AI Encoder Selection Using Data Distribution Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In wireless communication systems, the generalization problem between data-driven AI encoders and decoders leads to distortion in encoded information, affecting system performance due to non-identical distribution of training and transmission data.
Innovation Solution
An information encoding control method that configures multiple AI encoders and decoders, allowing the terminal to select an appropriate encoder based on configuration information, such as cosine similarity or statistical parameters, to ensure compatibility and avoid distortion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI encoder and AI decoder are used for information encoding and decoding, then transmission efficiency is improved, but information distortion occurs due to generalization problems when training data and transmission data are not identically distributed
Solution Approach 1:
The patent pre-calculates and stores statistical parameters (mean and covariance) of training data for multiple AI encoders before actual transmission. When encoding is needed, the system compares statistical parameters of current data with pre-stored parameters to select the most suitable AI encoder, avoiding the need to reprocess training data and ensuring consistent distribution characteristics between training and transmission data.
Solution Approach 2:
The patent changes the selection criterion from direct data comparison to statistical parameter comparison. By using mean and covariance matrices as selection criteria, the system can efficiently determine the most suitable AI encoder without processing the entire training dataset, while ensuring that the selected encoder's training data distribution matches the current transmission data distribution.
2Loss of information
If multiple AI encoders are configured to solve generalization problems, then information distortion is reduced, but storage pressure increases
Solution Approach 1:
The patent extracts only the essential statistical parameters (mean and covariance matrices) from the training data of multiple AI encoders and stores them in a database. This extraction approach retains the distribution characteristics needed for encoder selection while removing the bulk of the training data, significantly reducing storage requirements compared to storing complete training datasets.
Solution Approach 2:
The patent creates simplified statistical representations (mean and covariance matrices) as copies of the essential distribution characteristics of training data. These statistical copies are stored instead of the original training data, allowing the system to maintain multiple AI encoder configurations while minimizing storage pressure through compact statistical representations.
3Adaptability or versatility
If statistical parameters of multiple AI encoders are stored and compared in real-time, then appropriate encoder selection is achieved, but processing complexity increases
Solution Approach 1:
The patent pre-calculates and stores statistical parameters (mean and covariance matrices) of training data for multiple AI encoders before actual transmission. When encoding is needed, the system compares statistical parameters of current data with pre-stored parameters to select the most suitable AI encoder, avoiding the need to reprocess training data and ensuring consistent distribution characteristics between training and transmission data.
Solution Approach 2:
The patent replaces complex real-time data processing and distribution matching algorithms with simpler statistical parameter comparisons. By using pre-computed mean and covariance matrices as selection criteria, the system substitutes heavy computational mechanics with lightweight statistical comparisons, significantly reducing processing complexity while maintaining accurate encoder selection.
Data Source
AI summary
An information encoding control method includes: receiving first configuration information, where the first configuration information is used to configure N groups of parameters of N artificial intelligence (AI) encoders or an AI decider, and the AI decider is configured to determine an AI encoder that is in the N AI encoders and to which first information is applicable, and/or is configured to determine that none of the N AI encoders is applicable to encoding the first information; and sending first indication information to a network device, where the first indication information indicates that a first encoder is used for encoding the first information, the first encoder is determined based on the first configuration information and the first information, and the first encoder is an encoder in the N AI encoders, or is a second encoder different from the N AI encoders.