Latent Space Correction for Neural Network Feedback Compatibility
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Solution Overview
Problem
Existing communication systems face challenges in ensuring compatibility and accurate interpretation of artificial neural network-based feedback information, particularly in scenarios where the structure of the neural networks is not accurately shared between nodes, leading to discrepancies in CSI feedback operations.
Innovation Solution
The method involves determining a latent space correction operation by transforming latent data from one encoder to match the output of another, ensuring isometric transformation characteristics and using a total loss function to align neural networks, allowing for compatible feedback between communication nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If artificial neural networks are used for feedback operations, then feedback performance is improved, but compatibility between different nodes deteriorates due to undisclosed network structures
Solution Approach 1:
The patent introduces a reference data set as an intermediary that both communication nodes share and use to train their respective neural networks. This mediator enables compatibility without requiring disclosure of the actual network structures, allowing nodes to align their latent spaces through common reference data while maintaining their proprietary architectures
Solution Approach 2:
The patent applies parameter changes by transforming the latent data through learned transformation operations (such as rotation, scaling, or translation) that align different nodes' latent spaces. These parameter transformations enable compatibility adjustment without changing the fundamental network structures, resolving the contradiction between performance and compatibility
2Adaptability or versatility
If neural network structures are kept proprietary, then commercial value is protected, but accurate interpretation of feedback information deteriorates
Solution Approach 1:
The patent extracts only the essential compatibility information (reference data sets and transformation operations) from the proprietary neural networks while leaving the core network structures undisclosed. This extraction enables accurate feedback interpretation without requiring full structural disclosure, protecting commercial assets while improving measurement precision
Solution Approach 2:
The patent uses reference data sets as simplified copies or representations of the actual feedback information that can be shared between nodes. These copied data sets enable accurate interpretation without exposing the proprietary network structures, maintaining commercial value while improving interpretation accuracy
Data Source
AI summary
An operation method of a first communication node may comprise: determining a latent space correction operation including a transformation operation for correcting latent data output from a first encoder of a first artificial neural network corresponding to the first communication node, based on information of a reference data set provided from a second communication node; encoding first input data including first feedback information through the first encoder; correcting first latent data output from the first encoder based on the determined latent space correction operation; and transmitting a first feedback signal including the corrected first latent data to the second communication node, wherein the corrected first latent data is decoded into first output data corresponding to the first input data in a second decoder of a second artificial neural network corresponding to the second communication node.