MIMO Signal Detection via QR Decomposition and Euclidean Distance
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Solution Overview
Problem
Current signal detection methods in MIMO wireless communication systems, such as Zero-Forcing (ZF), Minimum Mean Square Error (MMSE), and Order Successive Interference Cancellation (OSIC), face challenges in achieving performance similar to Maximum Likelihood (ML) methods while maintaining low computational complexity, especially as the number of transmit antennas and modulation order increase, leading to high computational complexity and inefficiencies in calculating Log Likelihood Ratios (LLR) for soft decision decoding.
Innovation Solution
A receiver apparatus and method using QR decomposition to decompose the channel matrix into matrices Q and R, allowing for the estimation of transmit signal vectors by substituting symbols into candidate groups, calculating square Euclidean distances, and selecting vectors with the smallest distance values to determine candidate groups, thereby reducing computational complexity and enhancing performance by generating LLRs effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Maximum Likelihood (ML) method is used for signal detection, then detection performance is improved, but computational complexity increases exponentially
Solution Approach 1:
The patent segments the signal detection process into multiple phases, where in each phase only a subset of candidate signal vectors is evaluated. Instead of computing distances for all possible transmit signal vectors at once (as in ML), the detection is divided into sequential phases with candidate groups being refined iteratively, reducing the exponential complexity to a manageable level while maintaining near-ML performance.
Solution Approach 2:
The patent applies partial action by evaluating only a partial set of candidate signal vectors in each phase rather than all possible vectors. By selecting and evaluating limited candidate groups based on preliminary metrics, the system achieves sufficient detection performance without the excessive computational burden of exhaustive ML search.
2Productivity
If the number of transmit antennas and modulation order increase, then data transmission capacity is improved, but computational complexity of signal detection increases
Solution Approach 1:
The patent segments the large search space of candidate vectors into multiple smaller candidate groups across different phases. This segmentation allows the system to handle higher numbers of transmit antennas and modulation orders by processing candidates in manageable chunks rather than all at once, enabling high-capacity transmission without proportional complexity increases.
Solution Approach 2:
The patent implements dynamic candidate group selection where the set of candidates to be evaluated changes adaptively in each phase based on previous detection results. This dynamic approach allows the system to efficiently scale with increased antennas and modulation orders by concentrating computational effort on the most promising candidate regions rather than uniformly searching all possibilities.
3Device complexity
If linear signal detection methods (ZF, MMSE) are used, then computational complexity is reduced, but detection performance deteriorates
Solution Approach 1:
The patent introduces an intermediary approach that combines elements of linear detection and ML detection. It uses linear detection methods as a preliminary step to generate initial candidate groups, then refines these candidates with more sophisticated evaluation. This intermediary strategy achieves a balance between the low complexity of linear methods and the high performance of ML methods.
Data Source
AI summary
Receiving apparatus and method in a Multiple-Input Multiple-Output (MIMO) wireless communication system are provided. The receiver having N-ary receive antennas includes a decomposer for decomposing a channel matrix to a matrix Q and a matrix R through a QR decomposition; a detector for determining a candidate group of an n-th phase by estimating a plurality of transmit signal vectors by substituting a plurality of transmittable symbols into symbol combinations of a candidate group of a (n−1)-th phase as an n-th symbol and detecting (n+1)-th through N-th symbols using characteristics of the matrix R; a calculator for calculating square Euclidean distance values between the transmit signal vectors and a receive signal vector; and a determiner for determining the candidate group of the n-th phase by selecting transmit signal vectors having the smallest square Euclidean distance value among the transmit signal vectors.