Q-ary Symbol-Level Soft Information Generation for LDPC Decoding
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Solution Overview
Problem
Existing decoding systems, such as q-ary low density parity check (LDPC) and soft-input Reed-Solomon decoders, often lack q-ary symbol level soft information, which is essential for effective error correction.
Innovation Solution
A system and process to generate q-ary symbol-level soft information by determining the probability of error symbols based on dominant error patterns, using a probability ratio generator and error event correlators to convert these probabilities into usable soft information for decoders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If q-ary symbol level soft information is not generated, then the decoding system operates with available hardware, but error correction effectiveness deteriorates
Solution Approach 1:
The patent applies preliminary action by generating q-ary symbol level soft information before the decoding process. The system pre-computes probability ratios and dominant error patterns, then uses this pre-generated soft information to improve the effectiveness of subsequent error correction operations in both hard and soft decision decoders.
2Reliability
If soft information generation mechanisms are added, then error correction reliability improves, but system complexity increases
Solution Approach 1:
The patent segments the soft information generation process into distinct functional modules: probability ratio generation, dominant error pattern identification, and soft information computation. This segmentation allows each component to be optimized independently and integrated flexibly into existing decoder architectures, managing complexity through modular design.
Solution Approach 2:
The patent introduces intermediate structures including probability ratio generators and dominant error pattern databases that mediate between raw received signals and decoder inputs. These intermediaries transform hard decisions into soft information through systematic probability calculations, bridging the gap between available hardware and desired decoding performance.
3Measurement precision
If probability calculations are performed for all error patterns, then accuracy of soft information improves, but computational time increases
Solution Approach 1:
The patent extracts and focuses only on dominant error patterns rather than calculating probabilities for all possible error patterns. By identifying and isolating the most significant error patterns that contribute most to decoding accuracy, the system achieves high soft information accuracy while dramatically reducing computational time through selective probability calculation.
Solution Approach 2:
The patent changes the parameter of error pattern consideration from exhaustive (all patterns) to selective (dominant patterns only). This parameter change in the approach to error pattern analysis maintains measurement precision for the most critical cases while reducing overall computational burden by ignoring negligible error patterns.
Data Source
AI summary
A probability is determined, including by obtaining a set of probability ratios, wherein each probability ratio in the set is a ratio of a first probability to a second probability. A probability P(Em=z) that an mth error symbol Em has a level of z is determining based at least in part on one or more dominant error patterns.