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80
LowDensity ParityCheck Codes
, 1963
"... Preface The Noisy Channel Coding Theorem discovered by C. E. Shannon in 1948 offered communication engineers the possibility of reducing error rates on noisy channels to negligible levels without sacrificing data rates. The primary obstacle to the practical use of this theorem has been the equipment ..."
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Cited by 992 (1 self)
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Preface The Noisy Channel Coding Theorem discovered by C. E. Shannon in 1948 offered communication engineers the possibility of reducing error rates on noisy channels to negligible levels without sacrificing data rates. The primary obstacle to the practical use of this theorem has been the equipment complexity and the computation time required to decode the noisy received data.
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 188 (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.
A unified framework for tree search decoding: rediscovering the sequential decoder,” Information Theory
 IEEE Transactions on
, 2006
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Coding for Interactive Communication
 IN PROCEEDINGS OF THE 25TH ANNUAL SYMPOSIUM ON THEORY OF COMPUTING
, 1996
"... Let the input to a computation problem be split between two processors connected by a communication link; and let an interactive protocol ß be known by which, on any input, the processors can solve the problem using no more than T transmissions of bits between them, provided the channel is noiseless ..."
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Cited by 42 (4 self)
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Let the input to a computation problem be split between two processors connected by a communication link; and let an interactive protocol ß be known by which, on any input, the processors can solve the problem using no more than T transmissions of bits between them, provided the channel is noiseless in each direction. We study the following question: if in fact the channel is noisy, what is the effect upon the number of transmissions needed in order to solve the computation problem reliably? Technologically this concern is motivated by the increasing importance of communication as a resource in computing, and by the tradeoff in communications equipment between bandwidth, reliability and expense. We treat a model with random channel noise. We describe a deterministic method for simulating noiselesschannel protocols on noisy channels, with only a constant slowdown. This is an analog for general interactive protocols of Shannon's coding theorem, which deals only with data transmission, ...
Algorithmic Complexity in Coding Theory and the Minimum Distance Problem
, 1997
"... We start with an overview of algorithmiccomplexity problems in coding theory We then show that the problem of computing the minimum distance of a binary linear code is NPhard, and the corresponding decision problem is NPcomplete. This constitutes a proof of the conjecture Bedekamp, McEliece, van T ..."
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Cited by 36 (2 self)
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We start with an overview of algorithmiccomplexity problems in coding theory We then show that the problem of computing the minimum distance of a binary linear code is NPhard, and the corresponding decision problem is NPcomplete. This constitutes a proof of the conjecture Bedekamp, McEliece, van Tilborg, dating back to 1978. Extensions and applications of this result to other problems in coding theory are discussed.
Feedback and error protection strategies for wireless progressive video transmission
 IEEE Transactions on Circuits and Systems for Video Technology
, 2002
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Huffman Tree Based Metric Derivation for a Lowcomplexity Sequential Soft VLC Decoding
 in Proceedings of ICC'02
, 2002
"... This paper considers VLC decoding algorithms based on MAP sequence estimation techniques, using residual source redundancy to provide channel error correction. These algorithms rely on soft values available at the entrance of the VLC decoder. We present here a new soft VLC decoding algorithm based o ..."
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Cited by 14 (4 self)
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This paper considers VLC decoding algorithms based on MAP sequence estimation techniques, using residual source redundancy to provide channel error correction. These algorithms rely on soft values available at the entrance of the VLC decoder. We present here a new soft VLC decoding algorithm based on a sequential decoding technique that is very efficient in terms of decoding complexity.
Progressive Source Coding Combined with Regressive Channel Coding on Varying Channels
 in Proc. 3rd ITG Conf. Source and Channel Coding
, 2000
"...  A channel coding system for strongly varying channels (mobile radio, Internet) unknown to the encoder is presented. It matches progressively coded sources (e.g., video, images, audio, speech) with convolutional codes of very high memory applying regressive redundancy over the data frame of the sou ..."
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Cited by 13 (6 self)
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 A channel coding system for strongly varying channels (mobile radio, Internet) unknown to the encoder is presented. It matches progressively coded sources (e.g., video, images, audio, speech) with convolutional codes of very high memory applying regressive redundancy over the data frame of the source. The channel decoder with scalable complexity and delay employs modified sequential decoding. The decoder uses a new algorithm, the 'far end error decoder (FEED)' which in a change of paradigm does not aim at low error rate, but rather makes the first error as far out as possible under the actual channel conditions. We determine the errorfree region of the frame which means that for progressively coded sources we achieve the best reconstruction quality possible. The decoding method is selfadaptive to varying and unknown channel conditions (interference, fading, packet loss) and provides graceful degradation. Results with SPIHT coded images show performances better than previous known FEC schemes. Potentials for further improvements of this scheme are discussed.
DeepSpace Communications and Coding: A Marriage Made in Heaven
 in Proceedings of the 1992 DLR Seminar &quot;Advanced Methods for Satellite and Deep Space Comm
, 1992
"... this paper we have followed the usual communications practice of not distinguishing between a "code" (i. e., the set of all codewords) and an "encoder," but this difference can be important in coding theory.] The LinLyne code had a memory of M = 20, i. e., the encoder remembered ..."
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Cited by 9 (3 self)
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this paper we have followed the usual communications practice of not distinguishing between a "code" (i. e., the set of all codewords) and an "encoder," but this difference can be important in coding theory.] The LinLyne code had a memory of M = 20, i. e., the encoder remembered twenty past information bits as well as the current information bit when forming the two encoded binary digits that are emitted for each input information bit. It was known that the minimum Hamming distance dmin of this code was 11. This is the minimum distance between two encoded sequences of length 2 (M+1) = 22 digits (one "constraint length") that correspond to different values of the initial information bit. If one uses these code parameters mindlessly in (17), one computes an estimated coding gain of G c 5 (7.0 dB), but this estimate is so optimistic that it must be taken with a grain of salt! [In fact, one should really use in (17) the free distance, d free , of the convolutional code, which is the minimum Hamming distance between two encoded sequences of infinite length that correspond to different values of the initial information bit. This would, of course, give an even more optimistic estimate of G c. !] The problem is not one of great multiplicity of near neighbors as it was for the ReedMuller code. Rather, the problem is that one must take into account the fact that although Fanoalgorithm sequential decoding is virtually maximumlikelihood decoding if the decoder is allowed to compute until it makes a decoding decision, in practice one always aborts the decoding after some predetermined amount of computation on a frame and announces erasure of the corresponding frame of data. The actual error probability in the nonerased frames is virtually zero since the frames that would have ...