Wireless Capability Discovery for Multi-Model Beam Management
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Solution Overview
Problem
Existing wireless communication systems face challenges in managing multiple AI/ML models for diverse use cases, lacking a unified method for device capability discovery and generalization, leading to inefficiencies in beam management and resource utilization.
Innovation Solution
A device capability discovery method is proposed for wireless communication devices, enabling them to transmit and receive capability messages to download and activate appropriate ML models from a pool, based on their complexity and capability, facilitating the deployment of multiple AI/ML models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional beam selection based on CSI-RS/SSB measurement is used, then beam management can be achieved, but reference signal consumption and delay increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models offline using historical CSI data to learn beam patterns and channel characteristics. This pre-computed knowledge is then deployed in the UE, enabling fast beam selection without requiring extensive real-time reference signal measurements, thus reducing delay while maintaining beam management reliability
Solution Approach 2:
The patent substitutes the traditional mechanical measurement-based beam selection system with an AI/ML-based predictive system. Instead of relying on real-time CSI-RS/SSB measurements and exhaustive beam sweeping, the system uses trained neural networks to predict optimal beams, replacing the measurement-heavy mechanical process with intelligent computation
2Adaptability or versatility
If multiple AI/ML models are deployed for different use cases, then model functionality and versatility improve, but device complexity and resource management become unclear
Solution Approach 1:
The patent applies segmentation by dividing the AI/ML model pool into distinct categories based on use cases (beam management, channel prediction, interference mitigation, etc.). Each model is independently trained and managed, allowing the system to selectively deploy only the models needed for specific scenarios, thereby reducing overall management complexity while maintaining versatility
Solution Approach 2:
The patent implements dynamic model management where the UE can adaptively select, activate, and switch between different AI/ML models based on current channel conditions, device capabilities, and service requirements. This dynamic approach allows the system to optimize resource usage by activating only necessary models, reducing complexity while preserving adaptability
3Adaptability or versatility
If a generalized AI/ML model is developed to work for all scenarios, then model portability improves, but model precision and scenario-specific performance deteriorate
Solution Approach 1:
The patent segments the AI/ML modeling approach into two layers: a generalized base model that captures universal channel characteristics and beam patterns, and scenario-specific fine-tuned models that optimize for particular use cases. This hierarchical segmentation allows the system to benefit from both generalization (for portability) and specialization (for precision) by combining the strengths of both approaches
Solution Approach 2:
The patent applies local quality by allowing different parts of the model system to have different levels of generalization. The base model maintains high generalization for broad applicability, while scenario-specific models provide localized optimization for particular conditions. This differentiated quality approach ensures that each model component operates at the appropriate level of specificity for its intended purpose
Data Source
AI summary
The disclosure provides a device capability discovery method and a wireless communication device. The wireless communication device transmits a capability message of the wireless communication device to a source device having a pool of machine learning (ML) models. The capability message shows whether the wireless communication device is capable of executing multiple ML models. The wireless communication device downloads if needed, and activates one or more ML models from a subset in the pool of ML models. The subset in the pool of ML models matches the capability message of the wireless communication device.