Maximum Likelihood Detector Layered Candidate Selection
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Solution Overview
Problem
The existing maximum likelihood detection algorithms for wireless signal reception in MIMO systems are inefficient due to their exhaustive search methods, leading to high processing latency, complexity, and computation power requirements, which hinder the improvement of bandwidth usage efficiency and system throughput.
Innovation Solution
A maximum likelihood detector that selectively determines candidate signal values and calculates log likelihood ratios (LLRs) using a layered approach, reducing the number of candidate solutions to be considered, thereby lowering computation complexity and power consumption without compromising performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive search method is used for maximum likelihood detection, then detection accuracy is improved, but processing latency and computation complexity increase
Solution Approach 1:
The patent segments the exhaustive search process into two stages: first selecting K candidate signal values from all possible solutions, then performing refined detection only on these K candidates. This segmentation reduces the search space from all possible solutions to a manageable subset, lowering computation complexity while preserving detection accuracy through the two-stage approach.
Solution Approach 2:
Instead of performing complete exhaustive search on all possible solutions, the patent applies partial action by limiting the refined detection to only K selected candidates. This partial approach achieves sufficient detection accuracy for practical applications while dramatically reducing the computational burden compared to complete exhaustive search.
2Measurement precision
If exhaustive search method is used for maximum likelihood detection, then detection accuracy is improved, but processing time increases
Solution Approach 1:
The patent divides the detection process into two time-efficient segments: rapid selection of K candidate values followed by focused refined detection. This segmentation eliminates the need to process all possible solutions, significantly reducing processing latency while maintaining detection accuracy through the structured two-stage approach.
Solution Approach 2:
The patent performs preliminary action by pre-selecting K candidate signal values before performing the computationally intensive refined detection. This preliminary selection filters out unlikely candidates, ensuring that time-consuming detection operations are applied only to promising candidates, thereby reducing overall processing time.
3Measurement precision
If exhaustive search method is used for maximum likelihood detection, then detection accuracy is improved, but power consumption increases
Solution Approach 1:
The patent segments the computational workload into a low-power candidate selection phase and a focused refined detection phase applied to only K candidates. This segmentation prevents the system from consuming high power across all possible solutions, reducing overall computation power requirements while preserving detection accuracy through selective processing.
Solution Approach 2:
The patent applies partial action by performing refined detection on only K candidates rather than all possible solutions. This partial approach achieves adequate detection accuracy for practical purposes while dramatically reducing the energy consumption associated with exhaustive computation across the entire solution space.
Data Source
AI summary
The present invention discloses an ML (Maximum Likelihood) detector. An embodiment of the ML detector comprises a search value selecting circuit and an ML detecting circuit. The search value selecting circuit is configured to select a first-layer search value. The ML detecting circuit is configured to carry out the following steps: selecting first-layer candidate values according to the first-layer search value, one of a reception signal and a derivative thereof, and one of a channel estimation signal and a derivative thereof, and adding one or more first-layer candidate value(s), if necessary; calculating second-layer candidate values according to all the above-mentioned first-layer candidate values, and adding one or more second-layer candidate value(s) and its/their corresponding first-layer candidate value(s), if necessary; and calculating log likelihood ratios according to the whole first-layer and second-layer candidate values.