CSI Model Verification on UE Hardware for Dynamic Wireless Conditions
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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 insufficient performance monitoring and ineffective model updates, resulting in potential failures and suboptimal performance.
Innovation Solution
Implement proactive performance monitoring and post-deployment verification of AI/ML models on actual UE hardware, using key performance indicators to detect performance declines and switch to more suitable models, ensuring timely updates and improved model accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional AI/ML model validation techniques are used in wireless communication networks, then model deployment is simplified, but performance monitoring is insufficient and model accuracy deteriorates in dynamic environments
Solution Approach 1:
The system performs preliminary actions by deploying probe UEs to proactively monitor network performance metrics before actual service degradation occurs. These probe UEs continuously collect CSI, RSRP, and other KPIs in advance, enabling early detection of performance issues and proactive model validation before deployment to production UEs.
Solution Approach 2:
The patent introduces probe UEs as intermediary elements between the validation system and production UEs. These probe UEs serve as mediators that collect performance data, evaluate model accuracy, and provide feedback without directly impacting user services. The probe UEs act as test subjects that isolate validation activities from production operations.
2Measurement precision
If proactive performance monitoring is implemented using probe UEs, then model accuracy is maintained, but network complexity and deployment complexity increase
Solution Approach 1:
The probe UEs are designed with multi-functionality, serving both as regular user devices and as performance monitoring instruments. They execute validation workflows, collect CSI measurements, and evaluate model accuracy while maintaining normal communication functions. This universal design reduces the need for dedicated testing equipment and integrates monitoring capabilities into existing device architecture.
Solution Approach 2:
The system dynamically adjusts monitoring parameters such as CSI measurement configurations, reporting intervals, and evaluation thresholds based on network conditions and model deployment stages. Configuration parameters are modified to optimize measurement precision while managing network overhead, allowing flexible adaptation to different operational scenarios.
3Productivity
If AI/ML models are deployed without post-deployment verification, then deployment speed is increased, but model reliability and performance deteriorate
Solution Approach 1:
Post-deployment verification is performed as a preliminary action before full production deployment. The system first deploys AI/ML models to probe UEs for validation, conducts performance evaluations using collected KPIs, and only after successful verification proceeds to broader deployment. This staged approach maintains deployment speed while ensuring reliability through pre-approval testing.
Solution Approach 2:
The system implements continuous feedback loops where performance data collected from probe UEs is analyzed to evaluate model accuracy. This feedback mechanism provides real-time information about model performance, enabling automated validation decisions and iterative model improvements. The feedback system compares predicted versus actual performance metrics to determine whether models meet deployment criteria.
Data Source
AI summary
Described herein are solutions for dynamic model management and post-deployment verification for channel state information (CSI) and CSI feedback. 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.