Sphere Decoding for MIMO Signal Detection Complexity Reduction
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Solution Overview
Problem
The complexity of signal detection in multi-input multi-output (MIMO) systems increases significantly with the need for higher signal receiving performance, leading to increased system complexity, chip processor area, and power consumption, especially in high-order MIMO systems with insufficient orthogonal characteristics.
Innovation Solution
The sphere decoding method uses the Schnorr & Euchner enumeration rule and linking-list schemes to reduce detection complexity by selectively calculating partial Euclidean distances and updating subsets of constellation points, thereby obtaining preferred points with reduced computational overhead.
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 by a multiple of (T−2)×M
Solution Approach 1:
The patent divides the detection process into T detection layers, where each layer processes a subset of the total K preferred points. Specifically, each detection layer processes only (T-1) preferred points from the previous layer, segmenting the overall computational task to reduce the complexity multiplier from (T-2)×M to a manageable level while maintaining the total K preferred points across all layers for high receiving performance
Solution Approach 2:
The patent applies different processing strategies to different detection layers. The first detection layer processes K preferred points to obtain (T-1) preferred points, while subsequent layers process only (T-1) preferred points each. This local differentiation optimizes the computational load distribution across layers, reducing overall system complexity while preserving signal receiving performance
2Reliability
If the value of K is increased to enhance signal receiving performance, then the receiving performance is improved, but the area of the chip processor is enlarged
Solution Approach 1:
The patent segments the preferred point processing across T detection layers, where each layer handles a reduced subset of (T-1) preferred points instead of the full K points. This segmentation reduces the computational burden on individual processor units, thereby reducing the required chip processor area while maintaining the overall K preferred points necessary for high signal receiving performance
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 segments the computational workload across T detection layers, with each layer processing only (T-1) preferred points from the previous layer. This segmentation reduces the total number of partial Euclidean distance calculations and sorting operations required per detection layer, thereby reducing power consumption while maintaining the overall K preferred points necessary for high signal receiving performance
Solution Approach 2:
The patent applies partial action by processing only a subset of preferred points at each detection layer beyond the first layer. Instead of processing all K preferred points at every layer, each subsequent layer processes only (T-1) preferred points, reducing computational overhead and power consumption while still achieving the necessary receiving performance through the cumulative effect across all T layers
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 data processing task across T detection layers, where each layer processes a reduced subset of preferred points. The first layer processes K preferred points to obtain (T-1) preferred points, and subsequent layers process only (T-1) preferred points each. This segmentation increases the effective data processing amount by distributing work efficiently, thereby improving productivity while maintaining high signal receiving performance through the cumulative K preferred 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 the MIMO channel matrix are enumerated based on an enumeration rule, and at least one nth sub-set of the nth detection layer is defined. The constellation point with the least PED is obtained as a preferred point. Another constellation, not in the nth sub-set, of the nth detection layer is selected to substitute for the preferred point as one of the nth sub-set. If other preferred points are needed to be obtained, the nth sub-set is updated repeatedly according to the least PED. An optimal solution is determined according to Kn preferred points of the nth detection layer.