Sequential Decoding
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Sequential Decoding
Recognised by John Wozencraft, sequential decoding is a limited memory technique for decoding tree codes. Sequential decoding is mainly used as an approximate decoding algorithm for long constraint-length convolutional codes. This approach may not be as accurate as the Viterbi algorithm but can save a substantial amount of computer memory. It was used to decode a convolutional code in 1968 Pioneer 9 mission. Sequential decoding explores the tree code in such a way to try to minimise the computational cost and memory requirements to store the tree. There is a range of sequential decoding approaches based on the choice of metric and algorithm. Metrics include: *Fano metric *Zigangirov metric *Gallager metric Algorithms include: *Stack algorithm *Fano algorithm *Creeper algorithm Fano metric Given a partially explored tree (represented by a set of nodes which are limit of exploration), we would like to know the best node from which to explore further. The Fano metric (named aft ...
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Robert Fano
Roberto Mario "Robert" Fano (11 November 1917 – 13 July 2016) was an Italian-American computer scientist and professor of electrical engineering and computer science at the Massachusetts Institute of Technology. He became a student and working lab partner to Claude Shannon, whom he admired zealously and assisted in the early years of Information Theory. Early life and education Fano was born in Turin, Italy in 1917 to a Jewish family and grew up in Turin. Fano's father was the mathematician Gino Fano, his older brother was the physicist Ugo Fano, and Giulio Racah was a cousin. Fano studied engineering as an undergraduate at the School of Engineering of Torino (Politecnico di Torino) until 1939, when he emigrated to the United States as a result of anti-Jewish legislation passed under Benito Mussolini. He received his S.B. in electrical engineering from MIT in 1941, and upon graduation joined the staff of the MIT Radiation Laboratory. After World War II, Fano continued on to ...
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John Wozencraft
John McReynolds "Jack" Wozencraft (September 30, 1925 – August 31, 2009) was an electrical engineer and information theory, information theorist, professor emeritus at the Massachusetts Institute of Technology. One of the pioneers of coding theory, Wozencraft developed the sequential decoding techniques for convolutional codes that made error-free communication possible with relatively low computing power. Biography He attended the U.S. Military Academy at West Point, NY. Following graduation in 1946, he joined the United States Army Signal Corps Engineering Laboratory. He received his Doctor of Science, Sc.D. at Massachusetts Institute of Technology, MIT in 1957. From 1957 to 1976, when he retired, he served on the faculty of MIT's department of Electrical Engineering. While on a leave of absence from MIT (1972–1974), he served as Dean of Research at the Naval Postgraduate School in Monterey, California. Following his retirement from MIT in 1976, he returned to the Nav ...
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Tree Codes
In botany, a tree is a perennial plant with an elongated stem, or trunk, usually supporting branches and leaves. In some usages, the definition of a tree may be narrower, including only woody plants with secondary growth, plants that are usable as lumber or plants above a specified height. In wider definitions, the taller palms, tree ferns, bananas, and bamboos are also trees. Trees are not a taxonomic group but include a variety of plant species that have independently evolved a trunk and branches as a way to tower above other plants to compete for sunlight. The majority of tree species are angiosperms or hardwoods; of the rest, many are gymnosperms or softwoods. Trees tend to be long-lived, some reaching several thousand years old. Trees have been in existence for 370 million years. It is estimated that there are some three trillion mature trees in the world. A tree typically has many secondary branches supported clear of the ground by the trunk. This trunk typically con ...
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Convolutional Code
In telecommunication, a convolutional code is a type of error-correcting code that generates parity symbols via the sliding application of a boolean polynomial function to a data stream. The sliding application represents the 'convolution' of the encoder over the data, which gives rise to the term 'convolutional coding'. The sliding nature of the convolutional codes facilitates trellis decoding using a time-invariant trellis. Time invariant trellis decoding allows convolutional codes to be maximum-likelihood soft-decision decoded with reasonable complexity. The ability to perform economical maximum likelihood soft decision decoding is one of the major benefits of convolutional codes. This is in contrast to classic block codes, which are generally represented by a time-variant trellis and therefore are typically hard-decision decoded. Convolutional codes are often characterized by the base code rate and the depth (or memory) of the encoder ,k,K/math>. The base code rate is ty ...
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Viterbi Algorithm
The Viterbi algorithm is a dynamic programming algorithm for obtaining the maximum a posteriori probability estimate of the most likely sequence of hidden states—called the Viterbi path—that results in a sequence of observed events, especially in the context of Markov information sources and hidden Markov models (HMM). The algorithm has found universal application in decoding the convolutional codes used in both CDMA and GSM digital cellular, dial-up modems, satellite, deep-space communications, and 802.11 wireless LANs. It is now also commonly used in speech recognition, speech synthesis, diarization, keyword spotting, computational linguistics, and bioinformatics. For example, in speech-to-text (speech recognition), the acoustic signal is treated as the observed sequence of events, and a string of text is considered to be the "hidden cause" of the acoustic signal. The Viterbi algorithm finds the most likely string of text given the acoustic signal. History The Viterbi a ...
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Pioneer 9
Pioneer commonly refers to a settler who migrates to previously uninhabited or sparsely inhabited land. In the United States pioneer commonly refers to an American pioneer, a person in American history who migrated west to join in settling and developing new areas. Pioneer, The Pioneer, or pioneering may also refer to: Companies and organizations * Pioneer Aerospace Corporation *Pioneer Chicken, an American fast-food restaurant chain *Pioneer Club Las Vegas, a casino in Las Vegas, Nevada, U.S. *Pioneer Corporation, a Japanese electronics manufacturer *Pioneer Energy, a Canadian gas station chain *Pioneer Entertainment, a Japanese anime company *Pioneer Hi-Bred, a U.S.-based agriculture company *Pioneer Hotel & Gambling Hall, Laughlin, Nevada, U.S. *Pioneer Instrument Company, an American aeronautical instrument manufacturer *Pioneer movement, a communist youth organization *Pioneer Natural Resources, an energy company in Texas, U.S. *Pioneer Pictures, a former American film studi ...
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Binary Symmetric Channel
A binary symmetric channel (or BSCp) is a common communications channel model used in coding theory and information theory. In this model, a transmitter wishes to send a bit (a zero or a one), and the receiver will receive a bit. The bit will be "flipped" with a "crossover probability" of ''p'', and otherwise is received correctly. This model can be applied to varied communication channels such as telephone lines or disk drive storage. The noisy-channel coding theorem applies to BSCp, saying that information can be transmitted at any rate up to the channel capacity with arbitrarily low error. The channel capacity is 1 - \operatorname H_\text(p) bits, where \operatorname H_\text is the binary entropy function. Codes including Forney's code have been designed to transmit information efficiently across the channel. Definition A binary symmetric channel with crossover probability p, denoted by BSCp, is a channel with binary input and binary output and probability of error p. That i ...
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Bayes Theorem
In probability theory and statistics, Bayes' theorem (alternatively Bayes' law or Bayes' rule), named after Thomas Bayes, describes the probability of an event, based on prior knowledge of conditions that might be related to the event. For example, if the risk of developing health problems is known to increase with age, Bayes' theorem allows the risk to an individual of a known age to be assessed more accurately (by conditioning it on their age) than simply assuming that the individual is typical of the population as a whole. One of the many applications of Bayes' theorem is Bayesian inference, a particular approach to statistical inference. When applied, the probabilities involved in the theorem may have different probability interpretations. With Bayesian probability interpretation, the theorem expresses how a degree of belief, expressed as a probability, should rationally change to account for the availability of related evidence. Bayesian inference is fundamental to Bayesia ...
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Probability
Probability is the branch of mathematics concerning numerical descriptions of how likely an Event (probability theory), event is to occur, or how likely it is that a proposition is true. The probability of an event is a number between 0 and 1, where, roughly speaking, 0 indicates impossibility of the event and 1 indicates certainty."Kendall's Advanced Theory of Statistics, Volume 1: Distribution Theory", Alan Stuart and Keith Ord, 6th Ed, (2009), .William Feller, ''An Introduction to Probability Theory and Its Applications'', (Vol 1), 3rd Ed, (1968), Wiley, . The higher the probability of an event, the more likely it is that the event will occur. A simple example is the tossing of a fair (unbiased) coin. Since the coin is fair, the two outcomes ("heads" and "tails") are both equally probable; the probability of "heads" equals the probability of "tails"; and since no other outcomes are possible, the probability of either "heads" or "tails" is 1/2 (which could also be written ...
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Code Rate
In telecommunication and information theory, the code rate (or information rateHuffman, W. Cary, and Pless, Vera, ''Fundamentals of Error-Correcting Codes'', Cambridge, 2003.) of a forward error correction code is the proportion of the data-stream that is useful (non- redundant). That is, if the code rate is k/n for every bits of useful information, the coder generates a total of bits of data, of which n-k are redundant. If is the gross bit rate or data signalling rate (inclusive of redundant error coding), the net bit rate (the useful bit rate exclusive of error correction codes) is \leq R \cdot k/n. For example: The code rate of a convolutional code will typically be , , , , , etc., corresponding to one redundant bit inserted after every single, second, third, etc., bit. The code rate of the octet oriented Reed Solomon block code denoted RS(204,188) is 188/204, meaning that redundant octets (or bytes) are added to each block of 188 octets of useful information. A few er ...
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Hamming Distance
In information theory, the Hamming distance between two strings of equal length is the number of positions at which the corresponding symbols are different. In other words, it measures the minimum number of ''substitutions'' required to change one string into the other, or the minimum number of ''errors'' that could have transformed one string into the other. In a more general context, the Hamming distance is one of several string metrics for measuring the edit distance between two sequences. It is named after the American mathematician Richard Hamming. A major application is in coding theory, more specifically to block codes, in which the equal-length strings are vectors over a finite field. Definition The Hamming distance between two equal-length strings of symbols is the number of positions at which the corresponding symbols are different. Examples The symbols may be letters, bits, or decimal digits, among other possibilities. For example, the Hamming distance between: ...
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Likelihood
The likelihood function (often simply called the likelihood) represents the probability of random variable realizations conditional on particular values of the statistical parameters. Thus, when evaluated on a given sample, the likelihood function indicates which parameter values are more ''likely'' than others, in the sense that they would have made the observed data more probable. Consequently, the likelihood is often written as \mathcal(\theta\mid X) instead of P(X \mid \theta), to emphasize that it is to be understood as a function of the parameters \theta instead of the random variable X. In maximum likelihood estimation, the arg max of the likelihood function serves as a point estimate for \theta, while local curvature (approximated by the likelihood's Hessian matrix) indicates the estimate's precision. Meanwhile in Bayesian statistics, parameter estimates are derived from the converse of the likelihood, the so-called posterior probability, which is calculated via Bayes' r ...
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