CSI Compression Model Selection for Private AI Decompression
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Solution Overview
Problem
Existing wireless communication technologies face challenges in deploying and transmitting Artificial Intelligence (AI) models for Channel State Information (CSI) enhancement due to the need for privatization of model parameters and data, which are not adequately addressed.
Innovation Solution
A method and device for determining a compression model that allows terminals to compress CSI using model parameters received from a network device, enabling the use of privatized data and ensuring compatibility with different manufacturers' systems by adjusting or training the compression model based on terminal capabilities and parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI models are deployed for CSI enhancement, then communication performance is improved, but model parameter privacy and data privatization requirements create deployment complexity
Solution Approach 1:
The patent segments the AI model into two separate components: a compression model deployed at the terminal and a decompression model deployed at the network device. This segmentation allows each component to be independently optimized and deployed, reducing overall deployment complexity while maintaining communication performance benefits.
Solution Approach 2:
The patent introduces an intermediary mechanism where the terminal compresses CSI using its local compression model before transmission, and the network device decompresses using its decompression model. This intermediary compression-decompression process enables privacy-preserving CSI enhancement without requiring direct model parameter sharing.
2Reliability
If compression models are trained with privatized data, then model privacy is maintained, but compatibility across different manufacturers' systems becomes challenging
Solution Approach 1:
The patent designs universal compression and decompression model interfaces that can work across different manufacturers' systems. The models use standardized input-output formats and can be trained on privatized data while maintaining compatibility through common protocol definitions and parameter structures.
Solution Approach 2:
The patent enables adaptability across different systems by allowing model parameters to be adjusted or retrained based on specific terminal capabilities and frequency bands. The compression model can be configured with different parameters for different scenarios while maintaining the same fundamental architecture, ensuring both privacy and compatibility.
3Adaptability or versatility
If compression model parameters are adjusted for different terminal capabilities, then system adaptability is improved, but model configuration complexity increases
Solution Approach 1:
The patent implements dynamic model configuration where the compression model parameters are automatically adjusted based on terminal capabilities and operating conditions. The system can dynamically select or reconfigure model parameters without requiring complex manual configuration, adapting to different frequency bands and terminal types automatically.
Data Source
AI summary
A method for determining a compression model for compressing channel state information, performed by a terminal, includes: receiving model information sent by a network device, the model information comprising a first model parameter of a decompression model, and the decompression model being used by the network device to decompress the channel stage information sent by the terminal; and determining the compression model used by the terminal to compress the channel state information based on the model information.