Givens Rotation Matrix Parameterization for CSI Feedback
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Solution Overview
Problem
Current communication networks face inefficiencies in configuring and transmitting data related to AI/ML modeling and data communications between user equipment and networks.
Innovation Solution
The implementation of Givens rotation (GR) matrix parameterization pre-processing for channel state information feedback enhancement, which involves transforming channel measurement data into GR matrix parameter data and encoding it using AI models to generate CSI feedback data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional CSI feedback methods are used, then channel state information can be transmitted, but signaling load and bandwidth consumption increase
Solution Approach 1:
The patent transforms channel measurement data into Givens rotation matrix parameter data, changing the parameter representation from traditional CSI formats to GR matrix parameters. This parameter transformation enables more efficient encoding using AI models, reducing the amount of signaling data while preserving channel state information quality.
Solution Approach 2:
The patent replaces traditional mechanical encoding methods with AI-based encoding models. The AI model learns optimal encoding strategies for GR matrix parameter data, substituting conventional signaling mechanisms with intelligent, adaptive encoding that reduces bandwidth consumption while maintaining information fidelity.
2Measurement precision
If more data is transmitted for AI/ML modeling, then model accuracy improves, but transmission efficiency decreases
Solution Approach 1:
The patent extracts only the essential features needed for AI/ML modeling by transforming channel measurements into GR matrix parameters. This extraction process removes redundant information while retaining the critical components necessary for accurate channel state estimation, enabling efficient transmission with sufficient model training data.
Solution Approach 2:
The patent performs pre-processing of channel measurement data into GR matrix parameter format before transmission. This preliminary action prepares the data in an optimized structure that AI models can process efficiently, reducing the amount of data that needs to be transmitted while ensuring the preprocessed data contains all necessary information for accurate modeling.
3Productivity
If traditional encoding methods are used, then implementation is simple, but encoding efficiency is low
Solution Approach 1:
The patent replaces traditional mechanical encoding algorithms with AI-based encoding models. These AI models learn optimal encoding strategies from training data and automatically adapt to different channel conditions, achieving superior encoding efficiency compared to conventional methods while managing complexity through learned patterns rather than complex rule-based systems.
Solution Approach 2:
The patent changes the encoding approach by transforming CSI data into GR matrix parameters and applying AI-based encoding. This parameter transformation enables the use of neural network models that can efficiently encode the transformed data, achieving higher encoding efficiency by operating in the transformed parameter space rather than traditional CSI domains.
Data Source
AI summary
Methods, systems, apparatuses, and computer program products are provided for Givens rotation (GR) matrix parameterization pre-processing for channel state information feedback enhancement in a communication network. In this regard, channel measurement data related to a user equipment is transformed into Givens rotation (GR) matrix parameter data. Additionally, the GR matrix parameter data is encoded using at least one artificial intelligence (AI) model to generate channel state information (CSI) feedback data associated with the channel measurement data.