Multi-Dimensional Decoding with Reliability-Based Candidate Pruning
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Solution Overview
Problem
The complexity of multi-dimensional codes makes optimal decoding, such as maximum likelihood decoding, impractical due to rapidly increasing complexity, necessitating a reduction in complexity while maintaining decoding efficiency.
Innovation Solution
A method for multi-dimensional decoding that calculates and selects candidates based on reliability scores, applies a multi-dimensional soft decoding process, and iteratively refines candidate selection to reduce the number of candidates evaluated, thereby controlling complexity and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If optimal decoding such as maximum likelihood decoding is implemented, then decoding efficiency is maximized, but complexity grows rapidly making it impractical
Solution Approach 1:
The patent segments the decoding process into multiple dimensions, where each dimension processes a subset of code components. Instead of evaluating all candidates across all components simultaneously, the method divides the problem into manageable dimensional slices, processing candidates dimension by dimension to reduce overall complexity while maintaining decoding performance.
Solution Approach 2:
The patent introduces a dimensional framework for candidate evaluation, transitioning from a single-complexity-dimension approach to a multi-dimensional evaluation space. By organizing candidates across multiple dimensions and processing them systematically, the method reduces the computational burden of evaluating all candidates while preserving the ability to identify optimal solutions.
2Device complexity
If the number of candidates evaluated is reduced to control complexity, then decoding complexity decreases, but the probability of successful decoding may be compromised
Solution Approach 1:
The patent performs preliminary evaluation and filtering of candidates before the main decoding process. By pre-identifying and prioritizing promising candidates based on initial criteria, the method reduces the number of candidates that require full evaluation, thereby controlling complexity while ensuring that high-probability successful decoding candidates are not missed.
Solution Approach 2:
The patent dynamically adjusts evaluation parameters such as candidate selection thresholds and dimension processing strategies based on the specific code and error conditions. By changing these parameters adaptively, the method optimizes the balance between evaluating enough candidates to ensure successful decoding and limiting the number to control computational complexity.
Data Source
AI summary
A system, computer readable medium and a method for multi-dimensional decoding. The method may include calculating, for each single code component of multiple code components and for each dimension of the multi-dimensional decoding, multiple first candidates and assigning a first reliability score for each first candidate; selecting, for each single code component and for each dimension, a second candidate out of the multiple first candidates in response to first reliability scores of the multiple first candidates; selecting, per dimension, multiple third candidates out of all second candidates associated with the dimension, in response to reliability information related to the second candidates associated with the dimension; selecting fourth candidates out of third candidates of all dimensions; wherein a number of the fourth candidates is smaller than a number of the third candidates of all dimensions; wherein the fourth candidates are indicative of locations of bits to be evaluated; and applying a multi-dimensional soft decoding process on the multiple code components.