Neural Network Parameter Selection for Accurate CSI Feedback
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Solution Overview
Problem
Existing technologies face challenges in accurately applying Artificial Intelligence (AI) for channel state information (CSI) feedback in multi-antenna techniques, leading to inefficiencies in wireless communication systems.
Innovation Solution
A method and apparatus for transmitting network parameters that involve acquiring, selecting, and transmitting neural network parameters based on channel features to enhance CSI feedback, using AI to better match current channel conditions and improve communication quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional CSI feedback methods are used in multi-antenna systems, then system compatibility is maintained, but CSI accuracy and wireless communication quality are insufficient
Solution Approach 1:
The patent transforms traditional CSI feedback parameters into neural network parameter sets that can be dynamically selected and updated. By changing from fixed feedback parameters to adaptive neural network parameters, the system achieves both improved CSI accuracy and better AI technique applicability in multi-antenna systems.
Solution Approach 2:
The patent replaces traditional mechanical CSI feedback mechanisms with AI-based neural network parameter transmission. This substitution enables the system to leverage AI's strong feature extraction and classification abilities, significantly improving CSI acquisition accuracy while maintaining system compatibility through standardized parameter transmission protocols.
2Extent of automation
If AI techniques are applied to CSI feedback, then feature extraction ability is enhanced, but system complexity increases
Solution Approach 1:
The patent segments the AI-based CSI feedback system into modular components: neural network parameter acquisition modules, parameter set selection modules, and parameter transmission modules. This segmentation allows each component to perform specific functions independently, reducing overall system complexity while maintaining AI's automated feature extraction capabilities.
Solution Approach 2:
The patent designs universal neural network parameter sets that can be applied across different multi-antenna configurations and scenarios. These parameter sets serve multiple functions including channel state estimation, quality prediction, and communication optimization, thereby reducing system complexity through multi-functionality rather than requiring separate AI models for each function.
3Measurement precision
If neural network parameters are transmitted frequently, then CSI feedback accuracy is improved, but communication resource consumption increases
Solution Approach 1:
The patent implements periodic updates of neural network parameter sets based on channel condition changes rather than continuous transmission. The system selectively transmits updated parameter sets only when channel characteristics change significantly, thereby maintaining high CSI feedback accuracy while reducing communication resource consumption through periodic rather than continuous parameter updates.
Solution Approach 2:
The patent employs dynamic parameter set selection where the system adapts the transmission frequency and content of neural network parameters based on real-time channel conditions. When channels are stable, parameter updates are reduced; when channels change rapidly, updates are increased, optimizing the balance between CSI accuracy and resource consumption dynamically.
Data Source
AI summary
A network parameter set information transmission method and apparatus, a terminal, a base station, and a storage medium are disclosed. The method may include, acquiring at least one set of neural network parameters; selecting at least one set of neural network parameters from the at least one set of neural network parameters according to a channel feature; and transmitting information of the set of network parameters corresponding to the selected set of neural network parameters.