Perception-Aided Wireless Channel Prediction With ML Lifecycle Control
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Solution Overview
Problem
Wireless communications systems face challenges in maintaining accurate and reliable channel predictions due to environmental changes, such as construction or remodeling, which affect the performance of ML models used for perception-aided wireless communications.
Innovation Solution
Implementing lifecycle management (LCM) schemes for ML models trained on perception information, where a UE reports performance metrics to a network entity, enabling actions like retraining or deactivation based on environmental compatibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an ML model is trained to predict channel properties in a specific environment, then prediction accuracy is improved for that environment, but the model becomes incompatible when the environment changes or the device moves to a different environment
Solution Approach 1:
The patent implements dynamic ML model selection and lifecycle management where the network entity monitors environment changes and device movements, automatically updating or switching ML models to maintain prediction accuracy across varying conditions. This transforms the static model into a dynamic system that adapts to environmental changes.
Solution Approach 2:
The patent changes the parameters of the ML model by updating training data and model configurations based on environmental feedback. The network entity receives performance metrics and environment information, then retrains or adjusts model parameters to maintain accuracy when environments change.
2Reliability
If continuous monitoring and updating of ML models is performed to maintain accuracy in changing environments, then prediction reliability is improved, but system complexity and processing overhead increase
Solution Approach 1:
The patent implements a feedback mechanism where the UE reports performance metrics and environment information to the network entity, which then determines whether model updates are needed. This feedback loop maintains reliability by only triggering updates when necessary, reducing unnecessary processing overhead.
Solution Approach 2:
The system performs self-service by automatically monitoring performance, detecting when updates are needed, and executing model updates without manual intervention. This automation reduces operational complexity while maintaining high reliability through continuous self-optimization.
3Productivity
If ML model predictions are used for wireless communication decisions, then communication efficiency is improved, but energy consumption increases due to continuous model operation and updating
Solution Approach 1:
The patent implements periodic model updating based on triggered events such as performance degradation thresholds or environment change detections, rather than continuous updating. This periodic approach maintains communication efficiency while significantly reducing energy consumption by activating model operations only when necessary.
Solution Approach 2:
The patent dynamically adjusts model operation parameters such as update frequency and prediction intervals based on current communication conditions and performance metrics. This allows the system to optimize the balance between communication efficiency and energy consumption by scaling model operations to actual needs.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for perception-aided wireless communications. An example method for wireless communications by an apparatus includes obtaining a first configuration that indicates to predict at least one channel property, based at least in part on perception information, using a first machine learning (ML) model; communicating, via at least one communication channel, based at least in part on a prediction of one or more channel properties associated with the at least one communication channel, wherein the prediction of the one or more channel properties is obtained via the first ML model; and sending an indication of one or more performance metrics associated with predicting the one or more channel properties via the first ML model.