Two-Sided AI Model for CSI Compression and Reconstruction
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently compressing and transmitting large measurement reports, such as CSI reports, which results in reduced accuracy and increased resource consumption.
Innovation Solution
Implementing a two-sided AI/ML model architecture comprising a UE component and a network entity component, where UEs are categorized into groups based on characteristics, and trained models are used to compress and extract CSI-related data, utilizing scalar or vector quantization to reduce data exchange while maintaining fidelity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional compression methods are used for CSI reports, then data size is reduced, but measurement precision and data fidelity deteriorate
Solution Approach 1:
The patent transforms the compression approach by changing parameters from traditional lossy compression to lossless AI-based reconstruction. The encoder compresses CSI data into a condensed representation, and the AI decoder model reconstructs the original data with high fidelity, achieving both size reduction and precision maintenance through intelligent parameter transformation rather than conventional compression algorithms
Solution Approach 2:
The patent introduces an AI decoder model as an intermediary component between the compressed data and the final CSI output. This intermediary reconstructs the original data from the compressed representation, enabling the system to achieve both compression benefits and data fidelity requirements that traditional direct compression methods cannot simultaneously satisfy
2Measurement precision
If more data is exchanged for higher accuracy, then measurement precision improves, but device complexity and resource consumption increase
Solution Approach 1:
The patent extracts only the essential information needed for CSI representation through the encoder, discarding redundant data while preserving critical characteristics. The AI decoder then reconstructs the necessary CSI data from this extracted information, achieving high measurement precision with significantly reduced data exchange and lower device resource consumption
Solution Approach 2:
The patent creates a compressed copy of the CSI data through the encoder that contains the essential information in a condensed format. The AI decoder then generates an accurate reconstruction from this copy, achieving high fidelity without requiring the transmission of the complete original data, thus reducing resource consumption
3Adaptability or versatility
If AI models are trained for different UE groups, then adaptability improves, but device complexity increases
Solution Approach 1:
The patent segments the UE population into different groups based on their characteristics and trains separate AI models for each group. This segmentation allows the system to achieve high adaptability to different UE types while managing complexity through organized model storage and selection mechanisms, where each group has its dedicated optimized model rather than a single universal model
Data Source
AI summary
Various aspects of the present disclosure relate to methods, apparatuses, and systems that support operation of a two-sided model. For instance, implementations provide an architecture for exchanging data (e.g., channel state information (CSI)-related data) between different devices such as user equipment (UE) and network entities. In at least some implementations the architecture is composed of multiple components, such as a UE component and a network entity component. A UE component and a network entity component, for example, represent a two-sided model that can be implemented to compress and extract data such as CSI-related data. Accordingly, the present disclosure supports training of two-sided models such as based on different UE types with different characteristics.