AI Beam Management in O-RAN via Offline Training
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Solution Overview
Problem
Current beam management techniques in open radio access networks (O-RAN) face challenges in efficiently managing beams due to high computational requirements and resource consumption, especially in scenarios involving massive multiple-input and multiple-output (MIMO) systems.
Innovation Solution
The proposed solution involves an AI/ML-assisted beam management method that utilizes a non-real time RIC for offline training of AI/ML models and a near-real time RIC for online training and inference. This approach enables efficient beam management by distributing computational tasks and optimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional beam management techniques are used in O-RAN, then beam alignment can be achieved, but computational requirements and resource consumption become excessively high
Solution Approach 1:
The patent segments the beam management process into two distinct phases: offline training phase (performed during low-traffic periods) and online inference phase (performed during high-traffic periods). This segmentation allows computationally intensive model training to be separated from real-time beam selection, reducing online computational requirements while maintaining alignment accuracy.
Solution Approach 2:
The patent performs preliminary action by training AI/ML models offline during low-traffic periods before the actual beam management is needed. The trained models are then deployed for online inference during high-traffic periods, eliminating the need for real-time training and reducing online computational burden.
2Measurement precision
If AI/ML models are trained offline during low-traffic periods, then model accuracy improves, but training time and computational load increase during off-peak hours
Solution Approach 1:
The patent implements periodic action by scheduling offline model training during periodic low-traffic intervals. The system alternates between training phases (during low-traffic periods) and inference phases (during high-traffic periods), creating a rhythmic operation pattern that balances accuracy improvement with service availability.
Solution Approach 2:
The patent changes operational parameters by adjusting the traffic threshold that triggers mode switching between offline training and online inference. By dynamically adjusting when to perform training versus inference based on traffic conditions, the system optimizes the balance between model accuracy and service responsiveness.
3Productivity
If the system switches between offline training and online inference modes based on traffic conditions, then resource utilization optimizes, but system complexity increases
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring traffic conditions and using this information to determine whether to perform offline training or online inference. The system adjusts its operational mode based on real-time traffic feedback, optimizing resource utilization while maintaining manageable complexity through rule-based decision logic.
4Productivity
If beam management is performed using massive MIMO systems, then network capacity increases, but power consumption in terminals rises
Solution Approach 1:
The patent extracts the computationally intensive beam management calculations from the terminal devices and relocates them to the network infrastructure (base stations and O-RAN controllers). By performing AI/ML-based beam selection in the network rather than in terminals, the system maintains high network capacity while significantly reducing terminal power consumption.
Data Source
AI summary
A method of a near-real time controller may comprise: receiving, from a non-real time controller, information on an offline-trained artificial intelligence/machine learning (AI/ML) model; obtaining, from a base station, first data for beam management inference based on the offline-trained AI/ML model; performing beam management inference based on the offline-trained AI/ML model using the first data; and providing a control and policy according to a result of performing the beam management inference to the base station.