It is widely accepted that short cycles in Tanner graphs deteriorate the performance of message-passing algorithms. This discourages the use of these algorithms on Tanner graphs (TGs) of well-known algebraic codes such as Hamming codes, Bose-Chaudhuri-Hocquenghem codes, and Reed-Solomon codes. Yedidia et al. presented a method to generate code representations suitable for message-passing algorithms. This method does not guarantee a representation free of four-cycles. In this correspondence, we present an algorithm to convert an arbitrary linear block into a code with orthogonal parity-check equations. A combinatorial argument is used to prove that the algorithm guarantees a four-cycle free representation for any linear code. The effects of removing four-cycles on the performance of a belief propagation decoder for the binary erasure channel are studied in detail by analyzing the structures in different representations. Finally, we present bit-error rate (BER) and block-error rate (BLER) performance curves of linear block codes under belief propagation algorithms for the binary erasure channel and the additive white Gaussian noise (AWGN) channel in order to demonstrate the improvement in performance achieved with the help of the proposed algorithm.
It is widely accepted that short cycles in Tanner graphs deteriorate the performance of message-passing algorithms. This discourages the use of these algorithms on Tanner graphs (TGs) of well-known algebraic codes such as Hamming codes, Bose-Chaudhuri-Hocquenghem codes, and Reed-Solomon codes. Yedidia et al. presented a method to generate code representations suitable for message-passing algorithms. This method does not guarantee a representation free of four-cycles. In this correspondence, we present an algorithm to convert an arbitrary linear block into a code with orthogonal parity-check equations. A combinatorial argument is used to prove that the algorithm guarantees a four-cycle free representation for any linear code. The effects of removing four-cycles on the performance of a belief propagation decoder for the binary erasure channel are studied in detail by analyzing the structures in different representations. Finally, we present bit-error rate (BER) and block-error rate (BLER) performance curves of linear block codes under belief propagation algorithms for the binary erasure channel and the additive white Gaussian noise (AWGN) channel in order to demonstrate the improvement in performance achieved with the help of the proposed algorithm.
In this paper, two iterative message passing algorithms based on belief propagation proposed for the decoding of any binary linear block code are compared. The innovation of these algorithms lies in the fact that the structure of the graph of the parity check matrix is adaptively modified to render it suitable to belief propagation decoding. The modification is done such that the least reliable bits received from the channel are leaves of the graph. These algorithms perform favorably when compared with existing hard and iterative soft decision decoding algorithms in terms of error rate while maintaining a complexity polynomial in block length. It was found that while these two algorithms are conceptually similar, their performance in terms of word error rate (as a function of signal-to-noise ratio), decoding time (average number of iterations) and the updated bit reliabilities are very different
The growing popularity of a class of linear block codes called the low-density parity-check (LDPC) codes can be attributed to the low complexity of the iterative decoders, and their potential to achieve performance very close to the Shannon capacity. This makes them an attractive candidate for ECC applications in communication systems. This report proposes methods to systematically construct regular and irregular LDPC codes.A class of regular LDPC codes are constructed from incidence structures in finite geometries like projective geometry and affine geometry. A class of irregular LDPC codes are constructed by systematically splitting blocks of balanced incomplete block designs to achieve desired weight distributions. These codes are decoded iteratively using message-passing algorithms, and the performance of these codes for various channels are presented in this report.The application of iterative decoders is generally limited to a class of codes whose graph representations are free of small cycles. Unfortunately, the large class of conventional algebraic codes, like RS codes, has several four cycles in their graph representations. This report proposes an algorithm that aims to alleviate this drawback by constructing an equivalent graph representation that is free of four cycles. It is theoretically shown that the four-cycle free representation is better suited to iterative erasure decoding than the conventional representation. Also, the new representation is exploited to realize, with limited success, iterative decoding of Reed-Solomon codes over the additive white Gaussian noise channel.Wiberg, Forney, Richardson, Koetter, and Vontobel have made significant contributions in developing theoretical frameworks that facilitate finite length analysis of codes. With an exception of Richardson's, most of the other frameworks are much suited for the analysis of short codes. In this report, we further the understanding of the failures in iterative decoders for the binary symmetric channel. The failures of the decoder are classified into two categories by defining trapping sets and propagating sets. Such a classification leads to a successful estimation of the performance of codes under the Gallager B decoder. Especially, the estimation techniques show great promise in the high signal-to-noise ratio regime where the simulation techniques are less feasible.
One of the most efficient approaches to improving the frame error rate (FER) performance of low-density parity-check (LDPC) codes is based on adding both auxiliary variable nodes (AVN) and the corresponding redundant parity checks (RPC) to the binary parity-check matrices of the code. It is known that for the LDPC codes, whose Tanner graphs contain length four cycles, this technique allows for substantial improvement in the FER performance of the belief-propagation (BP) decoding on the binary erasure channel (BEC) channel. The AVN-based technique followed by adding orthogonal redundant parity-checks is known to be efficient for both the BEC and additive white Gaussian noise (AWGN) channels. In this paper, firstly, the AVN-based approach is generalized to efficiently removing cycles of length larger than four. Secondly, the AVN-based technique is reformulated in terms of labeled base matrices of quasi-cyclic (QC) LDPC codes. An improved iterative decoding of QC LDPC codes is proposed, which is applied to the labeled base matrices of QC LDPC codes extended by the AVN-RPC technique.
Error control coding is an essential part of modern communications systems. LDPC codes have been demonstrated to offer performance near the fundamental limits of channels corrupted by random noise. Optimal maximum likelihood decoding of LDPC codes is too complex to be practically useful even at short block lengths and so a graph-based message passing decoder known as the belief propagation algorithm is used instead. In fact, on graphs without closed paths known as cycles the iterative message passing decoding is known to be optimal and may converge in a single iteration, although identifying the message update schedule which allows single-iteration convergence is not trivial. At finite block lengths graphs without cycles have poor minimum distance properties and perform poorly even under optimal decoding. LDPC codes with large block length have been demonstrated to offer performance close to that predicted for codes of infinite length, as the cycles present in the graph are quite long. In this thesis, LDPC codes of shorter length are considered as they offer advantages in terms of latency and complexity, at the cost of performance degradation from the increased number of short cycles in the graph. For these shorter LDPC codes, the problems considered are: \n \nFirst, improved construction of structured and unstructured LDPC code graphs of short length with a view to reducing the harmful effects of the cycles on error rate performance, based on knowledge of the decoding process. Structured code graphs are particularly interesting as they allow benefits in encoding and decoding complexity and speed. Secondly, the design and construction of LDPC codes for the block fading channel, a particularly challenging scenario from the point of view of error control code design. Both established and novel classes of codes for the channel are considered. Finally the decoding of LDPC codes by the belief propagation algorithm is considered, in particular the scheduling of messages passed in the iterative decoder. A knowledge-aided approach is developed based on message reliabilities and residuals to allow fast convergence and significant improvements in error rate performance.
As successfully adopted in standard long code scenarios, belief propagation (BP) decoding has been considered a promising universal decoding candidate for next-generation wireless communications. However, when applied to short codes, BP decoding suffers from poor error correction performance due to harmful cycle structures in the Tanner graph. In this paper, we address this issue by designing a structured, sparse parity-check matrix (ssPCM) framework, composed of multiple cycle-free parity-check row blocks (PCRBs). The resulting ssPCMs feature regular row weights and perform better than the state-of-theart 4 -cycle-free row redundant PCMs across Bose-Chaudhuri-Hocquenghem (BCH) codes of length 63.
In the field of Error Correcting Coding (ECC), the concept of ''stopping set'' recently became a hot topic. A stopping set for a parity-check matrix is the set of bits which would cause the iterative decoding progress in Binary Erasure Channel (BEC) to ''stop'' when it is erased. It has already been proved in several papers that the decoding performance under iterative decoding algorithm of any linear block code in BEC is determined by their stopping set performance, in particular, their size and number. And it is commonly assumed by the coding researchers that those codes which have better stopping set performance would also perform better in other channels. In this thesis, the decoding performance of linear block codes, in particular, Hamming Codes, the original LDPC code proposed by Gallager and FG-LDPC codes is studied in Binary Symmetric Channel (BSC) and Additive White Gaussian Noise (AWGN) channel. Three iterative decoding algorithms, respectively Bit-Flipping algorithm, Weighted Bit-Flipping algorithm and Sum Product Algorithm are used. To emphasize the influence of stopping set property, the parity-check matrix for each code is modified by adding linearly dependent rows to the original parity-check matrix, or removing rows from it. These modifications are made in order to change the stopping set property of the codes. The performance of the modified parity-check matrices is thus analyzed, by comparing them to the original ones, using both theoretical analysis and simulation. The results show that the stopping set performance of linear block codes is not a crucial factor to their performances on other channels as it is in BEC. Sometimes the linear block codes of a better stopping set property do perform better in other channels, but sometimes the performance barely changes or even degrades. More precisely, the decoding performance of a certain parity-check matrix is determined by the type of codes, the type of channel and the decoding algorithm, rather than its stopping set property.