Proposed Decoding Scheme Research Articles

Fault-tolerant quaternary belief propagation decoding based on a neural network

The article discusses the challenge of finding an efficient decoder for quantum error correction codes for fault-tolerant experiments in quantum computing. The study aims to develop a better decoding scheme based on the flag-bridge fault tolerance experiment. The research compares two decoding algorithms, a deep neural network decoding scheme and a simple decoder, and a recurrent neural network decoding scheme based on the belief propagation algorithm variant MBP4 algorithm. The study improved the syndrome extraction circuit based on the flag-bridge method to meet the requirements of fault-tolerant experiments better. Two decoding schemes were studied, a combination of a deep neural network and a simple decoder and a recurrent neural network structure based on the MBP4 algorithm. The first scheme used neural networks to assist simple decoders in determining whether additional logical corrections need to be added. The second scheme used a recurrent neural network structure designed through the variant MBP4 algorithm, along with a post-processing method to pinpoint the error qubit position for decoding. Experimental results showed that the decoding scheme developed in the study improved the pseudo-threshold by 39.52% compared to the minimum-weight perfect matching decoder. The two decoders had thresholds of approximately 15.8% and 16.4%, respectively. The study’s findings suggest that the proposed decoding schemes could improve quantum error correction and fault-tolerant experiments in quantum computing.

A Scheme for Collective Encoding and Iterative Soft-Decision Decoding of Cyclic Codes of Prime Lengths: Applications to Reed–Solomon, BCH, and Quadratic Residue Codes

A novel scheme is presented for encoding and iterative soft-decision decoding of cyclic codes of prime lengths. The encoding of a cyclic code of a prime length is performed on a collection of codewords which are mapped through Galois Fourier transform into a codeword in a low-density parity-check code with a binary parity-check matrix for transmission. Using this matrix, binary iterative soft-decision decoding algorithm is applied to jointly decode a collection of codewords from the cyclic code. The joint-decoding allows for information sharing among the received vectors corresponding to the codewords in the collection during the iterative decoding process. For decoding Reed-Solomon and BCH codes of prime lengths, the proposed decoding scheme not only requires much lower decoding complexity than other soft-decision decoding algorithms for these codes, but also yields superior performance. The proposed decoding scheme can also achieve a joint-decoding gain over the maximum likelihood decoding of individual codewords. The decoding scheme is also applied to quadratic residue codes.

Improved Decoding of Linear Block Codes by Concatenated Decoders

The use of decoding algorithms allows us to retrieve information after transmitting it over a noisy communication channel. Soft decision decoding is powerful in concatenation schemes that use two or more levels of decoding. In our case, we make a concatenation between the Hartmann & Rudolph (HR) algorithm as symbol-by-symbol decoder and the chase-2 algorithm that is word-to-word decoding algorithm. In this paper, we propose to combine two decoding algorithms for constructing a new one with more efficiency and less complexity. This work consists firstly to use the HR with a reduced number of codewords of the dual code then the Chase-2 algorithm which exploits the output of PHR. The simulations results and the comparisons made show that the proposed decoding scheme guarantees very good performance with reduced temporal complexity.

Exploiting Error-Correction-CRC for Polar SCL Decoding: A Deep Learning-Based Approach

We investigate the cyclic redundancy check (CRC) codes aided successive cancellation list (SCL) decoding schemes to improve the performance of Polar codes. Distinguished from the existing literatures of Polar codes that only consider the error detection capability of CRC, we attempt to take advantage of the inherent error correction capability of CRC and we first devise a segmented CRC-error-correcting aided SCL decoding (SCC-SCL) framework. Based on this framework, an error-correcting table based SCC-SCL (ET-SCC-SCL) decoding scheme is proposed, by introducing the look-up-table based CRC error correction method into the segmented Polar SCL decoding process. Since the error correction capability is limited by the size of the look-up table, which in turn limits the performance gain, we further propose a deep learning based SCC-SCL (DL-SCC-SCL) decoding scheme. In this scheme, a long short-term memory (LSTM) network replaces the error correction table to perform error correction, combining the sequence of log likelihood ratios (LLRs) with the syndromes to determine the error patterns. Simulation results show that both of the proposed decoding schemes have significant performance gain over the classic CRC error-detection aided SCL decoding scheme. Especially for the DL-SCC-SCL, the performance gain at bit error rate of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> is about 0.5 dB.

Improved Polar SCL Decoding by Exploiting the Error Correction Capability of CRC

Novel successive cancellation list (SCL) decoding schemes of Polar codes by exploiting the error correction (EC) capability of the cyclic redundancy check (CRC) codes are proposed in this paper. Different from the schemes in the literature that only considered the error detection capability of CRC, the proposed schemes aim to improve the performance of CRC-aided Polar decoding to the most by benefiting from the potential EC capability of CRC. Specifically, we first propose a basic decoding scheme, named CRC error-correction-aided successive cancellation list (CRC-EC-SCL) decoding, by introducing the look-up-table-based CRC EC method into the Polar SCL decoding process. Then, based on the basic scheme, we further propose the segmented-CRC-EC-SCL decoding scheme. By segmented-CRC, the information block is divided into multiple segments and each segment is attached with a shorter CRC sequence. By conducting the CRC-EC-aided coding for each of the segments, multiple erroneous bits can be corrected early and error propagation can, thus, be efficiently suppressed. The simulation results show that both of the proposed decoding schemes have a significant performance gain over the classic CRC-EC-SCL decoding scheme. Especially for the segmented-CRC-EC-SCL, the performance gain at a frame error rate of 10−4 is about 0.4 dB. Moreover, the average computation complexity of the segmented-CRC-EC-SCL is close to the classic scheme.

Two-sided assembly line balancing problem of type I: Improvements, a simple algorithm and a comprehensive study

Many meta-heuristic methods have been applied to solve the two-sided assembly line balancing problem of type I with the objective of minimizing the number of stations, but some of them are very complex or intricate to be extended. In addition, different decoding schemes and different objectives have been proposed, leading to the different performances of these algorithms and unfair comparison. In this paper, two new decoding schemes with reduced search space are developed to balance the workload within a mated-station and reduce sequence-depended idle time. Then, graded objectives are employed to preserve the minor improvements on the solutions. Finally, a simple iterated greedy algorithm is extended for the two-sided assembly line balancing problem and modified NEH-based heuristic is introduced to obtain a high quality initial solution. And an improved local search with referenced permutation and reduced insert operators is developed to accelerate the search process. Computational results on benchmark problems prove the efficiency of the proposed decoding schemes and the new graded objectives. A comprehensive computational comparison among 14 meta-heuristics is carried out to demonstrate the efficiency of the improved iterated greedy algorithm.

Reduced memory decoding schemes for turbo decoding based on storing the index of the state metric

In the implementation of turbo-like decoder, the size of state metrics cache (SMC) has a predominant impact on the core area and the overall power dissipation. Different from previous reported decoding schemes, in the proposed decoding schemes, a compressing module and a regeneration module are added to the decoder. The compressing module sorts the forward state metrics from the minimum to the maximum, by which an index sequence and the corresponding increase metrics are calculated, and subsequently are stored in the SMC. In the regeneration module, the forward state metrics are estimated with the index sequence and the increase metrics that accessed from the SMC. With the cost of dummy calculation that is performed by the compressing and the regeneration modules, two decoding schemes are proposed. For an eight-state turbo codes, the linear and the nonlinear estimation based decoding schemes reduce the SMC size by 62.5% and 57.5%, respectively. The bit error rate (BER) simulation is performed for both binary turbo code and duo binary convolutional turbo code, and shows BER of the linear estimation-based scheme is superior to that of the enhanced max-log-MAP (the maximum a posteriori probability) algorithm, whereas BER of the non-linear estimation-based decoding scheme is very close to that of the near optimal decoding scheme.

Uniquely Decodable Codes with Fast Decoder for Overloaded Synchronous CDMA Systems

In this paper, we introduce a new class of signature matrices for overloaded synchronous CDMA systems that have a very low complexity decoder. While overloaded systems are more efficient from the bandwidth point of view, the Maximum Likelihood (ML) implementation for decoding is impractical even for moderate dimensions. Simulation results show that the performance of the proposed decoder is very close to that of the ML decoder. Indeed, the proposed decoding scheme needs neither multiplication nor addition and requires only a few comparisons . Furthermore, the computational complexity and the probability of error vs. Signal to Noise Ratios (SNR) are derived analytically.

Ordered statistics-based list decoding techniques for linear binary block codes

The ordered statistics-based list decoding techniques for linear binary block codes of small to medium block length are investigated. The construction of a list of the test error patterns is considered. The original ordered-statistics decoding (OSD) is generalized by assuming segmentation of the most reliable independent positions (MRIPs) of the received bits. The segmentation is shown to overcome several drawbacks of the original OSD. The complexity of the ordered statistics-based decoding is further reduced by assuming a partial ordering of the received bits in order to avoid the complex Gauss elimination. The probability of the test error patterns in the decoding list is derived. The bit error rate performance and the decoding complexity trade-off of the proposed decoding algorithms is studied by computer simulations. Numerical examples show that, in some cases, the proposed decoding schemes are superior to the original OSD in terms of both the bit error rate performance as well as the decoding complexity.

Decoding algorithms for shortened-extended turbo product codes in WiMAX systems

The component codes of turbo product codes inWiMAX systems are extended Hamming codes and single parity check codes as well as their shortened forms. In this paper, three novel iterative decoding algorithms based on Chase, MAP algorithms and their combination are proposed for shortened-extended turbo product codes. The iterative decoding algorithm based on Chase algorithm is proposed to reduce the decoding complexity without any performance loss. An efficient MAP algorithm is then proposed to decode the component codes of shortened single parity check codes and shortened-extended Hamming codes. A comprehensive performance comparison of the proposed decoding schemes is conducted for three typical classes of turbo product codes in WiMAX OFDMA systems. The suitable decoding algorithms are recommended for different classes based on the simulation results.

Algebraic decoding of the (41, 21, 9) Quadratic Residue code

In this paper, an algebraic decoding algorithm is proposed to correct all patterns of four or fewer errors in the binary (41, 21, 9) Quadratic Residue (QR) code. The technique needed here to decode the (41, 21, 9) QR code is different from the algorithms developed in [I.S. Reed, T.K. Truong, X. Chen, X. Yin, The algebraic decoding of the (41, 21, 9) Quadratic Residue code, IEEE Transactions on Information Theory 38 (1992 ) 974–986]. This proposed algorithm does not require to solve certain quadratic, cubic, and quartic equations and does not need to use any memory to store the five large tables of the fundamental parameters in GF(2 20) to decode this QR code. By the modification of the technique developed in [R. He, I.S. Reed, T.K. Truong, X. Chen, Decoding the (47, 24, 11) Quadratic Residue code, IEEE Transactions on Information Theory 47 (2001) 1181–1186], one can express the unknown syndromes as functions of the known syndromes. With the appearance of known syndromes, one can solve Newton’s identities to obtain the coefficients of the error-locator polynomials. Besides, the conditions for different number of errors of the received words will be derived. Computer simulations show that the proposed decoding algorithm requires about 22% less execution time than the syndrome decoding algorithm. Therefore, this proposed decoding scheme developed here is more efficient to implement and can shorten the decoding time.

Multistage decoding for internally bandwidth efficient coded Poisson fiber-optic CDMA communication systems

We consider the structure and performance of a multistage decoding scheme for an internally bandwidth efficient convolutionally coded Poisson fiber-optic code division multiple access (CDMA) communication system. The decoder is implemented electronically in several stages in which in each stage, the interfering users' coded bit decisions obtained in the previous stage is applied for computing the likelihood of the coded symbols of the desired user. The first stage is a soft-input Viterbi decoder for the internally coded scheme, in which the soft-input coded symbol likelihood values are computed by considering the multiuser interference as a noise signal. The likelihood of coded symbol computed in each stage is then entered into the convolutional decoder for the next bit decisions. The convolutional codes that are used for demonstrating the performance of the multistage decoder are super orthogonal codes (SOCs). We derive the bit error rates (BERs) of the proposed decoder for internally coded Poisson fiber-optic CDMA systems using optical orthogonal codes (OOCs) along with both ON-OFF keying (OOK) and binary pulse position modulation (BPPM) schemes. Our numerical results indicate that the proposed decoding scheme substantially outperforms the single-stage soft-input Viterbi decoder. We also derive the upper bound on the probability of error of a decoder for the known interference case, which is the ultimate performance of a multiuser decoder, and compare the result with that of the soft-input Viterbi decoder.