Neural Channel Feedback for Low-Error CSI Quantization
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Solution Overview
Problem
In Frequency-Division Duplex (FDD) communications systems, the quantization error in channel matrix feedback leads to inaccurate channel information feedback due to the use of codebooks, which cannot exactly match the dynamically changing downlink channel states, resulting in increased feedback overheads and reduced accuracy.
Innovation Solution
Employing neural networks to determine channel negotiation information, where the neural network parameters are related to historical modulation and coding schemes, reducing quantization errors and improving feedback accuracy by dynamically updating based on decision revenues and feedback errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If codebook-based quantization is used for channel matrix feedback, then feedback overhead is reduced, but quantization error increases and feedback accuracy deteriorates
Solution Approach 1:
The patent transforms the channel matrix parameters through neural network processing to generate channel negotiation information. The neural network learns optimal parameter transformations from historical data, enabling accurate channel state representation with reduced feedback dimensions. This resolves the contradiction by changing how channel parameters are encoded and transmitted.
Solution Approach 2:
The patent replaces the traditional codebook-based quantization mechanism with a neural network-based channel negotiation information generation mechanism. Instead of mapping channel matrices to discrete codebook indices, the system uses continuous neural network transformations guided by historical MCS data, achieving better accuracy without excessive overhead.
2Measurement precision
If codebook-based quantization is used, then feedback overhead is reduced, but the determined codeword cannot exactly match the downlink channel
Solution Approach 1:
The patent incorporates historical modulation and coding scheme data as feedback to the neural network for generating channel negotiation information. This feedback mechanism allows the system to learn from past channel conditions and optimize the mapping between channel states and negotiation information, reducing quantization error and improving measurement precision.
Solution Approach 2:
The patent performs preliminary processing of channel matrices through neural network transformations before feedback transmission. By pre-computing optimal channel negotiation information based on historical patterns, the system prepares more accurate representations in advance, reducing the need for high-precision quantization during real-time operation.
3Measurement precision
If neural network parameters are updated based on historical MCS and decision revenue, then feedback accuracy improves, but system complexity increases
Solution Approach 1:
The patent enables the neural network parameters to be automatically updated using historical MCS data and decision revenue feedback without requiring manual intervention or complex external optimization systems. The network self-adjusts its parameters based on accumulated operational data, improving accuracy while keeping the implementation relatively simple through automated learning.
Data Source
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AI summary
This application discloses a channel information feedback method, a communications apparatus, and a storage medium, and relates to the communications field. In this application, a network device may send a channel state information-reference signal CSI-RS to a terminal by using a downlink channel. The terminal may perform measurement estimation to obtain a channel matrix of the downlink channel based on the received CSI-RS, determine, by using a first neural network and based on the channel matrix of the downlink channel, channel negotiation information CNI corresponding to the downlink channel, and send the CNI corresponding to the downlink channel to the network device. The network device may determine, based on the received CNI corresponding to the downlink channel, a modulation and coding scheme MCS corresponding to the downlink channel. A parameter of the first neural network is related to a historical MCS. In this application, a quantization error when the terminal quantizes the channel matrix can be reduced, and accuracy of channel information feedback can be effectively improved. In addition, information entropy of the CNI corresponding to the downlink channel determined by the terminal by using the first neural network is relatively small, so that efficiency of channel information feedback can be further improved.