This paper presents the implementation of a low-complex iterative symbol-level decoding scheme for Reed-Solomon codes. Most soft-decision iterative decoders for Reed-Solomon codes work on a bit level due to the effici...
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This paper presents the implementation of a low-complex iterative symbol-level decoding scheme for Reed-Solomon codes. Most soft-decision iterative decoders for Reed-Solomon codes work on a bit level due to the efficient passing of soft information due to the sparsity of the binary parity check matrix. These decoders yield a good error correction performance, but this comes at the cost of an increased computational complexity resulting from working at a bit-level. This study aims to lower the computational complexity of iterative decoding of Reed-Solomon codes by intro-ducing a high-performance soft-decision iterative Reed-Solomon decoder that works on a symbol -level, in contrast to the bit-level implementation used in most iterative Reed-Solomon decoders. The proposed algorithm utilises soft information to decode while applying information set decoding techniques to an extended parity check matrix. The use of the extended parity check matrix provides additional parity check equations which assist the algorithm in determining the correct information set of symbols used in the decoding process. For a given on;k thorn Reed-Solomon code, the algorithm converges to a valid codeword by correctly decoding a specified information set of k symbols from the received vector. Simulations run show the proposed algorithm performs favourably when com-pared to other symbol-level iterative decoders while working at a relatively lower complexity.(c) 2023 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://***/ licenses/by-nc-nd/4.0/).
This paper introduces two effective techniques to reduce the decoding complexity of turbo product codes (TPC) that use extended Hamming codes as component codes. We first propose an advanced hard-inputsoft-output (HI...
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This paper introduces two effective techniques to reduce the decoding complexity of turbo product codes (TPC) that use extended Hamming codes as component codes. We first propose an advanced hard-inputsoft-output (HISO) decoding algorithm, which is applicable if an estimated syndrome stands for double-error. In conventional soft-inputsoft-output (SISO) decoding algorithms, 2(p)(p: the number of least reliable bits) number of hard decision decoding (HDD) operations are performed to correct errors. However, only a single HDD is required in the proposed algorithm. Therefore, it is able to lower the decoding complexity. In addition, we propose an early termination technique for undecodable blocks. The proposed early termination is based on the difference in the ratios of double-error syndrome detection between two consecutive half-iterations. Through this early termination, the average iteration number is effectively lowered, which also leads to reducing the overall decoding complexity. Simulation results show that the computational complexity of TPC decoding is significantly reduced via the proposed techniques, and the error correction performance remains nearly the same in comparison with that of conventional methods.
This correspondence extends the application of the recently proposed stochastic decoding approach to decode linear block codes with high-density parity-check matrices and discusses its hardware complexity. Results dem...
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This correspondence extends the application of the recently proposed stochastic decoding approach to decode linear block codes with high-density parity-check matrices and discusses its hardware complexity. Results demonstrate decoding performance close to floating-point iterative soft-inputsoft-output (SISO) decoding while offering nodes with considerably lower complexity compared to fixed-point SISO decoding.
soft-input soft-output decoding consists of estimating the information symbol a posteriori probablities, taking account of the coding constraints, given the received symbol a priori probabilities. Kullback's princ...
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We study the performance limit of concatenated codes based on binary constituent codes, under symbol-based iterative decoding. Although the results exposed herein are valid for any compound code built from both block ...
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Until very recently, joint source channel techniques mostly focused on systems using fixed-length coding, eventhough variable-length coding (VLC) is widely used, particularly in video coding. Typically, VLC bit stream...
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ISBN:
(纸本)0780372069
Until very recently, joint source channel techniques mostly focused on systems using fixed-length coding, eventhough variable-length coding (VLC) is widely used, particularly in video coding. Typically, VLC bit streams are made channel-robust through packetization and standard forward-error correction (FEC). However, when the channel conditions are fairly mild, FEC can reveal itself bandwidth-inefficient. A variable-rate extension of joint source channel decoding could thus potentially replace FEC under mild conditions or, for noisier channels, could be used together with FEC to ameliorate the coding rate, extending in both cases the range of situations under which the bit stream is adequately protected. We propose here two reduced-complexity VLC soft-inputdecoding techniques, as well as a comparison with existing algorithms. Experimental results of a new proposed VLC decoding algorithm show very good performance and low complexity.
This study proposes a novel approach to joint channel estimation and detection of orthogonal frequency division multiplexing transmission over underwater acoustic (UWA) multipath channels exhibiting cluster sparsity. ...
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This study proposes a novel approach to joint channel estimation and detection of orthogonal frequency division multiplexing transmission over underwater acoustic (UWA) multipath channels exhibiting cluster sparsity. Unlike most sparse channel estimations, the authors exploit the cluster-sparsity characteristic of UWA channels without additional prior information. They adopt a modified spike-and-slab prior model in their non-parametric Bayesian learning framework. To avoid the need for a closed-form Bayesian estimate, they apply the Markov chain Monte Carlo technique to joint achieve channel estimation and signal detection. The proposed solution is amenable to being integrated with soft-input soft-output decoding to improve the performance through turbo iteration. Simulation results demonstrate improved bit error rate of the proposed algorithm over existing algorithms.
It is well known that softdecoding algorithms are based on the use of reliability measures for binary and nonbinary symbols. In the paper we consider an analytical estimation of the distributions of reliability measu...
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It is well known that softdecoding algorithms are based on the use of reliability measures for binary and nonbinary symbols. In the paper we consider an analytical estimation of the distributions of reliability measures after softdecoding of block and convolutional codes or intersymbol interference (ISI) channels. This estimation, based on a simple geometric interpretation of reliability measures, allows the observation of different distributions for correct and incorrect symbols, and could be helpful for the analysis of softdecoding algorithms.
For binary codes, Fossorier et al. have shown that the soft-output Viterbi algorithm (SOVA) becomes equivalent to the Max-Log-maximum a posteriori (MAP) algorithm after a modification. In this letter, we generalize su...
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For binary codes, Fossorier et al. have shown that the soft-output Viterbi algorithm (SOVA) becomes equivalent to the Max-Log-maximum a posteriori (MAP) algorithm after a modification. In this letter, we generalize such modified SOVA to nonbinary cases, motivated by the fact that the performance gap between the original SOVA and the Max-Log-MAP algorithm broadens in these cases. The equivalence between the two algorithms is proved in a more compact form. The modified SOVA requires only add-compare-select operations and is well suited for high-speed parallel implementation.
In this letter, it is shown that after a proper simple modification, the soft-output Viterbi algorithm (SOVA) proposed by Hagenauer and Hoeher becomes equivalent to the Max-Log-maximum a posteriori ( MAP) decoding alg...
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In this letter, it is shown that after a proper simple modification, the soft-output Viterbi algorithm (SOVA) proposed by Hagenauer and Hoeher becomes equivalent to the Max-Log-maximum a posteriori ( MAP) decoding algorithm. Consequently, this modified SOVA allows to implement the Max-Log-MAP decoding algorithm by simply adjusting the conventional Viterbi algorithm. Hence, it provides an attractive solution to achieve low-complexity near-optimum soft-input soft-output decoding.
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