Maximum Likelihood Detector Search Range Reduction
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Solution Overview
Problem
The existing maximum likelihood detection methods for wireless signal reception in MIMO systems are inefficient due to their exhaustive search nature, leading to high processing latency, complexity, and power consumption.
Innovation Solution
A maximum likelihood detector and method that selects a search value based on a communication index and modulation type, reducing the search range to a subset of candidate signal values, thereby executing a maximum likelihood calculation with lower computation complexity and power consumption while maintaining error correction performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive search manner is used to estimate optimum solution among all possible solutions, then detection accuracy is improved, but processing latency and computation complexity increase significantly
Solution Approach 1:
The patent segments the exhaustive search space into multiple candidate solution sets based on different communication indexes. Instead of evaluating all possible solutions uniformly, the system divides the search into manageable segments (different candidate sets) and selects the most promising ones for detailed evaluation, thereby reducing processing latency while maintaining detection accuracy.
Solution Approach 2:
The patent applies partial action by evaluating only a subset of candidate solutions rather than all possible solutions. By using communication indexes to identify and evaluate only the most relevant candidate signal values, the system achieves sufficient detection accuracy without the excessive computation and time required for complete exhaustive search.
2Measurement precision
If exhaustive search manner is used to estimate optimum solution among all possible solutions, then detection accuracy is improved, but computation complexity increases significantly
Solution Approach 1:
The patent segments the computation process into multiple stages: first calculating communication indexes for all candidate solutions, then using these indexes to identify and evaluate only the most promising candidate sets. This segmentation reduces computation complexity by avoiding unnecessary calculations on less relevant candidates while preserving detection accuracy.
Solution Approach 2:
The patent performs partial computation by evaluating only a selected subset of candidate solutions based on communication indexes. This approach achieves sufficient detection accuracy without the excessive computation required for complete exhaustive search, thereby reducing device complexity.
3Device complexity
If search range is reduced to subset of candidate signal values, then computation complexity and power consumption are reduced, but detection accuracy may be compromised
Solution Approach 1:
The patent performs preliminary action by calculating communication indexes for all candidate signal values before the actual detection process. This preliminary evaluation creates a ranking or selection criterion that guides the subsequent reduced search, ensuring that the subset of candidates evaluated maintains high detection accuracy while reducing computation complexity.
Solution Approach 2:
The patent introduces communication indexes as an intermediary metric that bridges the gap between exhaustive search and reduced search. These indexes serve as a filtering mechanism that identifies promising candidate solutions, allowing the system to reduce the search range to a subset while maintaining detection accuracy through the guidance provided by the communication indexes.
Data Source
AI summary
The present disclosure includes 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 search value according to a communication index and a modulation type or determine the search value according to a predetermined value, in which the communication index is related to a reception signal or a derivative thereof, the search value is associated with a search range, and a number of candidate signal value(s) in the search range is not greater than a number of all candidate signal values of the modulation type. The ML detecting circuit is configured to execute an ML calculation according to the search value and one of the reception signal and the derivative thereof, so as to calculate a log likelihood ratio of every candidate signal value in the search range.