Receiver Branch Parallelization for Antenna Dimension Reduction
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Solution Overview
Problem
Traditional receivers with large-scale antennas face high processing complexity due to complex matrix inversion operations, which can overwhelm general-purpose processors and affect system timing.
Innovation Solution
Perform covariance matrix calculation on frequency-domain data, apply eigenvalue decomposition to determine spatial-domain filtering weights, and process data in parallel to reduce dimensions, thereby reducing algorithm complexity while ensuring real-time effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional matrix inversion operations are used for large-scale antenna processing, then processing accuracy is maintained, but processing complexity becomes excessively high
Solution Approach 1:
The patent segments the large-scale antenna array into multiple sub-arrays, each processed independently with simplified algorithms. This division reduces the computational complexity of matrix operations while maintaining overall processing accuracy through coordinated processing of all sub-arrays.
Solution Approach 2:
The patent transforms the traditional spatial domain processing into frequency domain processing using Fourier transforms. This dimensional transformation converts complex spatial matrix inversion operations into simpler frequency domain operations, significantly reducing processing complexity while preserving accuracy.
2Adaptability or versatility
If general-purpose processors are used for complex receiver processing, then processing flexibility is maintained, but processing time increases and affects system timing
Solution Approach 1:
The patent replaces traditional mechanical/computational matrix inversion operations with signal processing techniques based on Fourier transforms and spatial filtering. This substitution reduces the computational burden on general-purpose processors, decreasing processing time while maintaining flexibility through software-based implementation.
Solution Approach 2:
The patent changes the processing parameters from spatial domain to frequency domain, transforming the nature of computations required. This parameter transformation enables the use of efficient Fast Fourier Transform algorithms instead of complex matrix operations, significantly reducing processing time while maintaining adaptability through configurable filtering parameters.
3Device complexity
If dimension reduction processing is applied to branches, then processing complexity is reduced, but data representation accuracy may be affected
Solution Approach 1:
The patent extracts and processes only the most significant spatial components from the full antenna array data through dimension reduction. By identifying and retaining the dominant signal subspaces while discarding redundant information, the system reduces processing complexity while maintaining adequate data representation accuracy for practical applications.
Data Source
AI summary
Disclosed in the present application are a data processing method, and an electronic device and a storage medium. The method comprises: determining a covariance matrix of frequency-domain data which is received by a receiver; according to the covariance matrix, determining the number of branches which are processed in parallel by the receiver, and a spatial-domain filtering weight corresponding to each of the branches, wherein each of the branches corresponds to an antenna of at least one dimension; for each of the branches, according to the spatial-domain filtering weight corresponding to the branch, performing dimension reduction processing on data corresponding to the branch, so as to obtain data which has been subjected to dimension reduction and corresponds to the branch; and completing a processing operation for the frequency-domain data according to the data which has been subjected to dimension reduction and corresponds to each of the branches.