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IEEE Transactions on Circuits and Systems I Regular Papers

Variable-Node-Shift Based Architecture for Probabilistic Gradient Descent Bit Flipping on QC-LDPC Codes

Khoa Le, David Declercq, Fakhreddine Ghaffari, Lounis Kessal, Oana Boncalo, Valentin Savin

Published Dec 18, 2017• 17 citations
Publisher

Abstract

Probabilistic gradient descent bit-flipping (PGDBF) is a hard-decision decoder for low-density parity-check (LDPC) codes, which offers a significant improvement in error correction, approaching the performance of soft-information decoders on the binary symmetric channel. However, this outstanding performance is known to come with an augmentation of the decoder complexity, compared to the non-probabilistic gradient descent bit flipping (GDBF), becoming a drawback of this decoder. This paper presents a new approach to implementing PGDBF decoding for quasi-cyclic LDPC (QC-LDPC) codes, based on the so-called variable-node-shift architecture (VNSA). In VNSA-based PGDBF implementations, the regularity of QC-LDPC connection networks is used to cyclically shift the memory of the decoder, leading to the fact that, a variable node (VN) is processed by different computing units during the decoding process. With this modification, the probabilistic effects in VN operations can be produced by implementing different types of processing units, without requirement of a probabilistic signal generator. The VNSA is shown to further improve the decoding performance of the PGDBF, with respect to other hardware implementations reported in the literature, while reducing the complexity below that of the GDBF. The efficiency of the VNSA is proven by ASIC synthesis results and by decoding simulations.

DOI
10.1109/tcsi.2017.2777802

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Probabilistic gradient descent bit-flipping (PGDBF) is a hard-decision decoder for low-density parity-check (LDPC) codes, which offers a significant improvement in error correction, approaching the performance of soft-information decoders on the binary symmetric channel. However, this outstanding performance is known to come with an augmentation of the decoder complexity, compared to the non-probabilistic gradient descent bit flipping (GDBF), becoming a drawback of this decoder. This paper presents a new approach to implementing PGDBF decoding for quasi-cyclic LDPC (QC-LDPC) codes, based on the so-called variable-node-shift architecture (VNSA). In VNSA-based PGDBF implementations, the regularity of QC-LDPC connection networks is used to cyclically shift the memory of the decoder, leading to the fact that, a variable node (VN) is processed by different computing units during the decoding process. With this modification, the probabilistic effects in VN operations can be produced by implementing different types of processing units, without requirement of a probabilistic signal generator. The VNSA is shown to further improve the decoding performance of the PGDBF, with respect to other hardware implementations reported in the literature, while reducing the complexity below that of the GDBF. The efficiency of the VNSA is proven by ASIC synthesis results and by decoding simulations.
Probabilistic Gradient Descent Bit Flipping (PGDBF) algorithm have been recently introduced as a new type of hard decision decoder for Low-Density Parity-Check Code (LDPC) applied on the Binary Symmetric Channel. By following precisely the decoding steps of the deterministic Gradient Descent Bit-Flipping (GDBF) decoder, PGDBF additionally incorporates a random perturbation in the flipping operation of Variable Nodes (VNs) and produces an outstanding decoding performance which is better to all known Bit Flipping decoders, approaching the performance of soft decision decoders. We propose in this thesis several hardware implementations of PGDBF, together with a theoretical analysis of its error correction capability. With a Markov Chain analysis of the decoder, we show that, due to the incorporation of random perturbation in VN processing, the PGDBF escapes from the trapping states which prevent the convergence of decoder. Also, with the new proposed analysis method, the PGDBF performance can be predicted and formulated by a Frame Error Rate equation as a function of the iteration, for a given error pattern. The analysis also gives a clear explanation on several phenomenons of PGDBF such as the gain of re-decoding (or restarting) on a received error pattern. The implementation issue of PGDBF is also addressed as a main part in this thesis. The conventional implementation of PGDBF, in which a probabilistic signal generator is added on top of the GDBF, is shown with an inevitable increase in hardware complexity. Several methods for generating the probabilistic signals are introduced which minimize the overhead complexity of PGDBF. These methods are motivated by the statistical analysis which reveals the critical features of the binary random sequence required to get good decoding performance and suggesting the simplification directions. The synthesis results show that the implemented PGDBF with the proposed probabilistic signal generator method requires a negligible extra complexity with the equivalent decoding performance to the theoretical PGDBF. An interesting and particular implementation of PGDBF for the Quasi-Cyclic LDPC (QC-LDPC) is shown in the last part of the thesis. Exploiting the structure of QC-LDPC, a novel architecture to implement PGDBF is proposed called Variable-Node Shift Architecture (VNSA). By implementing PGDBF with VNSA, it is shown that the decoder complexity is even smaller than the deterministic GDBF while preserving the decoding performance as good as the theoretical PGDBF. Furthermore, VNSA is also shown to be able to apply on other types of LDPC decoding algorithms.
The Probabilistic Gradient Descent Bit-Flipping (PGDBF) decoder offers a significant improvement in decoding performance for Low-Density Parity-Check (LDPC) codes on Binary Symmetric Channel (BSC). However, this outstanding decoding performance comes along with a non-negligible extra hardware cost to realize the probabilistic behavior on top of the deterministic Gradient Descent Bit-Flipping (GDBF) decoder. This paper presents a novel solution to implement PGDBF decoder on Quasi-Cyclic LDPC codes. The proposed architecture takes advantage of the cyclic shift permutation nature of QC-LDPC and changes the message flow such that a probabilistic behavior is emulated without the cost of an actual probabilistic signal generator. It is shown that, the proposed architecture improves the PGDBF decoding performance with respect to the state-of-the-art implementation while reducing hardware complexity, even being lower than that of the deterministic GDBF. The efficiency of our proposed method is verified through the ASIC 90nm CMOS technology implementations and decoding simulations.
This paper deals with the hardware implementation of the recently introduced Probabilistic Gradient-Descent Bit-Flipping (PGDBF) decoder. The PGDBF is a new type of hard-decision decoder for Low-Density Parity-Check (LDPC) code, with improved error correction performance thanks to the introduction of deliberate random perturbation in the computing units. In the PGDBF, the random perturbation operates during the bit-flipping step, with the objective to avoid the attraction of so-called trapping-sets of the LDPC code. In this paper, we propose an efficient hardware architecture which minimizes the resource overhead needed to implement the random perturbations of the PGDBF. Our architecture is based on the use of a Short Random Sequence (SRS) that is duplicated to fully apply the PGDBF decoding rules, and on an optimization of the maximum finder unit. The generation of good SRS is crucial to maintain the outstanding decoding performance of PGDBF, and we propose two different methods with equivalent hardware overheads, but with different behaviors on different LDPC codes. Our designs show that the improved PGDBF performance gains can be obtained with a very small additional complexity, therefore providing a competitive hard-decision LDPC decoding solution for current standards.
Probabilistic gradient descent bit-flipping (PGDBF) is the state-of-the-art hard-decision algorithm for decoding low-density parity-check (LDPC) codes on binary symmetric channel (BSC). However, there still exists a considerable performance gap between the PGDBF algorithm and soft-decision algorithms, especially in the error-floor region. To bridge this performance gap, a tabu-list aided PGDBF (T-PGDBF) algorithm is proposed in this paper. In the T-PGDBF algorithm, a tabu-list is employed to help the decoding escape from trapping sets, which is the main cause of the error-floor phenomenon. The bits which are flipped in the current iteration will be added to the tabu-list to prevent them being flipped in the next iteration. Simulation results show that the T-PGDBF algorithm offers a significant performance gain when compared to the PGDBF algorithm, which can reach that of soft-decision algorithms. We also present the hardware architecture to implement the T-PGDBF algorithm. Synthesis results show that the improved performance offered by the T-PGDBF algorithm can be obtained with a small hardware overhead.
This paper presents a new high-throughput, low-complexity Bit Flipping (BF) decoder for Low-Density Parity-Check (LDPC) codes on the Binary Symmetric Channel (BSC), called Probabilistic Parallel Bit Flipping (PPBF). The advantage of PPBF comes from the fact that, no global operation is required during the decoding process and from that, all of the computations could be parallelized and localized at the computing units. Also in PPBF, the probabilistic feature in flipping the Variable Node (VN) is incorporated for all satisfaction level of its CN neighbors. This type of probabilistic incorporation makes PPBF more dynamic to correct some error patterns which are unsolvable by other BF decoders. PPBF offers a faster decoding process with an equivalent error correction performance to the Probabilistic Gradient Descent Bit Flipping (PGDBF) decoder, which is better than all so-far introduced BF decoders in BSC channel. A hardware implementation architecture of PPBF is also presented in this paper with detailed circuits for the probabilistic signal generator and processing units. The implementation of PPBF on FPGA confirms that, the PPBF complexity is much lower than that of the PGDBF and even lower than the one of the deterministic Gradient Descent Bit Flipping (GDBF) decoder. The good decoding performance along with the high throughput and low complexity lead PPBF decoder to become a brilliant candidate for the next generation of communication and storage standards.
Existing bit-flipping algorithms, when used for irregular low-density parity-check (LDPC) codes, often suffer from performance degradation due to the imbalance caused by the irregular codeword structure. To alleviate this problem, this paper presents a column-weighted probabilistic gradient-descent bit-flipping (CW-PGDBF) decoder for irregular LDPC codes. Different weighting factors are allocated to variable nodes with different column weights to solve the imbalance. Furthermore, a modified probabilistic flipping rule is employed to reduce the hardware complexity while maintaining the error-correction performance. Simulation results demonstrate that the proposed algorithm can achieve significantly improved performance compared with other BF-based algorithms for irregular codes. Additionally, a hardware architecture for the CW-PGDBF decoder is proposed with acceptable hardware overhead compared to the original PGDBF decoder.