Channel Decoding Using Preamble-Constrained LLR Correction
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Solution Overview
Problem
Current channel decoding methods face inefficiencies and increased computational complexity, particularly in decoding error correction codes like LDPC codes, due to the need for iterative updates and checks, which can lead to errors and prolonged computation times.
Innovation Solution
The method involves using structured priori information from a preamble to modify log likelihood ratio (LLR) values and apply constraint codes, specifically incrementing check nodes to constrain relationships between variable nodes, thereby reducing computational requirements and enhancing decoding accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If iterative updates and checks are performed for decoding error correction codes, then decoding accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-defining the structure of the parity check matrix based on preamble characteristics before the actual decoding process. The check nodes are pre-positioned to correspond to specific bit positions in the codeword, and the parity check relationships are established in advance. This eliminates the need for iterative updates and checks during decoding, as the predetermined structure directly provides the decoding solution, thereby reducing computational complexity while maintaining decoding accuracy.
2Reliability
If iterative updates and checks are performed for decoding error correction codes, then decoding reliability is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary action by establishing the parity check matrix structure and check node positions before decoding. The check nodes are pre-configured to monitor specific bit positions based on the preamble-defined structure. This allows the decoder to directly verify decoding correctness using the predetermined parity relationships without requiring multiple iterative passes, thereby maintaining high decoding reliability while significantly reducing computation time.
3Productivity
If structured priori information is used to modify LLR values, then decoding efficiency is improved, but decoding precision may be affected
Solution Approach 1:
The patent applies local quality by selectively modifying LLR values only at specific bit positions that are defined by the preamble structure, rather than uniformly processing all bits. The check nodes are positioned to monitor only the relevant bit positions that have structural correlations. This localized approach allows efficient use of priori information to guide decoding at critical positions while maintaining precise bit value determination through the targeted parity checks, thereby improving decoding efficiency without sacrificing precision.
Data Source
AI summary
A channel decoding method is provided. The method includes storing, in a memory, a set of first log likelihood ratio (LLR) values corresponding to bits of a codeword generated by modulation of a channel-encoded signal; changing, into a preset value, at least one LLR value corresponding to previously defined bits of the codeword from among the set of the first LLR values, to generate a set of second LLR values; and performing forward error correction (FEC) based on the set of the second LLR values and an FEC code, to estimate the bits of the codeword, in which the FEC code comprises a constraint code for constricting a previously defined structural correlation between the bits of the codeword.