Idle-Mode Beam Reporting for AI-Driven 5G Beam Selection
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Solution Overview
Problem
Existing AI-powered beam management systems in 5G NR networks face performance degradation due to dynamic changes in radio interface links, such as interference, UE mobility, and coverage level variations, leading to inefficient use of resources and increased signaling overhead.
Innovation Solution
Implement idle-mode beam measurement and reporting to train artificial intelligence learning models, using a smaller set of beams (Set B) for initial measurement, and inputting user equipment measurements into a beam management learning model to determine refined delivery beams (Set A) for efficient traffic delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI-powered beam management systems are implemented to adapt to dynamic changes in radio interface links, then beam management efficiency is improved, but radio signaling overhead increases
Solution Approach 1:
The beam management process is segmented into two distinct phases: idle-mode beam measurement and connected-mode AI-driven beam management. This segmentation allows the system to perform comprehensive beam measurements when UE is in idle mode without impacting connected-mode performance, thereby improving beam management efficiency while controlling signaling overhead during active communication
Solution Approach 2:
Beam measurements are performed in advance when user equipment is in idle mode, before actual data transmission begins. This preliminary action collects beam parameter data that can be used to train AI models and determine optimal delivery beams, reducing the need for extensive signaling during connected mode operations
2Measurement precision
If a larger set of beams is used for measurement to improve coverage, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The beam set is segmented into two categories: measurement beams with wider coverage for comprehensive signal quality assessment, and delivery beams with narrower coverage for efficient data transmission. This segmentation allows the system to use a larger effective measurement set without proportionally increasing the complexity of the delivery beam set
Solution Approach 2:
Different beam characteristics are assigned to different functional requirements: measurement beams are designed with wider coverage angles to capture signal variations across different locations, while delivery beams use focused narrow beams for high-rate data transmission. This local quality differentiation optimizes both measurement precision and transmission efficiency
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.