263 research outputs found

    Characterization and Efficient Search of Non-Elementary Trapping Sets of LDPC Codes with Applications to Stopping Sets

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    In this paper, we propose a characterization for non-elementary trapping sets (NETSs) of low-density parity-check (LDPC) codes. The characterization is based on viewing a NETS as a hierarchy of embedded graphs starting from an ETS. The characterization corresponds to an efficient search algorithm that under certain conditions is exhaustive. As an application of the proposed characterization/search, we obtain lower and upper bounds on the stopping distance smins_{min} of LDPC codes. We examine a large number of regular and irregular LDPC codes, and demonstrate the efficiency and versatility of our technique in finding lower and upper bounds on, and in many cases the exact value of, smins_{min}. Finding smins_{min}, or establishing search-based lower or upper bounds, for many of the examined codes are out of the reach of any existing algorithm

    Minimum Pseudoweight Analysis of 3-Dimensional Turbo Codes

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    In this work, we consider pseudocodewords of (relaxed) linear programming (LP) decoding of 3-dimensional turbo codes (3D-TCs). We present a relaxed LP decoder for 3D-TCs, adapting the relaxed LP decoder for conventional turbo codes proposed by Feldman in his thesis. We show that the 3D-TC polytope is proper and CC-symmetric, and make a connection to finite graph covers of the 3D-TC factor graph. This connection is used to show that the support set of any pseudocodeword is a stopping set of iterative decoding of 3D-TCs using maximum a posteriori constituent decoders on the binary erasure channel. Furthermore, we compute ensemble-average pseudoweight enumerators of 3D-TCs and perform a finite-length minimum pseudoweight analysis for small cover degrees. Also, an explicit description of the fundamental cone of the 3D-TC polytope is given. Finally, we present an extensive numerical study of small-to-medium block length 3D-TCs, which shows that 1) typically (i.e., in most cases) when the minimum distance dmind_{\rm min} and/or the stopping distance hminh_{\rm min} is high, the minimum pseudoweight (on the additive white Gaussian noise channel) is strictly smaller than both the dmind_{\rm min} and the hminh_{\rm min}, and 2) the minimum pseudoweight grows with the block length, at least for small-to-medium block lengths.Comment: To appear in IEEE Transactions on Communication

    Windowed Decoding of Protograph-based LDPC Convolutional Codes over Erasure Channels

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    We consider a windowed decoding scheme for LDPC convolutional codes that is based on the belief-propagation (BP) algorithm. We discuss the advantages of this decoding scheme and identify certain characteristics of LDPC convolutional code ensembles that exhibit good performance with the windowed decoder. We will consider the performance of these ensembles and codes over erasure channels with and without memory. We show that the structure of LDPC convolutional code ensembles is suitable to obtain performance close to the theoretical limits over the memoryless erasure channel, both for the BP decoder and windowed decoding. However, the same structure imposes limitations on the performance over erasure channels with memory.Comment: 18 pages, 9 figures, accepted for publication in the IEEE Transactions on Information Theor

    The Trapping Redundancy of Linear Block Codes

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    We generalize the notion of the stopping redundancy in order to study the smallest size of a trapping set in Tanner graphs of linear block codes. In this context, we introduce the notion of the trapping redundancy of a code, which quantifies the relationship between the number of redundant rows in any parity-check matrix of a given code and the size of its smallest trapping set. Trapping sets with certain parameter sizes are known to cause error-floors in the performance curves of iterative belief propagation decoders, and it is therefore important to identify decoding matrices that avoid such sets. Bounds on the trapping redundancy are obtained using probabilistic and constructive methods, and the analysis covers both general and elementary trapping sets. Numerical values for these bounds are computed for the [2640,1320] Margulis code and the class of projective geometry codes, and compared with some new code-specific trapping set size estimates.Comment: 12 pages, 4 tables, 1 figure, accepted for publication in IEEE Transactions on Information Theor

    Decoding LDPC Codes with Probabilistic Local Maximum Likelihood Bit Flipping

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    Communication channels are inherently noisy making error correction coding a major topic of research for modern communication systems. Error correction coding is the addition of redundancy to information transmitted over communication channels to enable detection and recovery of erroneous information. Low-density parity-check (LDPC) codes are a class of error correcting codes that have been effective in maintaining reliability of information transmitted over communication channels. Multiple algorithms have been developed to benefit from the LDPC coding scheme to improve recovery of erroneous information. This work develops a matrix construction that stores the information error probability statistics for a communication channel. This combined with the error correcting capability of LDPC codes enabled the development of the Probabilistic Local Maximum Likelihood Bit Flipping (PLMLBF) algorithm, which is the focus of this research work

    The Manifestation of Stopping Sets and Absorbing Sets as Deviations on the Computation Trees of LDPC Codes

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    The error mechanisms of iterative message-passing decoders for low-density parity-check codes are studied. A tutorial review is given of the various graphical structures, including trapping sets, stopping sets, and absorbing sets that are frequently used to characterize the errors observed in simulations of iterative decoding of low-density parity-check codes. The connections between trapping sets and deviations on computation trees are explored in depth using the notion of problematic trapping sets in order to bridge the experimental and analytic approaches to these error mechanisms. A new iterative algorithm for finding low-weight problematic trapping sets is presented and shown to be capable of identifying many trapping sets that are frequently observed during iterative decoding of low-density parity-check codes on the additive white Gaussian noise channel. Finally, a new method is given for characterizing the weight of deviations that result from problematic trapping sets
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