User Equipment Machine Learning Preconfiguration to Reduce Signaling Overhead
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Solution Overview
Problem
Existing methods for configuring user equipment (UE) for machine learning in wireless communications systems are inefficient and lack effective mechanisms for reducing signaling overhead and power consumption.
Innovation Solution
A network configures a UE by providing a neural network function and machine learning model along with corresponding parameters, enabling the UE to perform tasks with minimal network instruction through mechanisms like RRC signaling and a machine learning repository, allowing for reduced signaling and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the network provides detailed instructions and configurations to the UE for performing machine learning tasks, then the reliability and accuracy of task execution is improved, but the signaling overhead increases
Solution Approach 1:
The network pre-configures the UE with machine learning models, parameters, and task definitions before the UE needs to perform tasks. This preliminary configuration reduces the need for continuous detailed instructions during task execution, thereby reducing signaling overhead while maintaining execution reliability.
Solution Approach 2:
The patent introduces an intermediate configuration layer where the network provides high-level task definitions and parameters rather than detailed step-by-step instructions. This intermediary approach allows the UE to autonomously execute tasks using pre-configured machine learning models, reducing the amount of signaling required while maintaining reliability.
2Measurement precision
If the network continuously monitors and configures the UE for machine learning tasks, then the task execution accuracy is improved, but the power consumption at the UE increases
Solution Approach 1:
The network performs preliminary configuration of machine learning models and parameters before the UE executes tasks. This upfront configuration reduces the need for continuous monitoring and reconfiguration during task execution, thereby reducing power consumption while maintaining execution accuracy through the pre-optimized models.
Solution Approach 2:
The UE is equipped with autonomous machine learning capabilities that allow it to self-service and execute tasks independently using pre-configured models. This self-service approach reduces the need for continuous network monitoring and configuration updates, thereby reducing power consumption while maintaining accuracy through the pre-configured machine learning algorithms.
3Productivity
If the UE has full machine learning capabilities with all necessary models and parameters, then the task execution efficiency is improved, but the device complexity increases
Solution Approach 1:
The patent segments machine learning capabilities into modular components that can be selectively configured in the UE. Instead of implementing all possible machine learning models and parameters, the network configures only the specific models and parameters needed for particular tasks, thereby improving execution efficiency for those tasks while keeping device complexity manageable through selective configuration.
4Adaptability or versatility
If the network provides comprehensive machine learning configurations to the UE, then the adaptability of the UE to different tasks is improved, but the signaling overhead and configuration data size increase
Solution Approach 1:
The patent implements universal machine learning model structures that can adapt to multiple different tasks through parameter configuration rather than requiring separate models for each task. This multi-functionality approach allows the UE to handle diverse tasks with a single configurable framework, improving adaptability while reducing the overall configuration data size compared to providing separate comprehensive configurations for each task type.
Data Source
AI summary
Methods, systems, and devices for wireless communications are described. In some examples, a wireless communications system may support machine learning and may configure a user equipment (UE) for machine learning. The UE may transmit, to a base station, a request message that includes an indication of a machine learning model or a neural network function based at least in part on a trigger event. In response to the request message, the base station may transmit a machine learning model, a set of parameters corresponding to the machine learning model, or a configuration corresponding to a neural network function and may transmit an activation message to the UE to implement the machine learning model and the neural network function.