LDPC codes from cubic semisymmetric graphs

This letter gives a random construction for Low Density Parity Check (LDPC) codes, which uses an iterative algorithm to avoid short cycles in the Tanner graph. The construction method has great flexible choice in LDPC code’s parameters including codelength, code rate, the least girth of the graph, 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. Systemic codes have many applications in digital communication, so this letter proposes a construction of the generator matrix of systemic LDPC codes from the parity check matrix. Simulations show that the method performs well with iterative decoding.

Low Density Parity Check (LDPC) codes have capacity-approaching performance over several channels of interest. In practice, for good block-error rate performance, the girth of the Tanner graph of an LDPC code needs to be as high as possible. In theory, to show that block-error rate approaches zero for increasing block-lengths, the girth of the Tanner graph sequence needs to tend to infinity with block-length. To meet these requirements, we construct sequences of large-girth irregular LDPC codes for a given degree-distribution pair (DDP) by applying a general node splitting algorithm on large girth graphs. The obtained Tanner graph meets the required DDP up to a suitable approximation. By optimizing the node-splitting method and using suitable large-girth graphs, we show code constructions with smaller block length for the same girth, when compared to previous constructions. Similar gains in block length are observed in the construction of sequences of large-girth protograph LDPC codes. Simulations, over a binary erasure channel, confirm the gains in block-error rate obtained by the large girth construction.

Low-density Parity check codes (LDPC) are gaining interest for high data rate application in both terrestrial and spatial communications which requires low bit error rate. This paper proposes an algorithm through which the best parity check matrix is obtained for irregular low density parity check codes, which reduces the Bit Error Rate ,Frame Error Rate and computational complexity. Tanner graph also called a bipartite graph format in an Low Density Parity Check (LDPC) code design contains many short cycles; it will produce a computational complexity in a code design process and it degrades the code performance. To overcome this effect, we proposed an new algorithm which obtains a best final parity check matrix. Throughout the paper we will give the detailed description of an efficient algorithm proposed. The complexity is studied graphically and analytically. Using proposed algorithm, cycles of length four observed in corresponding matrix are removed. Each matrix is evaluated over a noisy Additive White Gaussian Noise (AWGN) channel. Bit Error rate and Frame error rate is calculated. The results are compared with randomly generated best parity check matrix. Simulation results shows that proposed method successfully overcomes the computational complexity and meets the near Shannon limit.

Low-density parity-check (LDPC) codes have been the subject of much interest due to the fact that they can perform near the Shannon limit. In this paper we present a construction of LDPC codes from cubic symmetric graphs. The codes constructed are (3, 3)-regular and the vast majority of the corresponding Tanner graphs have girth greater than four. We analyse properties of the codes obtained and present bounds for the code parameters, the dimension and the minimum distance. Furthermore, we give an expression for the variance of the syndrome weight of the codes constructed. Information on the LDPC codes constructed from bipartite cubic symmetric graphs with less than 200 vertices is presented as well. Some of the codes constructed are optimal, and some have an additional property of being self-orthogonal or linear codes with complementary dual (LCD codes).

Several combinatorial properties of low-density parity-check (LDPC) codes, such as minimum distance, diameter, stopping number, girth and cycle-length distribution of the corresponding Tanner graph, are known to influence their performance under iterative decoding. Recently, a new class of combinatorial configurations, termed trapping sets, was shown to be of significant importance in determining the properties of LDPC codes in the error-floor region. Very little is known both about the existence/parameters of trapping sets in structured LDPC codes and about possible techniques for reducing their negative influence on the code's performance. In this paper, we address both these problems from an algorithmic and combinatorial perspective. We first provide a numerical study of the trapping phenomena for the Margulis code, which exhibits a fairly high error-floor. Based on this analysis, conducted for two different implementations of iterative belief propagation, we propose a novel decoding process, termed averaged decoding. Averaged decoding provides for a significant reduction in the number of incorrectly decoded frames in the error-floor region of the Margulis code. Furthermore, based on the results of the algorithmic approach, we suggest a novel combinatorial characterizations of trapping sets in the class of LDPC codes based on finite geometries. Projective geometry LDPC codes are suspected to have extremely low error-floors, which is a property that we may attribute to the non-existence of certain small trapping sets in the code graph.

This dissertation presents a systematic exposition on finite-block-length coding theory and practice. We begin with the task of identifying the maximum achievable rates over noisy, finite-block-length constrained channels, referred to as (e, n)-capacity Cen, with e denoting target block-error probability and n the block length. We characterize how the optimum codes look like over finite-block-length constrained channels. Constructing good, short-block-length error-correcting codes defined on sparse graphs is the focus of the thesis. We propose a new, general method for constructing Tanner graphs having a large girth by progressively establishing edges or connections between symbol and check nodes in an edge-by-edge manner, called progressive edge-growth (PEG) construction. Lower bounds on the girth of PEG Tanner graphs and on the minimum distance of the resulting low-density parity-check (LDPC) codes are derived in terms of parameters of the graphs. The PEG construction attains essentially the same girth as Gallager's explicit construction for regular graphs, both of which meet or exceed an extension of the Erdos-Sachs bound. The PEG construction proves to be powerful for generating good, short-block-length binary LDPC codes. Furthermore, we show that the binary interpretation of GF(2b) codes on the cycle Tanner graph TG(2, dc), if b grows sufficiently large, can be used over the binary-input additive white Gaussian noise (AWGN) channel as good code for optimum and good code for iterative decoding. Codes on sparse graphs are often decoded iteratively by a sum-product algorithm (SPA) with low complexity. We investigate efficient digital implementations of the SPA for decoding binary LDPC codes from both the architectural and algorithmic point of view, and describe new reduced-complexity derivatives thereof. The unified treatment of decoding techniques for LDPC codes provides flexibility in selecting the appropriate design point in high-speed applications from a performance, latency, and computational complexity perspective.

Channel coding is an important building block in communication systems since it ensures the quality of service. Irregular repeat-accumulate (IRA) codes belong to the class of low-density parity-check (LDPC) codes and even outperform the recently introduced turbo-codes of current communication standards. The advantage of IRA codes over LDPC codes is that they come with a linear-time encoding complexity. IRA codes can be represented by a Tanner graph with arbitrary connections between nodes of given degrees. The implementation complexity of IRA decoders is dominated by the randomness of these connections. In this paper, we present a scalable partly parallel IRA decoder architecture. We present a joint graph-decoder design to parallelize IRA codes which can be efficiently processed by this decoder without any RAM access conflicts. We show design examples of these IRA codes which outperform the UMTS turbo-code by 0.2 dB.

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In this paper, we focus on the Generalized Belief Propagation (GBP) algorithm to solve trapping sets in Low-Density Parity-Check (LDPC) codes. Trapping sets are topological structures in Tanner graphs of LDPC codes that are not correctly decoded by Belief Propagation (BP), leading to exhibit an error-floor in the Bit-Error Rate (BER). Stemming from statistical physics of spin glasses, GBP consists in passing messages between clusters of Tanner graph nodes in another graph called the region-graph. Here, we introduce a specific clustering of nodes, based on a novel local loopfree principle, that breaks trapping sets such that the resulting region-graph is locally loopfree. We then construct a hybrid decoder made of BP and GBP that proves to be a powerful decoder as it clearly improves the BER and defeats the error-floor.

PMD mitigation through interleaving LDPC codes with polarization scramblers

In this paper we propose an innovative channel coding scheme called 'Accumulate Repeat Accumulate codes' (ARA). This class of codes can be viewed as serial turbo-like codes, or as a subclass of Low Density Parity Check (LDPC) codes, thus belief propagation can be used for iterative decoding of ARA codes on a graph. The structure of encoder for this class can be viewed as precoded Repeat Accumulate (RA) code or as precoded Irregular Repeat Accumulate (IRA) code, where simply an accumulator is chosen as a precoder. Thus ARA codes have simple, and very fast encoder structure when they representing LDPC codes. Based on density evolution for LDPC codes through some examples for ARA codes, we show that for maximum variable node degree 5 a minimum bit SNR as low as 0.08 dB from channel capacity for rate 1/2 can be achieved as the block size goes to infinity. Thus based on fixed low maximum variable node degree, its threshold outperforms not only the RA and IRA codes but also the best known LDPC codes with the dame maximum node degree. Furthermore by puncturing the accumulators any desired high rate codes close to code rate 1 can be obtained with thresholds that stay close to the channel capacity thresholds uniformly. Iterative decoding simulation results are provided. The ARA codes also have projected graph or protograph representation that allows for high speed decoder implementation.

In this paper, we propose an efficient exhaustive search algorithm for elementary trapping sets (ETS) of variable-regular low-density parity-check (LDPC) codes. Recently, Karimi and Banihashemi proposed a characterization of ETSs, which was based on viewing an ETS as a layered superset (LSS) of a short cycle in the code's Tanner graph. A notable advantage of LSS characterization is that it corresponds to a simple LSS-based search algorithm (expansion technique) that starts from short cycles of the graph and finds the ETSs with LSS structure efficiently. Compared to the LSS-based search, which is based on a single LSS expansion technique, the new search algorithm involves two additional expansion techniques. The introduction of the new techniques results in significant improvements in search efficiency compared to the LSS-based search. We prove that using the three expansion techniques, each and every ETS structure can be obtained starting from a simple cycle. We also provide extensive simulation results that show, compared to the LSS-based search, up to three orders of magnitude improvement in search speed and memory requirements can be achieved.

Low Density Parity Check (LDPC) codes are Shannon-Limit approaching codes. Shannon stated the theoretical bounds for digital data transmission in a given channel capacity with error correction coding. Spread Spectrum also called as CDMA (Code Division Multiple Access) is an efficient way of radio wave communication deployed in commercial broadcasts, ham radios, military communications, police radios, etc. CDMA is a means of signal transmission in which the signal frequency is spread over a very wide bandwidth. Spreading of data is achieved prior to transmission with the help of an independent code called ‘Pseudo Noise Code’ (PN sequences), which is subsequently used in the receiver end to de-spread the signal received in order to recover the signal data. PN sequences based on prime numbers like Legendre & Weil sequences are used in the present work. An impressive part of LDPC lies in its decoding algorithm. Sum-Product Algorithm (SPA) decoding offers the best error performance amongst all the types of decoding techniques of LDPC codes and thus applied here. This paper demonstrates an idea of introducing LDPC codes along with DS-Spread Spectrum technique using Legendre & Weil sequences. In the present work, the encoding and decoding algorithms are applied after spreading of the signal. SPA decoding technique is successfully employed and simulated results are plotted taking an example design to illustrate the concepts discussed above.

The Tanner graph is described. A girth in the Tanner graph is equivalent to a short cycle of 1- symbols in the parity check matrix. The condition called row-column constraint is introduced, in order to allow practical decoding procedures. They are based on the sum-product algorithm, which is briefly outlined. Regular and irregular LDPC codes are introduced. The firstly proposed LDPC codes by Gallager and MacKay-Neal are reviewed. The following main families of more recent LDPC codes are briefly described: codes based on protographs, codes constructed employing repeat and accumulation devices, codes derived from the decomposition of finite geometries, codes obtained starting from MDS codes. Furthermore codes obtained from superimposition or by circulant expansion are analysed. Masking and row or column splitting can be employed for reducing the 1-symbol density and breaking short cycles. The effects of such procedures on the code rate are stressed. For instance, circulant expansion and masking do not vary the code rate, whereas row (column) splitting increases (reduces) it. Irregular LDPC codes can be designed by means of proper rules progressively adding edges to the Tanner graph. A statistical treatment, called density evolution, is presented, in order to obtain asymptotic best performance at different code rates, taking into account the intrinsic nature of the sum-product decoding algorithm. A first approach to LDPC convolutional codes is presented, starting from good known LDPC block codes. The use of unwrapping is suggested, in particular with suitable QC codes, or a derivation from properly modified H-extension is reviewed.

Belief propagation decoding of binary LDPC codes combines three independent probability estimates, a-priori, intrinsic, and extrinsic information, iterations along the Tanner graph are estimating the value of the received bits, the likelihood of the values increasing with each iteration. The a-priori estimate of the information bits is a measure of the source statistics for generating bit values 0 and 1 (mapped to ±1), reflecting bias and redundancy in the information sequence itself. In this paper, we have modified the a-priori estimate to incorporate memory properties present in the transmitted information sequence resulting from a Markov source. We consider two principle alternatives for decoding. The first option is to use the Markov dependencies directly as further links between variable nodes in the Tanner graph of the LDPC code. The second alternative is to see the Markov source and the LDPC code as a serial concatenation asking for a Turbo iterative decoding between the two corresponding decoders. The latter comes with significant complexity compared to the direct embedding into the LDPC Tanner graph. Especially, one modification applying Jensen’s inequality leads to a very low decoding complexity at no performance loss. The Turbo scheme’s performance depends on the scheduling. Superior performance can be achieved with sufficient iterations in the LDPC decoder itself.