User Equipment Communication Model Adaptation via Parameter Updates
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Solution Overview
Problem
Current wireless communication systems face challenges in efficiently integrating machine learning to optimize communication models across varying environments, limiting their ability to adapt and improve performance.
Innovation Solution
The implementation of a method where user equipment requests base stations to change parameter values, allowing for data collection and optimization of communication models using machine learning techniques, enabling the system to adapt to different environments and improve performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning is integrated into wireless communication systems to optimize communication models, then communication performance and adaptability are improved, but system complexity increases
Solution Approach 1:
The patent introduces a communication model as an intermediary layer between the wireless communication system and machine learning algorithms. This model includes parameters that can be adjusted based on ML predictions, allowing the system to adapt to varying communication environments without directly embedding complex ML algorithms in the communication protocol, thus balancing adaptability improvement with system complexity management
Solution Approach 2:
The patent employs parameter changes by introducing a set of adjustable parameters in the communication model that can be modified based on machine learning predictions. These parameters control aspects such as resource allocation, transmission power, and scheduling decisions, enabling the system to adapt to different communication conditions without fundamentally changing the system architecture
2Productivity
If parameter values are changed frequently to collect training data for communication models, then model optimization capability is improved, but communication overhead increases
Solution Approach 1:
The patent applies partial action by selectively changing only certain parameters that are most critical for model training while keeping other parameters stable. This approach allows the system to collect sufficient training data without requiring complete parameter reconfiguration, thereby reducing the time and overhead associated with frequent parameter changes while still enabling effective model optimization
Data Source
AI summary
The devices, systems, and techniques described herein provide for efficient integration of machine learning techniques into wireless communication system frameworks. User equipment may perform communication with base stations based on communication models generated through machine learning. Data may be collected from various communication environments to optimize communication models (e.g., to train communication models, provide inputs to communication models, etc.). According to embodiments of the present disclosure, user equipment may update parameters (e.g., parameters for controlling wireless communication systems) to provide the user equipment with various data (e.g., data subsequent to modifying parameters of the wireless communication system) for enabling effective training and implementation of communication models that are implemented based on machine learning. Accordingly, user equipment may efficiently manage communication models based on various data, and user equipment may thus perform communication operations within wireless communication systems with improved performance (e.g., based on the generated and updated optimal communication models).