Sphere Decoding for MIMO Signal Detection Complexity
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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 employs the Schnorr & Euchner enumeration rule to reduce detection complexity by selecting preferred points based on partial Euclidean distances, maintaining signal receiving performance while minimizing system complexity through efficient enumeration 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 improves, but the system complexity greatly increases by a multiple of (T−2)×M
Solution Approach 1:
The patent applies partial action by calculating partial Euclidean distances instead of complete Euclidean distances for all constellation points. This allows the system to identify preferred points with sufficient accuracy without performing the full calculation for every point, thereby reducing computational complexity while maintaining acceptable receiving performance.
Solution Approach 2:
The patent extracts only the necessary computations for preferred point identification. By focusing calculations only on relevant constellation points and using partial distance metrics, the system extracts the essential information needed for detection while eliminating redundant calculations that contribute to complexity.
2Reliability
If the K-Best sphere decoding method is applied to high-order MIMO systems with insufficient orthogonal characteristics, then the receiving performance can be maintained, but the chip processor area and power consumption increase
Solution Approach 1:
The patent reduces the computational burden on the chip processor by implementing partial Euclidean distance calculations. This partial computation approach maintains the ability to identify preferred points accurately enough for high-order MIMO detection, thereby reducing the processor area required while preserving receiving performance.
3Reliability
If the K-Best sphere decoding method is applied to high-order MIMO systems with insufficient orthogonal characteristics, then the receiving performance can be maintained, but the power consumption increases
Solution Approach 1:
The patent implements energy-efficient detection by performing partial Euclidean distance calculations rather than complete calculations for all constellation points. This reduction in computational operations directly lowers power consumption while maintaining sufficient accuracy for reliable signal detection in high-order MIMO systems.
4Reliability
If maximum likelihood (ML) rule is used for signal detection, then the optimal receiving performance is provided, but the complexity of the signal detection system increases to a too-high extent
Solution Approach 1:
The patent extracts the essential detection functionality from the full maximum likelihood approach by focusing on preferred points identified through partial Euclidean distance calculations. This extraction maintains optimal receiving performance for the subset of preferred points while dramatically reducing system complexity compared to evaluating all constellation points.
Solution Approach 2:
The patent applies partial action by performing Euclidean distance calculations only for preferred points rather than all constellation points. This partial computation approach achieves optimal performance for the selected points while keeping system complexity at a manageable level.
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. The candidate range of the constellation points of the at least one nth sub-set is determined according to a predetermined number K of the preferred points of the at least one nth sub-set. K constellation points are obtained from the at least one nth sub-set as the preferred points according to partial Euclidean distances of the constellation points. The at least one nth sub-set includes at least K constellation points. Kn preferred points are selected from all K preferred points of the at least one nth sub-set. An optimal solution is determined according to the Kn, preferred points.