Sphere Decoding for MIMO Maximum-Likelihood Detection
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Solution Overview
Problem
In multi-input multi-output (MIMO) wireless communication systems, existing methods face challenges in efficiently processing signals received by multiple antennas to identify symbols transmitted by multiple antennas, especially under limited bandwidth, due to signal interference and the complexity of maximum-likelihood (ML) detection.
Innovation Solution
A method is introduced that uses a zero-forcing detection method to determine a central point and norm, constructing a sphere to search for the solution point of ML detection within a defined range, reducing the complexity of ML detection by utilizing sphere decoding and Cholesky factorization to simplify the search process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If maximum-likelihood (ML) detection is performed to accurately identify transmitted symbols in MIMO systems, then detection accuracy is improved, but computational complexity increases significantly
Solution Approach 1:
The patent segments the ML detection problem into two parts: first performing zero-forcing (ZF) detection to obtain an initial estimate, then using sphere decoding to search for the optimal solution within a constrained region around this estimate. This segmentation reduces the overall computational complexity while maintaining detection accuracy.
Solution Approach 2:
The patent performs preliminary ZF detection before the final ML detection. The ZF detection provides an initial estimate that serves as a starting point for the sphere decoding process, narrowing down the search space and reducing computational requirements for the subsequent ML detection step.
2Productivity
If the number of transmitting and receiving antennas is increased to transmit more data under limited bandwidth, then data transmission efficiency is improved, but signal interference increases
Solution Approach 1:
The patent extracts and eliminates the interference component through zero-forcing detection. The ZF detector computes an estimate that effectively removes the interference caused by other antenna signals, providing a cleaner initial estimate for the subsequent ML detection process.
3Measurement precision
If conventional ML detection methods are used without sphere decoding, then detection accuracy is maintained, but searching time increases
Solution Approach 1:
The patent applies local quality by concentrating the search effort in a specific region (the sphere) around the ZF estimate rather than searching the entire constellation space uniformly. This localized search approach maintains detection accuracy while significantly reducing the average searching time.
Solution Approach 2:
The patent transforms the detection problem by introducing a geometric dimension (the sphere constraint) to the search space. By formulating the search as finding lattice points within a sphere of radius R around the ZF estimate, the problem becomes more tractable and reduces searching time while maintaining accuracy.
Data Source
AI summary
The present invention relates to a method for searching a solution point of maximum-likelihood detection. The solution point locates at a symbol constellation. The method includes the following steps: determining a central point and a norm by a zero-forcing detection method; determining a searching range according to the central point and the norm; determining at least one qualified solution point according to the searching range; and determining the solution point of maximum-likelihood detection from the qualified solution points.