RRQR Decomposition for Linearly Independent User Selection in MIMO Systems
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Solution Overview
Problem
Current methods for selecting space division multiplexing users in MIMO communication systems fail to efficiently identify the most linearly independent users, leading to increased operational complexity and difficulty in meeting real-time performance requirements, especially as the number of candidate users increases.
Innovation Solution
The method involves performing a rank-revealing QR decomposition (RRQR) on the channel response matrix of candidate users to determine the most linearly independent users, which are then selected as space division multiplexing users based on the decomposition results, utilizing a permutation matrix, Q matrix, and R matrix to optimize user selection and beamforming weight calculation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a traversal method is used to select the most linearly independent users among candidate users, then the selection accuracy is improved, but the operation amount is rapidly increased with the increase of the number of candidate users
Solution Approach 1:
The patent transforms the user selection problem from evaluating all possible combinations to evaluating only N candidates based on their channel vectors. By changing the selection criterion to linear independence measurement through matrix decomposition, the computational complexity is reduced from exponential to polynomial order, achieving O(N*M^2) complexity where N is the number of candidates and M is the number of selected users.
Solution Approach 2:
The patent segments the user selection process into two independent stages: first selecting users with maximum linear independence using RRQR decomposition, then performing beamforming weight calculation. This segmentation allows the complex joint optimization problem to be decomposed into manageable sub-problems, reducing overall computational burden.
2Measurement precision
If the number of candidate users increases, then the selection of most linearly independent users becomes more accurate, but the real-time performance requirement becomes difficult to satisfy
Solution Approach 1:
The patent changes the computational approach from exhaustive search to matrix decomposition-based selection. By using RRQR decomposition, the selection time grows polynomially with the number of candidates rather than exponentially, enabling real-time processing even with large numbers of candidate users.
Solution Approach 2:
The patent performs preliminary channel estimation and matrix decomposition setup before the actual user selection. By pre-processing the channel matrices and establishing the decomposition structure in advance, the system reduces the real-time computational burden during user selection, enabling faster response to increasing numbers of candidates.
3Device complexity
If conventional user selection methods are used based on correlation between two users, then the computational complexity is reduced, but the correlation among multiple users is not reflected and the selected users may not be most linearly independent
Solution Approach 1:
The patent transitions from pairwise correlation analysis to multi-dimensional linear independence analysis by constructing a matrix with multiple user channel vectors and applying RRQR decomposition. This dimensional expansion allows simultaneous consideration of all user correlations in a unified mathematical framework, achieving both low computational complexity and high measurement precision.
Solution Approach 2:
The patent replaces the conventional pairwise correlation mechanism with a matrix decomposition mechanism. Instead of iteratively checking correlations between pairs of users, the system uses RRQR decomposition to directly identify the subset of users with maximum linear independence, substituting a more efficient mathematical operation for the conventional approach.
Data Source
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AI summary
A method and device for selecting space division multiplexing users are provided. The method includes: acquiring a channel response of each of candidate users by performing a channel estimation on received data of each of the candidate users; constructing a channel response matrix of all the candidate users by using the channel response of each of the candidate users; and performing a rank-revealing QR (RRQR) decomposition of the channel response matrix and determining, based on a result of the RRQR decomposition, candidate users that are most linearly independent and satisfying a space division multiplexing user number required by a base station as space division multiplexing users.