Antenna Array Channel Processing with Averaged Autocorrelation Matrices
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Solution Overview
Problem
Conventional cell split solutions suffer from large errors in channel information feedback, leading to low weight value performance and data processing efficiency due to quantization errors in channel information fed back by terminals.
Innovation Solution
A data processing method that involves obtaining a first matrix from reference signals, averaging channels in different rows of an antenna array to obtain a second matrix with reduced dimensions, and using a transformation matrix to calculate a third matrix for improved weight value performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If channel information is quantized for feedback, then feedback overhead is reduced, but measurement precision deteriorates due to large errors between quantized and real channel information
Solution Approach 1:
The patent uses an autocorrelation matrix to represent and process channel information instead of direct quantization. By computing the autocorrelation matrix from channel estimates and then averaging it to reduce dimensionality, the system maintains precise channel characteristics while enabling efficient feedback and processing, avoiding the precision loss inherent in direct quantization methods
Solution Approach 2:
The patent transforms channel information from its original high-dimensional form into an autocorrelation matrix representation, then further transforms it by averaging to reduce dimensionality. This parameter transformation approach preserves essential channel characteristics while reducing feedback overhead, achieving both low overhead and high precision simultaneously
2Measurement precision
If full-dimensional channel information is processed, then measurement precision is maintained, but device complexity increases due to large matrix dimensions and high calculation complexity
Solution Approach 1:
The patent extracts the essential characteristics of channel information by computing the autocorrelation matrix, which captures the statistical properties of the channel. By then averaging this matrix to reduce its dimensionality, the system extracts only the most important channel features, significantly reducing computational complexity while maintaining the precision needed for effective beamforming and communication
Solution Approach 2:
The patent segments the channel processing into distinct steps: first computing the autocorrelation matrix from channel estimates, then averaging it to reduce dimensionality. This segmentation allows the system to handle complex channel information in manageable stages, reducing overall computational complexity while preserving measurement precision at each stage
3Device complexity
If conventional cell split solutions are used, then device complexity is reduced, but productivity deteriorates due to low weight value performance and data processing efficiency
Solution Approach 1:
The patent changes the fundamental parameter representation from direct channel coefficients to an averaged autocorrelation matrix. This parameter change enables more efficient computation of beamforming weights and improves data processing efficiency, as the averaged matrix captures essential channel statistics without the full computational burden of processing complete high-dimensional channel information
Solution Approach 2:
The patent applies local quality by focusing computation on the averaged autocorrelation matrix rather than processing all channel dimensions uniformly. This approach concentrates computational resources on the most significant channel characteristics, improving weight value performance and data processing efficiency while maintaining manageable device complexity
Data Source
AI summary
This application provides a data processing method and a communication apparatus, and relates to the field of communication technologies. The method can be used for downlink data processing and uplink data processing. The communication apparatus may receive a reference signal, and obtain a first matrix based on the reference signal. The first matrix is an autocorrelation matrix whose dimension is N rows and N columns, and N indicates a quantity of channels of an antenna array. Based on the first matrix, channels in different rows of the antenna array are averaged to obtain a second matrix, where a dimension of the second matrix is N/(x×R) rows and N/(x×R) columns; R represents a quantity of channel rows of the antenna array; x represents polarization of the antenna array.