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402
Design of capacityapproaching irregular lowdensity paritycheck codes
 IEEE TRANS. INFORM. THEORY
, 2001
"... We design lowdensity paritycheck (LDPC) codes that perform at rates extremely close to the Shannon capacity. The codes are built from highly irregular bipartite graphs with carefully chosen degree patterns on both sides. Our theoretical analysis of the codes is based on [1]. Assuming that the unde ..."
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Cited by 432 (7 self)
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We design lowdensity paritycheck (LDPC) codes that perform at rates extremely close to the Shannon capacity. The codes are built from highly irregular bipartite graphs with carefully chosen degree patterns on both sides. Our theoretical analysis of the codes is based on [1]. Assuming that the underlying communication channel is symmetric, we prove that the probability densities at the message nodes of the graph possess a certain symmetry. Using this symmetry property we then show that, under the assumption of no cycles, the message densities always converge as the number of iterations tends to infinity. Furthermore, we prove a stability condition which implies an upper bound on the fraction of errors that a beliefpropagation decoder can correct when applied to a code induced from a bipartite graph with a given degree distribution. Our codes are found by optimizing the degree structure of the underlying graphs. We develop several strategies to perform this optimization. We also present some simulation results for the codes found which show that the performance of the codes is very close to the asymptotic theoretical bounds.
The Capacity of LowDensity ParityCheck Codes Under MessagePassing Decoding
, 2001
"... In this paper, we present a general method for determining the capacity of lowdensity paritycheck (LDPC) codes under messagepassing decoding when used over any binaryinput memoryless channel with discrete or continuous output alphabets. Transmitting at rates below this capacity, a randomly chos ..."
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Cited by 362 (8 self)
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In this paper, we present a general method for determining the capacity of lowdensity paritycheck (LDPC) codes under messagepassing decoding when used over any binaryinput memoryless channel with discrete or continuous output alphabets. Transmitting at rates below this capacity, a randomly chosen element of the given ensemble will achieve an arbitrarily small target probability of error with a probability that approaches one exponentially fast in the length of the code. (By concatenating with an appropriate outer code one can achieve a probability of error that approaches zero exponentially fast in the length of the code with arbitrarily small loss in rate.) Conversely, transmitting at rates above this capacity the probability of error is bounded away from zero by a strictly positive constant which is independent of the length of the code and of the number of iterations performed. Our results are based on the observation that the concentration of the performance of the decoder around its average performance, as observed by Luby et al. [1] in the case of a binarysymmetric channel and a binary messagepassing algorithm, is a general phenomenon. For the particularly important case of beliefpropagation decoders, we provide an effective algorithm to determine the corresponding capacity to any desired degree of accuracy. The ideas presented in this paper are broadly applicable and extensions of the general method to lowdensity paritycheck codes over larger alphabets, turbo codes, and other concatenated coding schemes are outlined.
Turbo decoding as an instance of Pearl’s belief propagation algorithm
 IEEE Journal on Selected Areas in Communications
, 1998
"... Abstract—In this paper, we will describe the close connection between the now celebrated iterative turbo decoding algorithm of Berrou et al. and an algorithm that has been well known in the artificial intelligence community for a decade, but which is relatively unknown to information theorists: Pear ..."
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Cited by 307 (15 self)
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Abstract—In this paper, we will describe the close connection between the now celebrated iterative turbo decoding algorithm of Berrou et al. and an algorithm that has been well known in the artificial intelligence community for a decade, but which is relatively unknown to information theorists: Pearl’s belief propagation algorithm. We shall see that if Pearl’s algorithm is applied to the “belief network ” of a parallel concatenation of two or more codes, the turbo decoding algorithm immediately results. Unfortunately, however, this belief diagram has loops, and Pearl only proved that his algorithm works when there are no loops, so an explanation of the excellent experimental performance of turbo decoding is still lacking. However, we shall also show that Pearl’s algorithm can be used to routinely derive previously known iterative, but suboptimal, decoding algorithms for a number of other errorcontrol systems, including Gallager’s
Near Shannon Limit Performance of Low Density Parity Check Codes
 Electronics Letters
, 1996
"... We report the empirical performance of Gallager's low density parity check codes on Gaussian channels. We show that performance substantially better than that of standard convolutional and concatenated codes can be achieved; indeed the performance is almost as close to the Shannon limit as that o ..."
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Cited by 305 (22 self)
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We report the empirical performance of Gallager's low density parity check codes on Gaussian channels. We show that performance substantially better than that of standard convolutional and concatenated codes can be achieved; indeed the performance is almost as close to the Shannon limit as that of Turbo codes.
On the design of lowdensity paritycheck codes within 0.0045 dB of the shannon limit
 IEEE Communications Letters
, 2001
"... Abstract—We develop improved algorithms to construct good lowdensity paritycheck codes that approach the Shannon limit very closely. For rate 1/2, the best code found has a threshold within 0.0045 dB of the Shannon limit of the binaryinput additive white Gaussian noise channel. Simulation results ..."
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Cited by 208 (6 self)
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Abstract—We develop improved algorithms to construct good lowdensity paritycheck codes that approach the Shannon limit very closely. For rate 1/2, the best code found has a threshold within 0.0045 dB of the Shannon limit of the binaryinput additive white Gaussian noise channel. Simulation results with a somewhat simpler code show that we can achieve within 0.04 dB of the Shannon limit at a bit error rate of 10 T using a block length of 10 U. Index Terms—Density evolution, lowdensity paritycheck codes, Shannon limit, sumproduct algorithm. I.
On the Optimality of Solutions of the MaxProduct Belief Propagation Algorithm in Arbitrary Graphs
, 2001
"... Graphical models, suchasBayesian networks and Markov random fields, represent statistical dependencies of variables by a graph. The maxproduct "belief propagation" algorithm is a localmessage passing algorithm on this graph that is known to converge to a unique fixed point when the graph is a tr ..."
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Cited by 184 (15 self)
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Graphical models, suchasBayesian networks and Markov random fields, represent statistical dependencies of variables by a graph. The maxproduct "belief propagation" algorithm is a localmessage passing algorithm on this graph that is known to converge to a unique fixed point when the graph is a tree. Furthermore, when the graph is a tree, the assignment based on the fixedpoint yields the most probable a posteriori (MAP) values of the unobserved variables given the observed ones. Recently, good
Correctness of Local Probability Propagation in Graphical Models with Loops
, 2000
"... This article analyzes the behavior of local propagation rules in graphical models with a loop. ..."
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Cited by 175 (9 self)
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This article analyzes the behavior of local propagation rules in graphical models with a loop.
Improved lowdensity paritycheck codes using irregular graphs
 IEEE Trans. Inform. Theory
, 2001
"... Abstract—We construct new families of errorcorrecting codes based on Gallager’s lowdensity paritycheck codes. We improve on Gallager’s results by introducing irregular paritycheck matrices and a new rigorous analysis of harddecision decoding of these codes. We also provide efficient methods for ..."
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Cited by 169 (15 self)
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Abstract—We construct new families of errorcorrecting codes based on Gallager’s lowdensity paritycheck codes. We improve on Gallager’s results by introducing irregular paritycheck matrices and a new rigorous analysis of harddecision decoding of these codes. We also provide efficient methods for finding good irregular structures for such decoding algorithms. Our rigorous analysis based on martingales, our methodology for constructing good irregular codes, and the demonstration that irregular structure improves performance constitute key points of our contribution. We also consider irregular codes under belief propagation. We report the results of experiments testing the efficacy of irregular codes on both binarysymmetric and Gaussian channels. For example, using belief propagation, for rate I R codes on 16 000 bits over a binarysymmetric channel, previous lowdensity paritycheck codes can correct up to approximately 16 % errors, while our codes correct over 17%. In some cases our results come very close to reported results for turbo codes, suggesting that variations of irregular low density paritycheck codes may be able to match or beat turbo code performance. Index Terms—Belief propagation, concentration theorem, Gallager codes, irregular codes, lowdensity paritycheck codes.
The Capacity of LowDensity Parity Check Codes under MessagePassing Decoding
 IEEE Trans. Inform. Theory
, 1998
"... In this paper we present a general method for determining the capacity of messagepassing decoders applied to low density parity check codes used over any binaryinput memoryless channel with discrete or continuous output alphabets. We show that for almost all codes in a suitably defined ensemble, t ..."
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Cited by 168 (9 self)
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In this paper we present a general method for determining the capacity of messagepassing decoders applied to low density parity check codes used over any binaryinput memoryless channel with discrete or continuous output alphabets. We show that for almost all codes in a suitably defined ensemble, transmission at rates below this capacity results in error probabilities that approach zero exponentially fast in the length of the code, whereas for transmission at rates above the capacity the error probability stays bounded away from zero. Our results are based on the observation that the concentration of the performance of the decoder around its average performance, as observed by Luby et. al. [1] in the case of a binary symmetric channel and a binary message passing algorithm, is a general phenomenon. For the particularly important case of belief propagation decoders we provide an effective algorithm to determine the corresponding capacity to any desired degree of accuracy. The ideas pre...
Analysis of sumproduct decoding of lowdensity paritycheck codes using a Gaussian approximation
 IEEE TRANS. INFORM. THEORY
, 2001
"... Density evolution is an algorithm for computing the capacity of lowdensity paritycheck (LDPC) codes under messagepassing decoding. For memoryless binaryinput continuousoutput additive white Gaussian noise (AWGN) channels and sumproduct decoders, we use a Gaussian approximation for message densi ..."
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Cited by 159 (2 self)
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Density evolution is an algorithm for computing the capacity of lowdensity paritycheck (LDPC) codes under messagepassing decoding. For memoryless binaryinput continuousoutput additive white Gaussian noise (AWGN) channels and sumproduct decoders, we use a Gaussian approximation for message densities under density evolution to simplify the analysis of the decoding algorithm. We convert the infinitedimensional problem of iteratively calculating message densities, which is needed to find the exact threshold, to a onedimensional problem of updating means of Gaussian densities. This simplification not only allows us to calculate the threshold quickly and to understand the behavior of the decoder better, but also makes it easier to design good irregular LDPC codes for AWGN channels. For various regular LDPC codes we have examined, thresholds can be estimated within 0.1 dB of the exact value. For rates between 0.5 and 0.9, codes designed using the Gaussian approximation perform within 0.02 dB of the best performing codes found so far by using density evolution when the maximum variable degree is IH. We show that by using the Gaussian approximation, we can visualize the sumproduct decoding algorithm. We also show that the optimization of degree distributions can be understood and done graphically using the visualization.