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IEEE International Symposium on Information Theory (ISIT)

A revolving iterative algorithm for decoding algebraic quasi-cyclic LDPC codes

Keke Liu, Shu Lin, Khaled Abdel-Ghaffar

Published Jul 1, 2013• 2 citations
Publisher

Abstract

An effective reduced-complexity min-sum algorithm for decoding algebraic quasi-cyclic LDPC codes is presented. The proposed decoding algorithm significantly reduces the hardware implementation complexity, the size of memory required to store information, and the computational complexity of a decoder with no or a small loss in performance compared to the scaled min-sum algorithm.

DOI
10.1109/isit.2013.6620708

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An effective reduced-complexity min-sum algorithm for decoding algebraic quasi-cyclic LDPC codes is presented. The proposed decoding algorithm significantly reduces the hardware implementation complexity, the size of memory required to store information, and the computational complexity of a decoder with no or a small loss in performance compared to the scaled min-sum algorithm.
Cyclic and quasi-cyclic algebraic LDPC codes constructed based on finite fields, finite geometries, and combinatorial designs can achieve excellent performance in terms of error rate, error floor and rate of decoding convergence with iterative decoding. However, the relatively high density of the parity-check matrix of an algebraic cyclic or quasi-cyclic LDPC code makes the hardware implementation complexity of the decoder quite large, which may be a critical issue in practical applications. This paper presents an effective reduced-complexity algorithm for decoding algebraic cyclic and quasi-cyclic LDPC codes based on the block cyclic structure and cyclic grouping of the rows of their parity-check matrices. The decoding of a code is carried out based on a single small submatrix of the parity-check matrix of the code in a revolving manner. The proposed decoding algorithm significantly reduces the hardware implementation complexity and the size of memory required to store information.
Iterative decoding of non-binary LDPC codes is currently performed using either the sum-product or the min-sum algorithms or slightly different versions of them. In this paper, several low-complexity quasi-optimal iterative algorithms are proposed for decoding non-binary codes. The min-max algorithm is one of them and it has the benefit of two possible LLR domain implementations: a standard implementation, whose complexity scales as the square of the Galois field's cardinality and a reduced complexity implementation called selective implementation, which makes the min-max decoding very attractive for practical purposes.
Low-density parity-check (LDPC) have been shown to have good error correcting performance approaching Shannon’s limit. Good error correcting performance enables efficient and reliable communication. However, a LDPC code decoding algorithm needs to be executed efficiently to meet cost , time, power and bandwidth requirements of target applications. Quasi-cyclic low-density parity-check (QC-LDPC) codes are an important subclass of LDPC codes that are known as one of the most effective error controlling methods. Quasi cyclic codes are known to possess some degree of regularity. Many important communication standards such as DVB-S2 and 802.16e use these codes. The proposed Optimized Min-Sum decoding algorithm performs very close to the Sum-Product decoding while preserving the main features of the Min-Sum decoding, that is low complexity and independence with respect to noise variance estimation errors.Proposed decoder is well matched for VLSI implementation and will be implemented on Xilinx FPGA family
Layered decoding is well appreciated in low-density parity-check (LDPC) decoder implementation since it can achieve effectively high decoding throughput with low computation complexity. This work, for the first time, addresses low-complexity column-layered decoding schemes and very-large-scale integration (VLSI) architectures for multi-Gb/s applications. At first, the min-sum algorithm is incorporated into the column-layered decoding. Then algorithmic transformations and judicious approximations are explored to minimise the overall computation complexity. Compared to the original column-layered decoding, the new approach can reduce the computation complexity in check node processing for high-rate LDPC codes by up to 90% while maintaining the fast convergence speed of layered decoding. Furthermore, a relaxed pipelining scheme is presented to enable very high clock speed for VLSI implementation. Equipped with these new techniques, an efficient decoder architecture for quasi-cyclic LDPC codes is developed and implemented with 0.13 µm VLSI implementation technology. It is shown that a decoding throughput of nearly 4 Gb/s at a maximum of 10 iterations can be achieved for a (4096, 3584) LDPC code. Hence, this work has facilitated practical applications of column-layered decoding and particularly made it very attractive in high-speed, high-rate LDPC decoder implementation.
Low-density parity-check (LDPC) codes have attracted tremendous attention for their excellent error-correction performance and inherent fitness for high-parallelism implementation. With great complexity reduction and friendly for hardware implementation, the min-sum (MS) decoding algorithm for LDPC codes gains widespread applications in practical. In the MS decoding algorithm, finding the first two minima among input variable-to-check messages is the most complex part. To further reduce the complexity of the MS decoding algorithm, an efficient single-minimum (single-min) based layered MS decoding for quasi-cyclic LDPC (QC-LDPC) codes is proposed in this paper. It is found that statistically the difference between the first minimum and second minimum increases as the decoding becomes more convergent. Accordingly, in the proposed single-min scheme, the second minimum is approximately derived based on the first minimum and an index indicating the convergence degree. Besides, some other metrics that are easily obtained are used to refine the second minimum. Simulation results show that the proposed method can significantly improve the error-correction performance and lower the average number of iterations, compared with the state-of-the-art single-min based MS decoding algorithms.