RantCell makes testing simple, scalable, and cost-efficient. Also read similar articles from here.
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