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Collected scientific works of Ukrainian State University of Railway Transport

ANALYSIS OF METHODS OF CONSTRUCTION AND ENCODING LOW-DENSITY PARITY CHECK CODES

Alexey Stanyslavovych Volkov, Stanislav Sergeevich Zhuchenko

Published Dec 22, 2014• 0 citations

Abstract

The article analyzes the famous methods of construction and representations of the check matrix block and convolutional codes with low-density parity-check. The analysis the famous methods of coding block and convolutional codes with low-density parity-check. Showed advantages and disadvantages of the known methods and allowed to identify and formulate a scientific and technical challenge. Shows the method of representation of the check matrix block codes with low-density parity-check using the Tanner graph, which like the check matrix is fully defines the parameters of the code. Check matrix of convolutional codes with low-density parity-check can also be described by Tanner graph, however, because the check matrix is infinite, then the Tanner graph is infinite. The analysis method of constructing the parity check matrix of block codes with low-density parity-check, which is based on a shortened Reed-Solomon code. Codes constructed in this method have a Tanner graph girth at least 6, which allows them to effectively use iterative decoding methods.

DOI: 10.18664/1994-7852.149.2014.82726

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The article analyzes the famous methods of construction and representations of the check matrix block and convolutional codes with low-density parity-check. The analysis the famous methods of coding block and convolutional codes with low-density parity-check. Showed advantages and disadvantages of the known methods and allowed to identify and formulate a scientific and technical challenge. Shows the method of representation of the check matrix block codes with low-density parity-check using the Tanner graph, which like the check matrix is fully defines the parameters of the code. Check matrix of convolutional codes with low-density parity-check can also be described by Tanner graph, however, because the check matrix is infinite, then the Tanner graph is infinite. The analysis method of constructing the parity check matrix of block codes with low-density parity-check, which is based on a shortened Reed-Solomon code. Codes constructed in this method have a Tanner graph girth at least 6, which allows them to effectively use iterative decoding methods.

In decoding a linear block code C by iterative decoding algorithms, these algorithms are essentially applied on a graphical representation of C such as Tanner graph and factor graph. Due to the impact of the structure of these graphs on the performance of code, we consider minimal parity check matrices(matrices with minimum number of nonzero entries) of product codes. An algorithm constructing such matrices is given. It turns out that under the given construction method, product coding technique is indeed a powerful method to construct irregular LDPC codes. Given two codes A and B, the construction method produces a Tanner graph with girth g := min {8, ga, gb} for the product code A ⊗ B, where ga and gb are the girth of Tanner graphs representing A and B, respectively. Simulation results confirm the positive practical impact of the minimality of the parity check matrices.

Due to their capacity approaching performance low-density parity-check (LDPC) codes gained a lot of attention in the last years. The parity-check matrix of the codes can be associated with a bipartite graph, called Tanner graph. To decrease the probability of decoding failure it is desirable to have LDPC codes with large girth of the associated Tanner graph. Moreover, to store such codes efficiently, it is desirable to have compact constructions for them. In this paper, we present constructions of LDPC convolutional codes with girth up to

12

using a special class of Latin squares and several lifting steps, which enables a compact representation of these codes. With these techniques, we can provide constructions for well-performing and efficiently storable time-varying and time-invariant LDPC convolutional codes as well as for LDPC block codes.

Low-density parity-check (LDPC) codes are a class of linear block codes with implementable decoders, which provide near-capacity performance on a large set of data-transmission and data-storage channels. LDPC codes were invented by Gallager in his 1960 doctoral dissertation and were mostly ignored during the 35 years that followed. One notable exception is the important work of Tanner in 1981, in which Tanner generalized LDPC codes and introduced a graphical representation of LDPC codes, now called a Tanner graph. The study of LDPC codes was resurrected in the mid 1990s with the work of MacKay, Luby, and others [3–6], who noticed, apparently independently of Gallager's work, the advantages of linear block codes with sparse (low-density) parity-check matrices. This chapter introduces LDPC codes and creates a foundation for further study of LDPC codes in later chapters. We start with the fundamental representations of LDPC codes via parity-check matrices and Tanner graphs. We then learn about the decoding advantages of linear codes that possess sparse parity-check matrices. We will see that this sparseness characteristic makes the code amenable to various iterative decoding algorithms, which in many instances provide near optimal performance. Gallager of course recognized the decoding advantages of such low-density parity-check codes and he proposed a decoding algorithm for the BI-AWGNC and a few others for the BSC. These algorithms have received much scrutiny in the past decade, and are still being studied.

An algebraic construction for a class of low-density parity-check (LDPC) convolutional codes is presented on the basis of difference families associated with the code generator matrix. It can be shown that these codes have girth of at least 10 on the Tanner graph which is independent of either the size of the code generator matrix or the minimum Hamming distance of the codes. The code construction guarantees the independence of the messages exchanged in the belief propagation decoding process during two successive decoding iterations. Computer simulations show that over the additive white Gaussian noise channel, the best error performance of these codes at moderate signal-to- noise ratio values is practically obtained using only three to five iterations.

This paper gives a new random construction for LDPC codes, which uses an iterative algorithm to avoid short cycles in the Tanner graph. The construction method has low complexity and great flexible choice in LDPC code parameters including code length, code rate, least girth of the graph, and the weight of column and row in the parity-check matrix. The method can be applied to the irregular LDPC codes and strict regular LDPC codes. Simulations show that the method performs well with iterative decoding.