Machine Learning Repeater Control for Lower-Overhead UE Switching
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Solution Overview
Problem
Existing wireless communication systems face inefficiencies and increased power consumption when switching repeater configurations due to the need for time-consuming measurements and reporting procedures, which can affect communication reliability and efficiency.
Innovation Solution
Implementing user equipment (UE) with machine learning (ML) algorithms to predict communication parameters based on repeater states, allowing for efficient and timely adjustments to repeater configurations, thereby reducing computational and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional measurement and reporting procedures are used for repeater configuration switching, then communication reliability is maintained, but processing time and power consumption increase
Solution Approach 1:
The system performs preliminary actions by pre-configuring multiple repeater configurations and using machine learning algorithms to predict optimal configurations before actual communication needs arise. This allows the UE to have prediction results ready, eliminating the need for time-consuming measurements and reporting procedures when configuration switching is actually needed.
Solution Approach 2:
The patent replaces the traditional mechanical measurement and reporting system with a machine learning-based prediction system. Instead of using conventional measurement procedures to determine optimal repeater configurations, the system uses ML algorithms trained on historical data to predict optimal configurations, significantly reducing processing time while maintaining reliability.
2Reliability
If traditional measurement and reporting procedures are used for repeater configuration switching, then accurate configuration selection is achieved, but power consumption increases
Solution Approach 1:
The machine learning models are trained in advance using historical communication data and repeater configuration outcomes. This preliminary training phase allows the system to make accurate configuration predictions without requiring energy-intensive real-time measurements and reporting procedures, thus reducing power consumption while maintaining accuracy.
Solution Approach 2:
The system employs self-service mechanisms where the machine learning models autonomously predict optimal repeater configurations based on current communication conditions and historical patterns. This eliminates the need for energy-consuming manual measurement and reporting procedures, as the system serves itself by making intelligent predictions without external intervention.
3Productivity
If machine learning algorithms are implemented at UE, then processing speed and adaptability improve, but device complexity increases
Solution Approach 1:
The system segments the machine learning functionality into distinct components: configuration prediction models, parameter selection algorithms, and integration interfaces with existing communication protocols. This segmentation allows the complex ML functionality to be implemented in a modular manner, managing device complexity while maintaining high processing speed and adaptability.
Solution Approach 2:
The machine learning framework is designed to be universal and multi-functional, capable of handling various repeater configuration scenarios and prediction tasks through a single integrated system. This universality reduces overall device complexity compared to implementing separate specialized systems for each function, while still providing high processing speed and adaptability across different communication conditions.
Data Source
AI summary
Methods, systems, and devices for wireless communications are described that provide for a user equipment (UE) to be configured with one or more machine learning (ML) algorithms for predicting communications parameters with a network entity via one or more repeaters that may have multiple different repeater configurations. The UE may select a ML algorithm, select one or more parameters for input to a ML algorithm, process an output of a ML algorithm, or any combination thereof, based on a state or status of one or more repeaters that are used for communications with the network entity. A UE also may request a change in a repeater configuration based on one or more predicted channel characteristics that indicate a configuration change will enhance channel conditions.