CNN-LSTM Precoding Matrix Determination for Low-Overhead m-MIMO Feedback
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Solution Overview
Problem
Existing massive Multiple-Input Multiple-Output (m-MIMO) systems face high computational complexity and inefficiencies in precoding matrix determination due to large feedback overheads and inadequate consideration of channel state information variations.
Innovation Solution
A method involving a first sub-network at the UE side using a convolutional neural network and LSTM to compress channel estimation information into low-dimensional channel characteristic information, and a second sub-network at the base station to determine precoding matrices based on this compressed data, optimizing precoding through spatiotemporal correlation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the UE feeds back full-dimensional channel matrix information to the base station, then the precision of precoding matrix determination is improved, but the feedback overhead and computational complexity increase significantly
Solution Approach 1:
The patent extracts only the essential channel characteristic information from the complete channel matrix. Instead of feeding back the entire channel matrix, the UE processes the channel estimation information through a neural network to extract compressed channel characteristic information that captures the most important spatial and temporal characteristics. This extraction principle reduces feedback overhead while preserving the essential information needed for accurate precoding matrix determination.
Solution Approach 2:
The patent transforms the channel information from high-dimensional raw channel matrix data to low-dimensional channel characteristic parameters through neural network processing. The dimensionality reduction converts complex channel state information into compact parameter representations that maintain the critical spatial and temporal correlations needed for precoding, thereby reducing feedback overhead without significantly compromising determination precision.
2Measurement precision
If the base station processes complete channel estimation information, then the accuracy of precoding matrix determination is improved, but the computational complexity increases significantly
Solution Approach 1:
The patent applies preliminary processing at the UE side by using a neural network to compress channel estimation information into channel characteristic information before transmission. This preliminary compression action reduces the amount of data that needs to be processed at the base station, thereby lowering computational complexity while maintaining the essential information needed for accurate precoding matrix determination.
Solution Approach 2:
The base station extracts precoding matrices from the compressed channel characteristic information using a second neural network. This extraction process operates on low-dimensional data rather than complete channel matrices, significantly reducing computational complexity while maintaining determination accuracy through the preserved spatial and temporal correlations in the compressed representation.
3Quantity of substance
If traditional channel compression methods are used, then the feedback overhead is reduced, but the ability to capture spatiotemporal correlation is insufficient
Solution Approach 1:
The patent replaces traditional mechanical or mathematical channel compression methods with neural network-based compression. The first neural network at the UE and the second neural network at the base station learn and capture complex spatiotemporal correlations in channel data that traditional methods cannot effectively model. This substitution enables accurate representation of channel characteristics in compressed form, maintaining reliability while reducing feedback overhead.
Solution Approach 2:
The neural networks transform channel information into a different parameter space that efficiently captures spatiotemporal correlations. By learning optimal parameter representations through training, the system compresses channel data while preserving the essential correlation structures, achieving both reduced feedback overhead and maintained reliability in capturing channel characteristics.
Data Source
AI summary
A precoding matrix determination method, performed by a base station, includes: obtaining channel characteristic information qk,t and a compression rate parameter γ from a user equipment (UE), in which k is used to indicate a kth UE and t is used to indicate a timestamp; and determining precoding matrices Ft and Wt based on the compression rate parameter γ and the channel characteristic information qk,t.