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IEEE International Symposium on Information Theory (ISIT)

Distance spectrum estimation of LDPC convolutional codes

Hua Ren Zhou, David G. M. Mitchell, Norbert Goertz, Daniel J. Costello

Published Jul 1, 2012• 5 citations
Publisher

Abstract

Time-invariant low-density parity-check convolutional codes (LDPC-CCs) derived from corresponding quasi-cyclic (QC) LDPC block codes (LDPC-BCs) can be described by a polynomial syndrome former matrix (polynomial-domain transposed parity-check matrix). In this paper, an estimation of the distance spectrum of time-invariant LDPC-CCs is obtained by splitting the polynomial syndrome former matrix into submatrices representing “super codes” and then evaluating the linear dependence between codewords of the corresponding super codes. This estimation results in an upper bound on the minimum free distance of the original code and, additionally, a lower bound on the number of codewords Awwith Hamming weight w.

DOI
10.1109/isit.2012.6284234

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Time-invariant low-density parity-check convolutional codes (LDPC-CCs) derived from corresponding quasi-cyclic (QC) LDPC block codes (LDPC-BCs) can be described by a polynomial syndrome former matrix (polynomial-domain transposed parity-check matrix). In this paper, an estimation of the distance spectrum of time-invariant LDPC-CCs is obtained by splitting the polynomial syndrome former matrix into submatrices representing “super codes” and then evaluating the linear dependence between codewords of the corresponding super codes. This estimation results in an upper bound on the minimum free distance of the original code and, additionally, a lower bound on the number of codewords Awwith Hamming weight w.
Time-invariant low-density parity-check convolutional codes (LDPCccs) can be constructed from a polynomial form of a parity-check matrix that defines quasi-cyclic LDPC block codes based on circulant matrices. Based on this polynomial matrix, we discuss the relationships between the polynomial domain and the time domain parity-check and syndrome former matrices with respect to cycle properties. We present a novel, simple way to describe cycles in the polynomial version of the syndrome former matrix and we exploit this concept in a new cycle counting algorithm.
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.
Time-variant Low-Density Parity-Check convolutional codes (LDPCccs) can be derived from unwrapping Quasi-Cyclic (QC) LDPC block codes. Rather than analyzing cycles in a large-scale time-domain parity check matrix, we propose a new way to describe cycle properties in compact form by a “polynomial syndrome former”, i.e., a syndrome former in an equivalent polynomial representation. According to the relationship of cycle structures between time- and polynomial-domain syndrome formers, we present a cycle counter algorithm to analyze cycle properties in time-variant LDPCccs as well as in time-invariant LDPCccs.
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.
Quasi-cyclic (QC) low-density parity-check (LDPC) codes are an important instance of proto-graph-based LDPC codes. In this paper we present upper bounds on the minimum Hamming distance of QC LDPC codes and study how these upper bounds depend on graph structure parameters (like variable degrees, check node degrees, girth) of the Tanner graph and of the underlying proto-graph. Moreover, for several classes of proto-graphs we present explicit QC LDPC code constructions that achieve (or come close to) the respective minimum Hamming distance upper bounds. Because of the tight algebraic connection between QC codes and convolutional codes, we can state similar results for the free Hamming distance of convolutional codes. In fact, some QC code statements are established by first proving the corresponding convolutional code statements and then using a result by Tanner that says that the minimum Hamming distance of a QC code is upper bounded by the free Hamming distance of the convolutional code that is obtained by “unwrapping” the QC code.
In this paper, we propose an efficient method for computing the weight spectrum of LDPC convolutional codes constructed from quasi-cyclic block codes. In the proposed method, we reduce the memory size of their parity-check matrices with the same distance profile as original codes, and apply a forward and backward tree search algorithm to the parity-check matrices of reduced memory. We show numerical results of computing the minimum free distance and the low-part weight spectrum of LDPC convolutional codes of memory about 130.