K-Best MIMO Decoder Candidate Selection Logic
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The K-Best algorithm for MIMO decoders faces high computational complexity due to complex tree-like processing, resulting in significant calculation and processing effort, which hinders achieving near-optimal performance with tolerable complexity, throughput, latency, and power consumption requirements.
Innovation Solution
An apparatus and method that generate a candidate group of 2L candidates in the first layer of a K-Best algorithm, iteratively select and send at least two candidates with minimum distance values to subsequent layers, reducing logic, area, and power consumption, while increasing throughput by doubling the processing speed through efficient candidate selection and replacement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a brute-force K-Best tree implementation is used, then the MIMO decoder achieves near-optimal performance, but the computational complexity and processing effort increase exponentially
Solution Approach 1:
The patent segments the candidate selection process into multiple iterative steps, where in each step only a subset of candidates (at least two with minimum distance values) is selected and processed. This divides the exponentially complex brute-force search into manageable sequential stages, reducing the processing effort at each layer while maintaining near-optimal performance through iterative refinement.
2Measurement precision
If all 1024 cost candidates are calculated and sorted at each tree layer, then the most accurate candidates are selected, but the processing time and computational effort become prohibitive
Solution Approach 1:
Instead of calculating and sorting all 1024 cost candidates at each layer, the patent applies partial action by selecting and processing only at least two candidates with minimum distance values in each iterative step. This partial processing approach significantly reduces computation time while still achieving accurate candidate selection by focusing computational resources on the most promising candidates.
3Device complexity
If the number of candidates per stream is reduced to K at each stage, then the computational complexity is reduced, but the throughput and processing speed are limited
Solution Approach 1:
The patent implements continuous useful action by iteratively selecting at least two candidates with minimum distance values and immediately processing them in the next iterative step without waiting for complete candidate sets. This continuous processing approach increases throughput by keeping the computational pipeline full and avoiding idle cycles, while still maintaining reduced complexity through selective candidate processing.
Data Source
AI summary
The invention relates to an apparatus for selecting candidates in a K-Best algorithm of a MIMO decoder. The K-Best algorithm uses a layered structure comprising a first layer and subsequent layers. In each subsequent layer 2L candidates are selected by iteratively carrying out a selection step, wherein in the selection step the apparatus is configured to calculate and select at least two candidates having minimum distance values of a candidate group, and after each iteratively carried out selection step, the selected at least two candidates are sent to a further subsequent layer for iteratively generating a further candidate group of 2L candidates in the further subsequent layer.