Using AI to Manage Wireless Network Resources Smarter

Intel's 2025 patent on using a hierarchy of AI models to manage wireless network resources, making them more efficient by learning from network measurements and rewards.

What it covers

This patent describes a computer system, called a 'computing node,' designed to help manage wireless networks like the 5G and beyond (Next Generation or NG). It uses a smart approach with multiple Artificial Intelligence (AI) models, specifically machine learning models, organized in layers like a pyramid. The system takes measurements from the network and uses these AI models to make decisions. A key part is how these models communicate: a model at a lower level gets instructions from a model at a higher level, and it also uses its own measurements to send signals. This allows the system to train the AI models using 'reward functions,' which are like scores that tell the AI if it's doing a good job. For example, a model might decide how to best allocate bandwidth based on traffic data and instructions from a higher-level model overseeing network stability.

What it doesn't cover

• —Does not cover AI models that are not organized in a multi-level hierarchy.
• —Does not cover systems that do not generate reward functions for training AI models.
• —Does not cover wireless networks that are not 'Next Generation' (NG) networks.
• —Does not cover systems where control signaling only comes from a single AI model.
• —Does not cover network management that doesn't use network measurements.

The clever bit

The innovation lies in structuring the AI models in a hierarchy, allowing for more sophisticated and layered decision-making. This enables models at different levels to specialize in different aspects of network management, from real-time adjustments to longer-term strategic planning, all trained through a system of rewards.

Why it matters

As wireless networks become more complex with 5G and future technologies, managing their resources efficiently is crucial. This patent offers a method for using AI to dynamically optimize network performance, potentially leading to better speeds, lower latency, and more reliable connections for users.

Real-world examples

• 1.5G network resource management
• 2.Future wireless network optimization
• 3.Intelligent network controllers (RICs)