Lattice Reduction-Aided Symbol Detection in MIMO Systems
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current MIMO detection methods face challenges in achieving optimal performance due to the complexity of determining the transformation matrix for lattice reduction, particularly in large-scale MIMO systems, which affects the accuracy of symbol detection and increases computational complexity.
Innovation Solution
The implementation of a simplified Seysen's Algorithm (SSA) for lattice reduction, which reduces the computational complexity from quadratic to linear by modifying the calculation of candidate update values and restricting scaling coefficients, allowing for efficient orthogonalization of the channel matrix.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional lattice reduction algorithms are used to determine the transformation matrix, then detection accuracy is improved, but computational complexity increases quadratically
Solution Approach 1:
The patent modifies the lattice reduction algorithm by changing the parameters of candidate update operations. Specifically, it restricts the search space of transformation matrix candidates by imposing constraints on the update operations (e.g., limiting the number of non-zero elements in update vectors), thereby reducing computational complexity from quadratic to linear while maintaining detection accuracy through optimized parameter selection
Solution Approach 2:
The patent segments the lattice reduction process into distinct stages: (1) generating candidate update operations with restricted structures, (2) evaluating candidates using a simplified criterion, and (3) iteratively updating the transformation matrix. This segmentation allows each stage to be computationally efficient while collectively achieving accurate symbol detection
2Productivity
If the transformation matrix determination is simplified to reduce computational complexity, then processing speed is improved, but detection accuracy deteriorates
Solution Approach 1:
The patent introduces optimized parameters for the lattice reduction process, including restricted update operation structures and simplified evaluation criteria. These parameter changes enable faster processing by reducing the search space while maintaining detection accuracy through carefully designed constraints that preserve the essential geometric properties needed for accurate detection
Solution Approach 2:
The patent employs simplified candidate update operations that are computationally inexpensive to evaluate. By using restricted-update operations with fewer non-zero elements, each candidate evaluation becomes cheaper, allowing more candidates to be assessed quickly without sacrificing detection performance
Data Source
Figure 1
Figure 2
Figure 3
AI summary
An orthogonalization matrix calculation circuit may include a scaling coefficient calculation circuit configured to calculate a scaling coefficient for each of a plurality of candidate update operations for the orthogonalization matrix, wherein each of the plurality of candidate update operations comprises combining linearly at least one of a first column or a second column of the orthogonalization matrix previously utilized to update the orthogonalization matrix, an update operation selection circuit configured to select an optimum candidate update operation from the plurality of candidate update operations, and a matrix update circuit configured to update the orthogonalization matrix according to the scaling coefficient of the optimum candidate update operation.