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.
Innovation Solution
A maximum likelihood detector and wireless signal receiver with a maximum likelihood detection function that employs a search value selecting circuit and a maximum likelihood detecting circuit to select candidate signal values and calculate log likelihood ratios (LLRs) in a layered approach, reducing the number of solutions to be considered and thereby lowering computation complexity.
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 significantly
Solution Approach 1:
The patent segments the exhaustive search process into multiple layers (first layer, second layer, etc.) where each layer processes a subset of candidate signal values. The search value selecting circuit divides the total search space by selecting only certain candidate values at each layer, thereby segmenting the computational workload while maintaining detection accuracy through systematic multi-layer processing.
Solution Approach 2:
The patent applies partial action by calculating log likelihood ratios (LLRs) for only a selected portion of candidate signal values rather than all possible values. The search value selecting circuit identifies and processes only the most relevant candidates at each layer, performing partial computations that suffice for accurate detection without the need for 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 detection process is segmented into multiple processing layers that operate in sequence. Each layer handles a specific portion of the candidate signal values, allowing the system to process detections in manageable chunks rather than requiring all computations simultaneously, thereby reducing overall processing latency while maintaining accuracy.
Solution Approach 2:
The search value selecting circuit performs preliminary selection of candidate signal values before the main LLR calculation process. By pre-identifying and filtering candidate values at each layer, the system prepares the data in advance, reducing the computational burden during the actual detection phase and thereby decreasing processing time.
3Measurement precision
If exhaustive search method is used for maximum likelihood detection, then detection accuracy is improved, but computation power requirements increase
Solution Approach 1:
The patent implements partial action by computing LLRs only for selected candidate signal values identified by the search value selecting circuit, rather than computing for all possible candidates. This selective computation approach maintains detection accuracy for the most relevant candidates while significantly reducing the total computation power required.
Solution Approach 2:
The computation power requirement is segmented across multiple layers, where each layer processes a specific subset of candidates. This distribution of computational tasks across layers reduces the peak power consumption at any single moment while achieving the same overall detection accuracy through cumulative processing.
Data Source
AI summary
The present invention discloses an ML (Maximum Likelihood) detector comprising: a search value selecting circuit selecting a first-layer search value; and an ML detecting circuit. The ML detecting circuit executes the following steps: selecting K 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; calculating K second-layer candidate values according to the K first-layer candidate values; determining whether to add P second-layer supplemental candidate value(s) and generating a decision; when the decision is affirmative, adding the P second-layer supplemental candidate values, generating P first-layer supplemental candidate values according to the P second-layer supplemental candidate values, and calculating log likelihood ratios (LLRs) according to the (K+P) first-layer and (K+P) second-layer candidate values; and when the decision is negative, calculating LLRs according to the K first-layer and K second-layer candidate values.