Block Code SISO Decoding With Parity-Selected Test Vectors
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Solution Overview
Problem
Conventional soft-input soft-output (SISO) decoders for block codes, such as the Chase-Pyndiah decoder, suffer from high complexity and power consumption due to the large number of test vectors required for acceptable decoding performance, leading to increased chip area and operational costs in ASIC implementations.
Innovation Solution
The improved Chase-Pyndiah decoder reduces complexity by using a substantially reduced number of test vectors and introduces a new normalization factor in the Pyndiah Update rule, optimizing test vectors with even parity to maintain or enhance performance while decreasing decoder complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional Chase-Pyndiah decoder uses a large number of test vectors to achieve acceptable decoding performance, then decoding performance is improved, but device complexity increases
Solution Approach 1:
The patent extracts and utilizes only the even parity error vectors from the complete set of test vectors. By separating the error vectors into even and odd parity groups and selectively using only the even parity group, the decoder achieves acceptable performance with substantially fewer test vectors, thereby reducing device complexity while maintaining reliability
Solution Approach 2:
The patent changes the parameter selection criterion from using all possible test vectors to using only those with even parity. This parameter change (selecting based on parity property) reduces the number of test vectors required while maintaining the essential decoding functionality, thus resolving the contradiction between performance and complexity
2Reliability
If conventional Chase-Pyndiah decoder uses a large number of test vectors, then decoding performance is improved, but power consumption increases
Solution Approach 1:
The patent extracts only the even parity error vectors from the complete test vector set, eliminating the need to process odd parity vectors. This extraction reduces the computational workload and number of operations required, directly lowering power consumption while maintaining acceptable decoding performance through the sufficient subset of even parity vectors
3Reliability
If conventional Chase-Pyndiah decoder uses a large number of test vectors, then decoding performance is improved, but chip area increases
Solution Approach 1:
The patent extracts and stores only the even parity error vectors in memory, removing the need to store and process the complete set of test vectors. This reduction in the number of stored vectors directly decreases the memory requirements and associated circuitry, thereby reducing chip area while maintaining decoding performance through the sufficient even parity subset
Data Source
AI summary
A decoder decodes a soft information input vector represented by an input vector that is binary and that is constructed from the soft information input vector. The decoder stores even parity error vectors that are binary and odd parity error vectors that are binary for L least reliable bits (LRBs) of the input vector. The decoder computes a parity check of the input vector, and selects as error vectors either the even parity error vectors or the odd parity error vectors based at least in part on the parity check. The decoder hard decodes test vectors, representing respective sums of the input vector and respective ones of the error vectors, based on the L LRBs, to produce codewords that are binary for corresponding ones of the test vectors, and metrics associated with the codewords. The decoder updates the soft information input vector based on the codewords and the metrics.