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Factor Graphs and the SumProduct Algorithm
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 1998
"... A factor graph is a bipartite graph that expresses how a "global" function of many variables factors into a product of "local" functions. Factor graphs subsume many other graphical models including Bayesian networks, Markov random fields, and Tanner graphs. Following one simple c ..."
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Cited by 1791 (69 self)
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A factor graph is a bipartite graph that expresses how a "global" function of many variables factors into a product of "local" functions. Factor graphs subsume many other graphical models including Bayesian networks, Markov random fields, and Tanner graphs. Following one simple computational rule, the sumproduct algorithm operates in factor graphs to computeeither exactly or approximatelyvarious marginal functions by distributed messagepassing in the graph. A wide variety of algorithms developed in artificial intelligence, signal processing, and digital communications can be derived as specific instances of the sumproduct algorithm, including the forward/backward algorithm, the Viterbi algorithm, the iterative "turbo" decoding algorithm, Pearl's belief propagation algorithm for Bayesian networks, the Kalman filter, and certain fast Fourier transform algorithms.
Near Shannon limit errorcorrecting coding and decoding
, 1993
"... Abstract This paper deals with a new class of convolutional codes called Turbocodes, whose performances in terms of Bit Error Rate (BER) are close to the SHANNON limit. The TurboCode encoder is built using a parallel concatenation of two Recursive Systematic Convolutional codes and the associated ..."
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Cited by 1776 (6 self)
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Abstract This paper deals with a new class of convolutional codes called Turbocodes, whose performances in terms of Bit Error Rate (BER) are close to the SHANNON limit. The TurboCode encoder is built using a parallel concatenation of two Recursive Systematic Convolutional codes and the associated decoder, using a feedback decoding rule, is implemented as P pipelined identical elementary decoders. Consider a binary rate R=1/2 convolutional encoder with constraint length K and memory M=K1. The input to the encoder at time k is a bit dk and the corresponding codeword
Good ErrorCorrecting Codes based on Very Sparse Matrices
, 1999
"... We study two families of errorcorrecting codes defined in terms of very sparse matrices. "MN" (MacKayNeal) codes are recently invented, and "Gallager codes" were first investigated in 1962, but appear to have been largely forgotten, in spite of their excellent properties. The ..."
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Cited by 750 (23 self)
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We study two families of errorcorrecting codes defined in terms of very sparse matrices. "MN" (MacKayNeal) codes are recently invented, and "Gallager codes" were first investigated in 1962, but appear to have been largely forgotten, in spite of their excellent properties. The decoding of both codes can be tackled with a practical sumproduct algorithm. We prove that these codes are "very good," in that sequences of codes exist which, when optimally decoded, achieve information rates up to the Shannon limit. This result holds not only for the binarysymmetric channel but also for any channel with symmetric stationary ergodic noise. We give experimental results for binarysymmetric channels and Gaussian channels demonstrating that practical performance substantially better than that of standard convolutional and concatenated codes can be achieved; indeed, the performance of Gallager codes is almost as close to the Shannon limit as that of turbo codes.
Iterative (turbo) soft interference cancellation and decoding for coded CDMA
 IEEE Trans. Commun
, 1999
"... Abstract — The presence of both multipleaccess interference (MAI) and intersymbol interference (ISI) constitutes a major impediment to reliable communications in multipath codedivision multipleaccess (CDMA) channels. In this paper, an iterative receiver structure is proposed for decoding multiuse ..."
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Cited by 456 (18 self)
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Abstract — The presence of both multipleaccess interference (MAI) and intersymbol interference (ISI) constitutes a major impediment to reliable communications in multipath codedivision multipleaccess (CDMA) channels. In this paper, an iterative receiver structure is proposed for decoding multiuser information data in a convolutionally coded asynchronous multipath DSCDMA system. The receiver performs two successive softoutput decisions, achieved by a softinput softoutput (SISO) multiuser detector and a bank of singleuser SISO channel decoders, through an iterative process. At each iteration, extrinsic information is extracted from detection and decoding stages and is then used as a priori information in the next iteration, just as in Turbo decoding. Given the multipath CDMA channel model, a direct implementation of a slidingwindow SISO multiuser detector has a prohibitive computational complexity. A lowcomplexity SISO multiuser detector is developed based on a novel nonlinear interference suppression technique, which makes use of both soft interference cancellation and instantaneous linear minimum meansquare error filtering. The properties of such a nonlinear interference suppressor are examined, and an efficient recursive implementation is derived. Simulation results demonstrate that the proposed lowcomplexity iterative receiver structure for interference suppression and decoding offers significant performance gain over the traditional noniterative receiver structure. Moreover, at high signaltonoise ratio, the detrimental effects of MAI and ISI in the channel can almost be completely overcome by iterative processing, and singleuser performance can be approached. Index Terms — Coded CDMA, instantaneous MMSE filtering, multiuser detection, soft interference cancellation, Turbo processing.
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 404 (16 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
Achieving nearcapacity on a multipleantenna channel
 IEEE Trans. Commun
, 2003
"... Recent advancements in iterative processing of channel codes and the development of turbo codes have allowed the communications industry to achieve nearcapacity on a singleantenna Gaussian or fading channel with low complexity. We show how these iterative techniques can also be used to achieve nea ..."
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Cited by 402 (2 self)
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Recent advancements in iterative processing of channel codes and the development of turbo codes have allowed the communications industry to achieve nearcapacity on a singleantenna Gaussian or fading channel with low complexity. We show how these iterative techniques can also be used to achieve nearcapacity on a multipleantenna system where the receiver knows the channel. Combining iterative processing with multipleantenna channels is particularly challenging because the channel capacities can be a factor of ten or more higher than their singleantenna counterparts. Using a “list ” version of the sphere decoder, we provide a simple method to iteratively detect and decode any linear spacetime mapping combined with any channel code that can be decoded using socalled “soft ” inputs and outputs. We exemplify our technique by directly transmitting symbols that are coded with a channel code; we show that iterative processing with even this simple scheme can achieve nearcapacity. We consider both simple convolutional and powerful turbo channel codes and show that excellent performance at very high data rates can be attained with either. We compare our simulation results with Shannon capacity limits for ergodic multipleantenna channel. Index Terms—Wireless communications, BLAST, turbo codes, transmit diversity, receive diversity, fading channels, sphere decoding, softin/softout, concatenated codes 1
Serial Concatenation of Interleaved Codes: Performance Analysis, Design, and Iterative Decoding
 IEEE Trans. Inform. Theory
, 1996
"... A serially concatenated code with an interleaver consists of the cascade of an outer code... ..."
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Cited by 372 (32 self)
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A serially concatenated code with an interleaver consists of the cascade of an outer code...
The generalized distributive law
 Information Theory, IEEE Transactions on
"... Abstract—In this semitutorial paper we discuss a general message passing algorithm, which we call the generalized distributive law (GDL). The GDL is a synthesis of the work of many authors in the information theory, digital communications, signal processing, statistics, and artificial intelligence ..."
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Cited by 359 (2 self)
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Abstract—In this semitutorial paper we discuss a general message passing algorithm, which we call the generalized distributive law (GDL). The GDL is a synthesis of the work of many authors in the information theory, digital communications, signal processing, statistics, and artificial intelligence communities. It includes as special cases the Baum–Welch algorithm, the fast Fourier transform (FFT) on any finite Abelian group, the Gallager–Tanner–Wiberg decoding algorithm, Viterbi’s algorithm, the BCJR algorithm, Pearl’s “belief propagation ” algorithm, the Shafer–Shenoy probability propagation algorithm, and the turbo decoding algorithm. Although this algorithm is guaranteed to give exact answers only in certain cases (the “junction tree ” condition), unfortunately not including the cases of GTW with cycles or turbo decoding, there is much experimental evidence, and a few theorems, suggesting that it often works approximately even when it is not supposed to. Index Terms—Belief propagation, distributive law, graphical models, junction trees, turbo codes. I.
Unveiling Turbo Codes: Some Results on Parallel Concatenated Coding Schemes
, 1995
"... A parallel concatenated coding scheme consists of two simple constituent systematic encoders linked by an interleaver. The input bits to the first encoder are scrambled by the interleaver before entering the second encoder. The codeword of the parallel concatenated code consists of the input bits to ..."
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Cited by 314 (6 self)
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A parallel concatenated coding scheme consists of two simple constituent systematic encoders linked by an interleaver. The input bits to the first encoder are scrambled by the interleaver before entering the second encoder. The codeword of the parallel concatenated code consists of the input bits to the first encoder followed by the parity check bits of both encoders. This construction can be generalized to any number of constituent codes. Parallel concatenated schemes employing two convolutional codes as constituent codes, in connection with an iterative decoding algorithm of complexity comparable to that of the constituent codes, have been recently shown to yield remarkable coding gains close to theoretical limits. They have been named, and are known as, "turbo codes". We propose a method to evaluate an upper bound to the bit error probability of a parallel concatenated coding scheme averaged over all interleavers of a given length. The analytical bounding technique is then used to s...
Applications of ErrorControl Coding
, 1998
"... An overview of the many practical applications of channel coding theory in the past 50 years is presented. The following application areas are included: deep space communication, satellite communication, data transmission, data storage, mobile communication, file transfer, and digital audio/video t ..."
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Cited by 276 (0 self)
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An overview of the many practical applications of channel coding theory in the past 50 years is presented. The following application areas are included: deep space communication, satellite communication, data transmission, data storage, mobile communication, file transfer, and digital audio/video transmission. Examples, both historical and current, are given that typify the different approaches used in each application area. Although no attempt is made to be comprehensive in our coverage, the examples chosen clearly illustrate the richness, variety, and importance of errorcontrol coding methods in modern digital applications.