Iterative Soft-Input Soft-Output Decoding of Reed-Solomon Codes by Adapting the Parity-Check Matrix

An iterative algorithm is presented for soft-input soft-output (SISO) decoding of Reed-Solomon (RS) codes. The proposed iterative algorithm uses the sum-product algorithm (SPA) in conjunction with a binary parity-check matrix of the RS code. The novelty is in reducing a submatrix of the binary parit...

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Vydané v:IEEE transactions on information theory Ročník 52; číslo 8; s. 3746 - 3756
Hlavní autori: Jing Jiang, Narayanan, K.R.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York, NY IEEE 01.08.2006
Institute of Electrical and Electronics Engineers
Predmet:
Adapting the parity-check matrix
Adaptive algorithms
Additive white noise
Algorithms
Applied sciences
AWGN
Coding, codes
Computer simulation
Convergence
Decoding
Descent
Error correction codes
Exact sciences and technology
Gain
gradient descent
Information, signal and communications theory
Iterative algorithms
Iterative decoding
Maximum likelihood decoding
Parity check codes
Performance loss
Reed-Solomon (RS) codes
Signal and communications theory
soft-decision decoding
Sparse matrices
Telecommunications and information theory
Reed Solomon code
Performance evaluation
Multistage method
Reed-Solomon (RS) codes
Iterative method
Potential function
Decoding
Algorithm
Adaptive method
iter ative decoding
Adapting the parity-check matrix
Simulation
Descent method
soft-decision decoding
Soft decision
Gradient method
gradient descent
Parity check
ISSN:0018-9448
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Shrnutí:An iterative algorithm is presented for soft-input soft-output (SISO) decoding of Reed-Solomon (RS) codes. The proposed iterative algorithm uses the sum-product algorithm (SPA) in conjunction with a binary parity-check matrix of the RS code. The novelty is in reducing a submatrix of the binary parity-check matrix that corresponds to less reliable bits to a sparse nature before the SPA is applied at each iteration. The proposed algorithm can be geometrically interpreted as a two-stage gradient descent with an adaptive potential function. This adaptive procedure is crucial to the convergence behavior of the gradient descent algorithm and, therefore, significantly improves the performance. Simulation results show that the proposed decoding algorithm and its variations provide significant gain over hard-decision decoding (HDD) and compare favorably with other popular soft-decision decoding methods
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ISSN:0018-9448
DOI:10.1109/TIT.2006.878176