Beam Management Spatial Prediction With Post-Deployment Model Verification
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Solution Overview
Problem
Current AI/ML model validation techniques in wireless communication networks are inadequate for dynamic and complex environments, leading to inefficient beam management and failure detection, as they fail to account for the vast number of real-world scenarios and do not provide proactive performance monitoring.
Innovation Solution
Implementing proactive performance monitoring and post-deployment verification of AI/ML models for beam management, using UE hardware to predict and evaluate Tx/Rx beam pairs, and monitor KPIs to ensure accurate beam selection and update models before failure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional AI/ML model validation techniques are used, then the validation process is simple, but the model fails to account for vast number of real-world scenarios and does not provide proactive performance monitoring
Solution Approach 1:
The patent implements post-deployment verification that proactively monitors model performance in real-world scenarios before actual failure occurs. The system continuously validates model predictions against actual network measurements and triggers retraining when performance degradation is detected, rather than relying solely on pre-deployment validation.
Solution Approach 2:
The system establishes a feedback loop where model predictions are continuously compared with actual network measurements (RSRP, SINR, beam quality). This feedback mechanism enables proactive detection of performance degradation and triggers automated retraining, improving reliability through continuous verification.
2Productivity
If proactive performance monitoring is implemented, then beam management efficiency is enhanced, but the system complexity increases
Solution Approach 1:
The system performs self-verification by automatically monitoring its own model performance using real network measurements. The UE hardware itself generates the verification data by comparing predicted beam qualities with actual measurements, eliminating the need for external validation infrastructure and reducing overall system complexity.
Solution Approach 2:
The same UE hardware that performs normal beam management operations is also used for model verification. The system uses existing measurement capabilities (RSRP, SINR, beam quality measurements) for dual purposes: normal operation and model validation, avoiding additional dedicated hardware.
3Reliability
If model updates are performed timely based on performance monitoring, then communication reliability is maintained, but the update frequency and processing load increase
Solution Approach 1:
The system performs partial validation by focusing monitoring on key performance indicators (beam quality, RSRP, SINR) rather than exhaustive testing. Model retraining is triggered only when performance degradation thresholds are exceeded, avoiding unnecessary updates and reducing energy consumption while maintaining reliability.
Data Source
AI summary
Described herein are solutions for dynamic model management and post-deployment verification for beam management spatial prediction. A user equipment (UE) can receive multiple artificial intelligence (AI)/machine learning (ML) models from an over-the-air (OTA) server. UE can deploy, monitor, and evaluate active and inactive AI/ML models according to one or more key performance indicators (KPIs). Examples of the KPIs can include input data and conditions associated with the AI/ML model, a distribution of output data produced by the AI/ML model, and an inference accuracy of the AI/ML model. UE 210 can determine that an AI/ML model is verified when KPIs are satisfied. These and many other features and examples are described herein.