On the Viterbi decoding algorithm

Omura, J. K.

doi:10.1109/tit.1969.1054239

Cited by 191 publications

(54 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Viterbi introduced the Viterbi algorithm [15]. It was shown to be optimal by Forney [16] and Omura [17] and to be eminently practical by Heller [18], [19].…”

Section: A Historical Perspectivementioning

confidence: 99%

Spatially Coupled Ensembles Universally Achieve Capacity Under Belief Propagation

Kudekar

Richardson

Urbanke

2013

IEEE Trans. Inform. Theory

358

318

View full text Add to dashboard Cite

Abstract-We investigate spatially coupled code ensembles. For transmission over the binary erasure channel, it was recently shown that spatial coupling increases the belief propagation threshold of the ensemble to essentially the maximum a-priori threshold of the underlying component ensemble. This explains why convolutional LDPC ensembles, originally introduced by Felström and Zigangirov, perform so well over this channel.We show that the equivalent result holds true for transmission over general binary-input memoryless output-symmetric channels. More precisely, given a desired error probability and a gap to capacity, we can construct a spatially coupled ensemble which fulfills these constraints universally on this class of channels under belief propagation decoding. In fact, most codes in that ensemble have that property. The quantifier universal refers to the single ensemble/code which is good for all channels but we assume that the channel is known at the receiver.The key technical result is a proof that under belief propagation decoding spatially coupled ensembles achieve essentially the area threshold of the underlying uncoupled ensemble.We conclude by discussing some interesting open problems.

show abstract

“…Viterbi introduced the Viterbi algorithm [15]. It was shown to be optimal by Forney [16] and Omura [17] and to be eminently practical by Heller [18], [19].…”

Section: A Historical Perspectivementioning

confidence: 99%

Spatially Coupled Ensembles Universally Achieve Capacity Under Belief Propagation

Kudekar

Richardson

Urbanke

2013

IEEE Trans. Inform. Theory

358

318

View full text Add to dashboard Cite

show abstract

“…It was quickly recognized [56], [57] that the VA was actually an optimum decoding algorithm. More importantly, Jerry Heller at the Jet Propulsion Laboratory (JPL) [58], [59] realized that relatively short convolutional codes decoded by the VA were potentially quite practical-e.g., a 64-state code could obtain a sizable real coding gain, on the order of 6 dB.…”

Section: B Convolutional Codes In the 1960s And 1970smentioning

confidence: 99%

Channel coding: The road to channel capacity

2007

View full text Add to dashboard Cite

Starting from Shannon's celebrated 1948 channel coding theorem, we trace the evolution of channel coding from Hamming codes to capacity-approaching codes. We focus on the contributions that have led to the most significant improvements in performance vs. complexity for practical applications, particularly on the additive white Gaussian noise (AWGN) channel. We discuss algebraic block codes, and why they did not prove to be the way to get to the Shannon limit. We trace the antecedents of today's capacity-approaching codes: convolutional codes, concatenated codes, and other probabilistic coding schemes. Finally, we sketch some of the practical applications of these codes.

show abstract

“…As convolutional codes become more powerful, Power Consumption Ratio Figure 15 Power consumption ratio of phong shading and gouraud shading: one triangle shading the complexity of the corresponding decoders generally increases. The Viterbi algorithm (VA) [16,17], which is the most extensively employed decoding algorithm for convolutional codes, works well for codes with short constraint length K. For more powerful codes with large constraint lengths the Adaptive Viterbi algorithm (AVA) [18,19] is used. It reduces the average number of computations per decoded information bit.…”

Section: Adaptive Viterbi Decodingmentioning

confidence: 99%

Dynamically Parameterized Algorithms and Architectures to Exploit Signal Variations

Jain

Laffely

Burleson

et al. 2004

The Journal of VLSI Signal Processing-Systems for Signal, Image

View full text Add to dashboard Cite

Signal processing algorithms and architectures can use dynamic reconfiguration to exploit variations in signal statistics with the objectives of improved performance and reduced power. Parameters provide a simple and formal way to characterize incremental changes to a computation and its computing mechanism. This paper develops a framework for dynamic parameterization and applies it to MPEG-4 motion estimation.A novel motion estimation architecture facilitates the dynamic variation of parameters to achieve power-compression tradeoffs. Our work shows that parameter variation in motion estimation helps achieve power reduction by an order of magnitude, trading off higher ii

show abstract

On the Viterbi decoding algorithm

Cited by 191 publications

References 1 publication

Spatially Coupled Ensembles Universally Achieve Capacity Under Belief Propagation

Spatially Coupled Ensembles Universally Achieve Capacity Under Belief Propagation

Channel coding: The road to channel capacity

Dynamically Parameterized Algorithms and Architectures to Exploit Signal Variations

Contact Info

Product

Resources

About