Sphere Decoding for MIMO Signal Detection Complexity
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Solution Overview
Problem
The complexity of signal detection in MIMO systems increases significantly with higher order MIMO systems, leading to increased system complexity, chip processor area, and power consumption, especially when the orthogonal characteristics of the MIMO channel decay, making it difficult to maintain high signal receiving performance.
Innovation Solution
The sphere decoding method uses the Schnorr & Euchner enumeration rule to select preferred points from constellation points based on the signal-to-noise ratio (SNR) of each detection layer, reducing detection complexity while maintaining signal receiving performance by calculating partial Euclidean distances only for a subset of constellation points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the value of K is increased to enhance signal receiving performance, then the receiving performance is improved, but the system complexity greatly increases
Solution Approach 1:
The patent divides the constellation points into multiple subsets based on their Euclidean distances from the received signal. Instead of uniformly processing all K preferred points, the method segments them into different groups (first subset, second subset, etc.) with different numbers of points. This segmentation allows the system to allocate different computational resources to different subsets, reducing the overall complexity while maintaining receiving performance.
Solution Approach 2:
The patent applies different processing strategies to different subsets of constellation points based on their local characteristics. The first subset contains points with smaller Euclidean distances and is processed with higher priority and more computational resources, while the second subset contains points with larger distances and is processed with fewer resources. This local quality approach ensures that critical points receive adequate attention while reducing unnecessary computations on less critical points.
2Reliability
If the value of K is increased to enhance signal receiving performance, then the receiving performance is improved, but the chip processor area is enlarged
Solution Approach 1:
The patent implements partial action by not uniformly processing all K preferred points with the same computational effort. Instead, it processes only a portion of the constellation points (those in the first subset with smaller Euclidean distances) with full computational resources, while processing the remaining points (second subset) with reduced resources or skipping them. This partial action approach maintains receiving performance by focusing on the most critical points while reducing the chip processor area required.
3Reliability
If the value of K is increased to enhance signal receiving performance, then the receiving performance is improved, but the power consumption is increased
Solution Approach 1:
The patent reduces power consumption by performing partial computations on the constellation points. Instead of calculating and processing all K preferred points uniformly, the method selectively processes only the first subset of points with smaller Euclidean distances, which contribute most to the receiving performance. The second subset of points is either processed with reduced computational effort or skipped entirely, significantly reducing power consumption while maintaining acceptable performance levels.
Solution Approach 2:
The patent segments the K preferred points into multiple subsets based on their Euclidean distances, allowing differentiated processing. The first subset (with smaller distances) is processed with full computational resources to ensure high receiving performance, while the second subset (with larger distances) is processed with fewer resources or skipped. This segmentation strategy reduces the total computational load and power consumption while maintaining the critical performance requirements.
4Reliability
If the value of K is increased to enhance signal receiving performance, then the receiving performance is improved, but the data processing amount is reduced
Solution Approach 1:
The patent segments the constellation points into multiple subsets based on their Euclidean distances from the received signal. The first subset contains points with smaller distances and is processed with higher priority, while the second subset contains points with larger distances and is processed with lower priority or skipped. This segmentation reduces the total data processing amount by eliminating unnecessary computations on less critical points while maintaining receiving performance through focused processing of the most important points.
Data Source
AI summary
A sphere decoding method applied to a MIMO channel is provided. Multiple constellation points of an nth detection layer corresponding to a MIMO channel matrix are enumerated based on an enumeration rule, and at least one nth sub-set of the nth detection layer is defined. K constellation points are obtained from each of the at least one nth sub-set as preferred points, and Kn preferred points are selected from all the K preferred points of the at least one nth sub-set. K1 preferred points are transferred to a second detection layer from a first detection layer. K(T−1) preferred points are transferred to a Tth detection layer from a (T−1)th detection layer. An optimal solution is determined according to Kn preferred points of the nth detection layer. K and at least one of K1 to KT are determined by the The characteristic of the MIMO channel matrix.