AI Beam Prediction Measurements With Inference and Monitoring Split
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Solution Overview
Problem
Conventional solutions for beam management in wireless communication networks lack flexibility and efficiency in configuring and adapting AI/ML model measurements for beam prediction, leading to suboptimal performance and increased resource overhead.
Innovation Solution
A communication device receives a measurement configuration for AI/ML model beam prediction, defining separate sets of resources for inference and monitoring measurements with a common trigger value, allowing dynamic adaptation to radio conditions and device capabilities, and supports periodic, aperiodic, and semi-persistent measurements through RRC, DCI, and MAC CE.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate sets of resources are configured for inference and monitoring measurements, then measurement flexibility and adaptability are improved, but device complexity and configuration overhead increase
Solution Approach 1:
The patent divides measurement resources into separate sets: first resources for inference measurements and second resources for monitoring measurements. This segmentation allows independent configuration and adaptation of each measurement type, enabling the system to optimize inference accuracy and monitoring efficiency separately while managing complexity through structured resource allocation.
Solution Approach 2:
The patent implements dynamic measurement adaptation by allowing the network to configure different measurement settings, reporting periods, and resource allocations based on actual radio conditions and device capabilities. The system can dynamically switch between periodic, event-triggered, and semi-persistent measurement modes, enabling flexibility in adapting to changing environmental conditions while maintaining manageable complexity through standardized procedures.
2Reliability
If AI/ML model measurements are implemented for beam prediction, then beam management performance is improved, but resource overhead and measurement complexity increase
Solution Approach 1:
The patent designs measurement resources that serve multiple functions: the same resource sets can be used for both inference measurements (to generate beam predictions) and monitoring measurements (to validate model performance). This multi-functionality reduces overall resource overhead by eliminating redundant measurements while maintaining the dual objectives of accurate beam prediction and reliable model monitoring.
Solution Approach 2:
The patent optimizes measurement overhead by dynamically adjusting measurement parameters such as resource allocation, measurement frequency, and reporting thresholds based on radio conditions and device capabilities. The system can reduce measurement density in stable conditions and increase it during transitions, thereby maintaining beam prediction accuracy while minimizing unnecessary measurement overhead.
3Productivity
If dynamic adaptation to radio conditions is enabled, then measurement efficiency is improved, but system complexity and processing requirements increase
Solution Approach 1:
The patent implements feedback mechanisms where measurement results from both inference and monitoring are fed back to the network and device. This feedback enables dynamic adjustment of measurement configurations, triggering new measurements when performance thresholds are crossed or when radio conditions change significantly. The feedback loop improves measurement efficiency by focusing resources on critical moments while maintaining manageable complexity through threshold-based triggering and standardized feedback procedures.
Data Source
AI summary
A measurement solution for beam prediction in a communication system. A first communication device receives a measurement configuration for at least one AI/ML model for beam prediction from a second communication device. The measurement configuration indicates a first trigger value for measurement of at least one first resource in a first set of resources and at least one second resource in a second set of resources. The first set of resources is for an inference measurement of the AI/ML model and the second set of resources is for a monitoring measurement of the AI/ML model. Based on the received measurement configuration, the first communication device measures the first resource to obtain a first inference measurement report and measures the second resource to obtain a first monitoring measurement report for the AI/ML model.