AI-Based CSI Prediction From Historical Channel Information
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Solution Overview
Problem
Existing methods for processing and feeding back channel state information (CSI) in wireless communication systems are inefficient, particularly in predicting future CSI based on historical information, leading to suboptimal channel transmission quality.
Innovation Solution
A method involving AI-based processing of M pieces of channel information to determine and feed back channel state information, utilizing neural networks to enhance CSI prediction and transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AI-based processing is used to predict channel state information, then prediction accuracy and channel transmission quality are improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent segments the channel information processing by dividing M future channel information pieces into multiple groups, where each group is processed independently to generate corresponding CSI. This segmentation reduces the computational complexity of processing all M pieces simultaneously while maintaining prediction accuracy, as each subgroup can be handled with less computational resources.
Solution Approach 2:
The patent performs preliminary processing by acquiring and storing N historical channel information pieces before prediction is needed. This preliminary action allows the AI model to be pre-trained or pre-configured with historical patterns, enabling more efficient real-time prediction when actual CSI generation is required, thus reducing online computational complexity.
2Reliability
If M pieces of channel information are processed to generate CSI, then channel transmission quality is improved, but processing time and system overhead increase
Solution Approach 1:
By segmenting M channel information pieces into multiple smaller groups, the patent enables parallel processing of each group to generate corresponding CSI simultaneously. This segmentation approach reduces the sequential processing time while maintaining the overall reliability by ensuring each group is adequately processed.
Solution Approach 2:
The patent applies partial action by processing only N historical channel information pieces to predict M future pieces, rather than processing all possible historical data. This selective partial processing achieves sufficient prediction accuracy for reliable channel transmission while significantly reducing the processing time and computational overhead.
3Measurement precision
If AI models are deployed for CSI prediction, then feature extraction capability is improved, but energy consumption increases
Solution Approach 1:
The patent segments the AI processing task by dividing channel information into multiple groups that can be processed separately. This segmentation allows the system to activate only the necessary AI model components for each group, reducing overall energy consumption while maintaining effective feature extraction capability across all channel information pieces.
Solution Approach 2:
The patent uses partial action by applying AI processing only to the essential N historical channel information pieces needed for prediction, rather than processing all available data with full AI complexity. This selective partial processing achieves adequate feature extraction for accurate CSI prediction while significantly reducing energy consumption compared to exhaustive AI processing.
Data Source
AI summary
A method for determining channel state information includes acquiring M pieces of channel information according to N pieces of channel information; determining channel state information corresponding to the M pieces of channel information; and feeding back the channel state information corresponding to the M pieces of channel information. Here N is an integer greater than 1, and M is an integer greater than or equal to 1.