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JOURNAL OF ELECTRONICS INFORMATION TECHNOLOGY

New Ensemble of Time-invariant LDPC Convolutional Codes with High Performance

Liwei Mu, Xingcheng Liu, Zhang Han

Published Jan 1, 2016• 0 citations
OpenAlex

Abstract

③ Abstract: In this paper, a new ensemble of the polynomial matrix of a time-invariant LDPC convolutional code is proposed. Based on the method of deriving time-invariant LDPC convolutional codes from QC (Quasi-Cyclic)- LDPC block codes, the elements over finite fields are used to generate directly the polynomial parity-check matrices of LDPC convolutional codes. A concrete example of using MDS (Maximum-Distance Separable) convolutional codes to derive the polynomial matrices is given. The proposed method ensures the fast encoding property, maximum encoding memory and designed rate. Simulation results show that the proposed LDPC convolutional codes exhibit low error floor and good decoding performance under BP (Belief Propagation) decoding algorithm over AWGN (Additive White Gaussian Noise) channel.

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An Algebraic Construction of Quasi-Cyclic LDPC Codes Based on the Conjugates of Primitive Elements over Finite Fields

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Recently, there have been major developments in utilizing the finite fields to construct Low-density Parity-check (LDPC) codes. In this correspondence, an algebraic approach based on the conjugates of primitive elements over finite fields to construct Quasi-Cyclic (QC) Low-Density Parity-Check codes is presented. Proposed QC-LDPC codes provide an excellent error performance with Belief Propagation (BP) decoding over an Additive White Gaussian Noise (AWGN) channel. Based on numerical results, the performance analysis shows that the proposed QC-LDPC codes perform as well as the randomly constructed Progressive edge growth (PEG) LDPC codes and algebraic QC-LDPC in the lower signal-to-noise ratio (SNR) region but outperform their counterparts in the higher SNR region. Also, the codes constructed are QC in nature, so the encoding can be done with shift register circuits having linear complexity.

IEEE International Conference on Communication Technology (ICCT)•Oct 1, 2018•3 citations
③ Abstract: In this paper, a new ensemble of the polynomial matrix of a time-invariant LDPC convolutional code is proposed. Based on the method of deriving time-invariant LDPC convolutional codes from QC (Quasi-Cyclic)- LDPC block codes, the elements over finite fields are used to generate directly the polynomial parity-check matrices of LDPC convolutional codes. A concrete example of using MDS (Maximum-Distance Separable) convolutional codes to derive the polynomial matrices is given. The proposed method ensures the fast encoding property, maximum encoding memory and designed rate. Simulation results show that the proposed LDPC convolutional codes exhibit low error floor and good decoding performance under BP (Belief Propagation) decoding algorithm over AWGN (Additive White Gaussian Noise) channel.
Low-density parity-check (LDPC) block codes invented by Gallager in 1962 achieve exceptional error performance on a wide variety of communication channels. LDPC convolutional codes are the convolutional counterparts of LDPC block codes. This dissertation describes techniques for the design of LDPC convolutional codes, and analyzes their distance properties and iterative decoding convergence thresholds. The construction of time invariant LDPC convolutional codes by unwrapping the Tanner graph of algebraically constructed quasi-cyclic LDPC codes is described. The convolutional codes outperform the quasi-cyclic codes from which they are derived. The design of parity-check matrices for time invariant LDPC convolutional codes by the polynomial extension of a base matrix is proposed. An upper bound on free distance, proving that time invariant LDPC codes are not asymptotically good, is obtained. The Tanner graph is used to describe a pipelined message passing based iterative decoder for LDPC convolutional codes that outputs decoding results continuously. The performance of LDPC block and convolutional codes are compared for fixed decoding parameters like computational complexity, processor complexity, and decoding delay. In each case, the LDPC convolutional code performance is at least as good as that of LDPC block codes. An analog circuit to implement pipelined decoding of LDPC convolutional codes is proposed. The distance properties of a permutation matrix based (time varying) ensemble of (J,K) regular LDPC convolutional codes is investigated. It is proved that these codes (for J > 2) have free distance increasing linearly with constraint length, i.e., they are asymptotically good. Further, the asymptotic free distance to constraint length ratio for the convolutional codes is several times larger than the minimum distance to block length ratio for corresponding LDPC block codes. Iterative decoding of terminated LDPC convolutional codes, based on the ensemble mentioned above, is analyzed, assuming transmission over a binary erasure channel. The structured irregularity of the codes leads to significantly better convergence thresholds compared to corresponding LDPC block codes. At the calculated thresholds, both the bit and block error probability can be made arbitrarily small. The results obtained suggest that the thresholds approach capacity with increasing J.
Time-invariant low-density parity-check convolutional codes (TI LDPC-CCs) can be represented by a polynomial-domain parity-check matrix derived from the corresponding quasi-cyclic (QC) LDPC block codes (LDPC-BCs), while time-varying (TV) LDPC-CCs can be obtained by unwrapping the parity-check matrices of LDPC-BCs. The cycle enumerators for TI and TV LDPC-CCs are compared. Based on the analysis of the graphical structures of short cycles in HT(D), we introduce a method of designing the polynomial syndrome former matrix HCRT(D) for LDPC-CCs. It eliminates short cycles and shows improved decoding performance on an additive white Gaussian noise (AWGN) channel with lower bit error ratio (BER) curves.
Low-Density Parity-Check convolutional codes (LD- PCccs) are very interesting for practical error-correction coding in wireless transmission as they have excellent performance and at the same time they allow for variable block size with low complexity encoding and decoding. As for all LDPC codes that are decoded by the sub-optimal (but highly efficient) Sum Product Algorithm, the cycles in the code graph are very important for the practical performance of the coding scheme. Time-invariant LDPCccs can be defined by a polynomial syndrome former (transposed parity-check matrix in polynomial form), that can be derived from corresponding Quasi-Cyclic (QC) LDPC block codes. Given the polynomial syndrome former with certain structures, unavoidable cycles with lengths ranging from 6 to 12 will be shown to exist. We provide some rules for designing good codes with respect to the shortest cycle in the code-graph, the girth of the code, which is a crucial parameter for its decoding performance.
In this paper, we present three construction methods of systematic regular LDPC-CCs based on randomly or algebraically structured sparse matrices. We provide an overview of LDPC-CCs and compares between Gallager’s, Mackay-Neal and Quasi-Cyclic (QC) construction methods, which are a good choice for many wireless applications due to their errors correction efficiency and low decoding complexity. At a rate-1/2, for the same size of the parity matrix to obtain the same encoding and decoding complexity, and using the Bit Flipping (BF) decoding algorithm over Additive White Gaussian Noise (AWGN) channel, we simulated the BER performance of a (3,6) regular LDPC convolutional codes, where the results showed that Gallager’s construction method (random) outperforms Mackay-Neal and QC ones.
A unified approach to search for and optimize codes determined by their sparse parity-check matrices is presented. Replacing the nonzero elements of a binary parity-check matrix (the base or parent matrix) either by circulants or by companion matrices of elements from a finite field GF(2m), we obtain quasi-cyclic low-density parity-check (LDPC) block codes and binary images of nonbinary LDPC block codes, respectively. By substituting monomials of a formal variable D, we obtain the polynomial description of an LDPC convolutional code. A set of performance measures applicable to different classes of LDPC codes is considered, and a greedy algorithm for code performance optimization is presented. The heart of the new optimization algorithm is a fast procedure for searching for LDPC codes with large girth of their Tanner graphs. For a few classes of LDPC codes, examples of codes combining good error-correcting performance with compact representation are obtained. In particular, we present optimized convolutional LDPC codes and conclude that the LDPC block codes are still superior to their convolutional counterparts if both decoding complexity and coding delay are considered. Moreover, a specific channel model can easily be embedded into the optimization loop. Thereby, the code can be optimized for a specific channel. The efficiency of such an optimization is demonstrated via an example of faster than Nyquist (FTN) signaling using LDPC codes. The FTN strategy combined with a rate R = 1/2 LDPC code of length 64800 optimized for effective data rate R = 3/4 gains more than 0.5 dB compared with the standard LDPC codes of the same rate and length. The obtained gain corresponds to transmission at the capacity of the binary input additive white Gaussian noise channel. In most numerical examples, we consider codes with bidiagonal structure of the parity-check matrix. This restriction preserves low encoding complexity and allows fair comparison with codes selected for communication standards.
Recently, there have been major developments in utilizing the finite fields to construct Low-density Parity-check (LDPC) codes. In this correspondence, an algebraic approach based on the conjugates of primitive elements over finite fields to construct Quasi-Cyclic (QC) Low-Density Parity-Check codes is presented. Proposed QC-LDPC codes provide an excellent error performance with Belief Propagation (BP) decoding over an Additive White Gaussian Noise (AWGN) channel. Based on numerical results, the performance analysis shows that the proposed QC-LDPC codes perform as well as the randomly constructed Progressive edge growth (PEG) LDPC codes and algebraic QC-LDPC in the lower signal-to-noise ratio (SNR) region but outperform their counterparts in the higher SNR region. Also, the codes constructed are QC in nature, so the encoding can be done with shift register circuits having linear complexity.