MIMO Transmitting Point Selection With Shifted-Coordinate K-Best Search
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Solution Overview
Problem
Current methods for selecting transmitting points in MIMO communication systems are inefficient due to high computational complexity, repeated sampling, and potential missed sampling, especially when dealing with a large number of effective transmitting points, which affects the accuracy and reliability of the K-best method.
Innovation Solution
A method that involves transferring the receiving point's coordination to a new origin on the complex plane, identifying sequences of candidates with increasing accumulated path values, and using parallel processing to efficiently select the specific number of transmitting points with the smallest accumulated path values, while determining the necessity of maximum-likelihood searching based on channel matrix diagonal elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the K-best method is used to reduce computational complexity by selecting only k candidate nodes at each layer, then the computation loading is reduced, but the risk of discarding optimal candidates increases
Solution Approach 1:
The patent performs preliminary actions by calculating and storing the squared Euclidean distances between all transmitting points and the receiving point before the actual selection process. This pre-computation allows for faster comparison and selection during the K-best algorithm execution, reducing the computational burden during the main detection process while maintaining accurate candidate selection.
Solution Approach 2:
The patent changes the parameter representation by working with squared Euclidean distances instead of actual distances, and by transforming the coordinate system to a new origin. This parameter transformation simplifies the comparison operations and reduces computational complexity while preserving the relative ordering of candidates, thus maintaining selection accuracy.
2Area of stationary object
If the radius is gradually enlarged to encompass all effective transmitting points, then the coverage is improved, but the calculation precision deteriorates
Solution Approach 1:
The patent pre-calculates and stores the squared Euclidean distances between all transmitting points and the receiving point before the radius expansion process. This preliminary computation ensures that when the radius is gradually enlarged to cover all effective points, the path values are obtained with high precision from the pre-computed distance data rather than being calculated repeatedly during the expansion process.
3Measurement precision
If a large number of sampling points are used to effectively contact all nodes on the circle, then the sampling accuracy is improved, but the total amount of calculation increases hugely
Solution Approach 1:
The patent extracts and pre-computes only the essential information needed for candidate selection - the squared Euclidean distances between transmitting points and the receiving point. By extracting this key parameter beforehand, the method avoids the need for extensive repeated sampling and calculation during the K-best algorithm, significantly reducing computational complexity while maintaining sampling accuracy.
Solution Approach 2:
The patent changes the approach from geometric sampling on circles to direct distance-based comparison using pre-computed squared Euclidean distances. This parameter transformation eliminates the need for complex circular sampling and radius expansion calculations, reducing computational complexity from O(N) sampling points to simple distance comparisons while preserving the accuracy of identifying the k nearest candidates.
Data Source
AI summary
The present invention provides a method for identifying a specific number of communicating points having relatively smallest accumulated path values from a plurality of transmitting points for a receiving point in a communication system. The method includes steps of: (a) defining a first coordination of each of the plurality of transmitting points and the receiving point on a complex plane; (b) transferring the first coordination of the receiving point to a second coordination thereof, in which the second coordination of the receiving point is near an origin of the complex plane; and (c) identifying the specific number of transmitting points having relatively smallest accumulated path values based on the second coordination of the receiving point.