AI Beam Management Resource Mapping for Training-Inference Consistency
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Solution Overview
Problem
Existing wireless communication systems face challenges in maintaining consistency between training and inference operations of AI/ML models, leading to potential degradation in performance due to environmental changes.
Innovation Solution
A method and apparatus for intelligent beam management that ensures consistency by configuring distinct resource sets for training and inference operations of AI/ML models, using associated identifiers (IDs) to match or differentiate beams, and transmitting signals based on these IDs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If AI/ML models are deployed for intelligent beam management, then intelligent functionality is improved, but consistency between training and inference operations deteriorates due to environmental changes
Solution Approach 1:
The patent applies preliminary action by configuring resource sets and associated IDs during the training phase before deployment. The training resource sets and inference resource sets are pre-configured with matching associated IDs to ensure consistency. This preliminary configuration establishes the foundation for consistent AI/ML model operations across different phases, preventing performance degradation due to environmental changes.
Solution Approach 2:
The patent implements copying by creating inference resource sets that replicate the structure and associated IDs of training resource sets. The inference resource sets are configured to mirror the training resource sets, ensuring that the AI/ML model encounters identical resource configurations during both training and inference operations. This copying approach maintains operational consistency while enabling intelligent functionality.
2Adaptability or versatility
If resource sets are configured for both training and inference operations, then operational flexibility is improved, but system complexity increases
Solution Approach 1:
The patent applies universality by designing a unified resource set configuration framework that serves both training and inference operations. The same resource set configuration mechanism and associated ID system are used across different operational phases, reducing the need for separate complex configuration systems. This multi-functional approach improves operational flexibility while controlling system complexity through reuse of the same configuration paradigm.
Solution Approach 2:
The patent implements segmentation by dividing resource sets into distinct training resource sets and inference resource sets, each with their own configuration parameters and associated IDs. This segmentation allows independent optimization and management of resources for different operational phases while maintaining overall system coherence through the unified associated ID framework, thereby managing complexity through structured organization.
3Measurement precision
If associated IDs are configured to match beams across different resource sets, then beam management accuracy is improved, but configuration overhead increases
Solution Approach 1:
The patent applies local quality by configuring associated IDs specifically for beam-related resources where precision is critical, rather than uniformly across all resource parameters. The associated ID system focuses on identifying and matching beams across training and inference resource sets, providing localized precision where it matters most for beam management accuracy while avoiding unnecessary configuration overhead in other areas.
Data Source
AI summary
A method of a base station may comprise: configuring a first resource set and a second resource set for training and inference operations of an artificial intelligence/machine learning (AI/ML) model; determining whether the first resource set and the second resource set are identical and configuring an associated identifier (ID) for each of the first resource set and the second resource set; and transmitting a signal to a terminal based on at least one of a plurality of beams corresponding to the associated ID of the first resource set or a plurality of beams corresponding to the associated ID of the second resource set.