AI-Enhanced Codebook CSI Feedback in Wireless Terminals
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Solution Overview
Problem
Current wireless communication systems face challenges in efficiently transmitting and receiving enhanced codebook-based channel state information (CSI) between base stations and terminals, particularly in next-generation mobile communication systems that require advanced channel state feedback mechanisms.
Innovation Solution
A method and device for transmitting and receiving enhanced codebook-based CSI, where a terminal receives CSI-reference signals from the network, and transmits CSI reports including information about a first precoding matrix from a predetermined codebook and a second precoding matrix determined using artificial intelligence (AI)/machine learning (ML) model-related information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If codebook-based CSI feedback is used in wireless communication systems, then channel state information can be transmitted efficiently, but the accuracy and adaptability of channel state estimation is limited by pre-defined codebooks
Solution Approach 1:
The patent segments the precoding matrix selection process into two parts: (1) selecting a base precoding matrix from a predefined codebook, and (2) generating enhanced precoding matrices by combining the base matrix with AI/ML model outputs. This segmentation allows the system to maintain codebook-based efficiency while adding AI-enhanced precision through the combination of structured codebook search and AI-generated refinements.
Solution Approach 2:
The patent changes the parameter space from purely predefined codebook entries to a hybrid space that includes AI/ML model outputs as additional parameters. The terminal determines a second precoding matrix by combining the first codebook-based matrix with AI/ML model-related information, effectively expanding the parameter search space beyond traditional codebook limitations while maintaining feedback efficiency.
2Measurement precision
If AI/ML models are integrated into CSI feedback mechanisms, then channel state estimation accuracy improves, but device complexity increases
Solution Approach 1:
The patent applies partial action by using AI/ML models only for generating enhanced precoding matrices after an initial codebook-based selection, rather than using AI/ML for the entire CSI feedback process. The terminal first selects a base precoding matrix from the codebook (simpler operation) and then applies AI/ML enhancement only when needed, reducing overall device complexity while maintaining accuracy improvements.
Solution Approach 2:
The patent introduces an intermediary approach where the AI/ML model processes channel state information separately from the traditional codebook-based feedback mechanism. The AI/ML component acts as an intermediary that refines the initial codebook selection, allowing the terminal to leverage AI capabilities without completely redesigning the existing feedback structure, thus managing complexity.
3Adaptability or versatility
If enhanced codebook-based CSI feedback is implemented, then adaptability to complex channel conditions improves, but feedback processing time increases
Solution Approach 1:
The patent applies preliminary action by performing the AI/ML model processing in advance or during low-activity periods to generate enhanced precoding matrices before they are needed for actual CSI feedback transmission. This allows the computationally intensive AI/ML operations to be completed beforehand, reducing the real-time processing time during critical feedback transmission windows while maintaining high adaptability.
Solution Approach 2:
The patent implements periodic action by scheduling AI/ML model executions at specific intervals or triggered by certain channel conditions rather than continuously. The terminal periodically updates the enhanced precoding matrices using AI/ML models, balancing adaptability to changing channel conditions with processing time constraints by activating the computationally intensive operations only when necessary.
Data Source
AI summary
A method and device for transmitting or receiving improved codebook-based channel state information in a wireless communication system are disclosed. A method performed by a terminal in a wireless communication system, according to one embodiment of the present disclosure, comprises the steps of: receiving one or more channel state information (CSI)-reference signals (RS) from a network; and transmitting, to the network, one or more CSI reports including information indicating a first precoding matrix based on the one or more CSI-RSs and a channel quality indicator (CQI) based on a second precoding matrix, wherein the first precoding matrix is included in a set of precoding matrices defined on the basis of one or more predetermined codebooks, and the second precoding matrix can be determined on the basis of the first precoding matrix and one or more pieces of artificial intelligence (AI)/machine learning (ML) model-related information.