MIMO Signal Detection Using Candidate-Reduced MLD for High-Order QAM
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current MIMO communication systems face challenges in efficiently demodulating signals with high-order modulation schemes like 256 QAM, as Maximum Likelihood Detection (MLD) methods incur excessive computational complexity, and existing methods like ZF and MMSE fail to provide sufficient performance or realistic computation reduction.
Innovation Solution
A wireless communication device configuration that estimates transmission paths and noise power, uses linear filters for signal separation, calculates bit Log Likelihood Ratios (LLRs) and mutual information to select transmission candidate points based on distance from reference signal points, and applies MLD using narrowed-down reception candidate points to reduce computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If MLD demodulation is applied to high-order modulation systems like 256 QAM or 1024 QAM, then detection performance is improved, but computation scale increases exponentially making it difficult to apply to actual systems
Solution Approach 1:
The patent segments the MLD computation process by dividing all possible transmission signal points into multiple groups based on their correlation coefficients with the received signal. Instead of evaluating all points simultaneously, the system processes groups sequentially, calculating correlation coefficients and selecting candidate points from each group, thereby breaking down the exponentially complex computation into manageable segments.
Solution Approach 2:
The patent performs preliminary grouping and correlation coefficient calculation for all transmission signal points before the actual MLD selection process. By pre-calculating correlation coefficients and organizing signal points into groups based on these coefficients, the system prepares the data structure in advance, enabling efficient candidate selection during the detection phase without re-computing from scratch.
2Device complexity
If ZF or MMSE methods are used for signal separation, then computation complexity is reduced, but sufficient detection performance cannot be achieved
Solution Approach 1:
The patent merges the advantages of both linear detection methods (ZF/MMSE) and full MLD by using ZF/MMSE as a preliminary step to generate initial candidate points, then applying MLD specifically to these candidates. This combination allows the system to benefit from the low computational complexity of linear methods while achieving the high detection performance of MLD on a reduced search space.
Solution Approach 2:
The patent applies different processing qualities to different regions of the signal space by using linear detection results to identify promising candidate regions, then applying full MLD only to these localized regions rather than uniformly across all possible signal points. This localized application of high-quality MLD processing optimizes the balance between performance and complexity.
Data Source
AI summary
A wireless communication device suppresses increase in computation scale when applying maximum likelihood detection to a multi-level modulation scheme. Units calculate a likelihood, bit log-likelihood ratio, and mutual information content for the separation result of a received signal on the basis of the signal point of a reference selected from among a plurality of signal points that a transmitted signal can assume. A transmission candidate point selection unit selects signal points in a number that corresponds to the mutual information content as transmission candidate points from among the plurality of signal points in ascending order of distance to 0 and distance to 1 for each modulation bit that constitutes the signal point of the reference. A reception candidate point that is a candidate for the received signal is calculated. An external LLR calculation unit calculates the bit log-likelihood ratio by an MLD method.