Iterative Joint Detection-Decoding Algorithms Using Euclidean Distance-Based Feedback

In this article, two algorithms for improving the bit error rate performance of the non-binary LDPC codes over higher order modulation are proposed. The first algorithm is the predictive syndrome based symbol flipping decoding algorithm. The flipping function of this algorithm utilizes the channel r...

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Bibliographic Details
Published in:International Conference on Information and Automation for Sustainability pp. 19 - 24
Main Authors: Ullah, Waheed, Jayakody, Dushantha Nalin K., Li, Jun, Chursin, Yuri
Format: Conference Proceeding
Language:English
Published: IEEE 11.08.2021
Subjects:
Bit error rate
Coded modulation
Complexity theory
Detectors
Iterative algorithms
Joint iterative detection-decoding
Majority logic decoding
Modulation
Non-binary LDPC
Prediction algorithms
Quadrature amplitude modulation
Reliability based decoding
Symbol flipping decoding
ISSN:2151-1810
Online Access:Get full text
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Summary:In this article, two algorithms for improving the bit error rate performance of the non-binary LDPC codes over higher order modulation are proposed. The first algorithm is the predictive syndrome based symbol flipping decoding algorithm. The flipping function of this algorithm utilizes the channel reliability to identify the least reliable symbol position. In Algorithm 1, if the predicted symbol value satisfies the check sum, then the candidate symbol value is declared as correct otherwise that value is adjusted and sent back to the QAM detector. Algorithm 2 in this paper is an improvement to iterative joint detection-decoding algorithm by using the method of iterative hard decision based majority logic to select the new candidate symbol value. The feedback value to the QAM detector is adjusted by using Euclidean distance between the current symbol and the newly selected symbol value. Numerical results and complexity analysis show that the proposed schemes have better bit error rate versus complexity trade-off in comparison to some of the existing algorithms.
ISSN:2151-1810
DOI:10.1109/ICIAfS52090.2021.9606029