AI-Driven Beam Management With Reduced Measurement Beam Reporting
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Solution Overview
Problem
Existing AI-powered beam management systems in 5G NR networks face inefficiencies due to the assumption that the beam management model remains well-trained, which is challenged by dynamic factors like interference, UE mobility, and signal strength variations, leading to high signaling overhead and performance degradation.
Innovation Solution
Implement a delivery beam management learning model that utilizes a smaller set of beams (Set B) for initial measurement, with AI-driven refinement to determine optimal delivery beams (Set A) based on user equipment feedback, reducing the number of reference signals needed and optimizing beam management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a well-trained beam management model is assumed to remain static, then device complexity is reduced, but reliability deteriorates due to dynamic factors like interference and UE mobility causing performance degradation
Solution Approach 1:
The patent implements dynamic beam management by transitioning from static pre-trained models to online learning models that continuously adapt to changing radio conditions. The model is updated in real-time based on feedback from measurement beams, allowing the system to maintain reliability despite dynamic factors like interference and UE mobility while managing complexity through efficient online learning algorithms.
2Reliability
If AI-driven beam refinement is implemented dynamically, then beam management reliability is improved, but signaling overhead increases due to continuous model updates and feedback requirements
Solution Approach 1:
The patent extracts only the essential feedback information needed for model updates by having the UE report measurements of a reduced set of measurement beams rather than all possible beams. This selective extraction of critical information maintains model reliability while significantly reducing the signaling overhead associated with continuous feedback transmission.
Solution Approach 2:
The system performs partial beam management by focusing online learning updates only on the most critical beam parameters and using a reduced set of measurement beams. This partial action approach maintains sufficient beam management reliability while reducing the computational and signaling burden compared to exhaustive beam management.
3Productivity
If a reduced set of measurement beams (Set B) is used for AI-driven refinement, then productivity is improved by reducing reference signals, but measurement precision may deteriorate
Solution Approach 1:
The patent introduces Set B measurement beams as intermediary elements that bridge the gap between the reduced beam set and the full beam set (Set A). These measurement beams serve as proxies that provide sufficient information for AI-driven model updates without requiring measurements of all possible delivery beams, thus maintaining measurement precision while improving productivity through reduced signaling.
Data Source
AI summary
A radio network node transmits beam reporting configuration information indicative of measurement beams. User equipment receive the information and measure signals transmitted via the measurement beams. The user equipment transmits measured measurement beam signal values to the node via an existing connection, or if the user equipment is idle, via a special-purpose connection established in response to a request by the user equipment. An idle user equipment may avoid requesting a special-purpose connection if a difference between a measured measurement beam signal value and a measured synchronization signal block signal value does not exceed a reporting criterion. The node may analyze measured signal values received from the user equipment using a learning model to determine a refined delivery beam usable to deliver traffic to the user equipment, and may analyze a measured signal value, reported by the user equipment, corresponding to the delivery beam to determine a different delivery beam.