Results 1  10
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19,446
Maximum likelihood from incomplete data via the EM algorithm
 JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B
, 1977
"... A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situat ..."
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Cited by 11972 (17 self)
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A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value
Maximumlikelihood decoding of reedsolomon codes is nphard
 IEEE Transactions on Information Theory
"... Maximumlikelihood decoding is one of the central algorithmic problems in coding theory. It has been known for over 25 years that maximumlikelihood decoding of general linear codes is NPhard. Nevertheless, it was so far unknown whether maximumlikelihood decoding remains hard for any specific fam ..."
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Cited by 41 (3 self)
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Maximumlikelihood decoding is one of the central algorithmic problems in coding theory. It has been known for over 25 years that maximumlikelihood decoding of general linear codes is NPhard. Nevertheless, it was so far unknown whether maximumlikelihood decoding remains hard for any specific
Fast MaximumLikelihood Decoding of the Golden Code
 IEEE Trans. on Wireless Comm., accepted
, 2009
"... Abstract—Because each golden code codeword conveys four information symbols from an Mary QAM alphabet, the complexity of an exhaustivesearch decoder is proportional to M 4. In this paper we prove that the golden code is fastdecodable, meaning that maximumlikelihood decoding is possible with a wo ..."
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Cited by 24 (1 self)
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Abstract—Because each golden code codeword conveys four information symbols from an Mary QAM alphabet, the complexity of an exhaustivesearch decoder is proportional to M 4. In this paper we prove that the golden code is fastdecodable, meaning that maximumlikelihood decoding is possible with a
A Fast MaximumLikelihood Decoder for Convolutional Codes
"... Abstract—The lazy Viterbi decoder is a maximumlikelihood decoder for block and stream convolutional codes. For many codes of practical interest, under reasonable noise conditions, the lazy decoder is much faster than the original Viterbi decoder. For a code of constraint length , the lazy algorith ..."
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Cited by 3 (1 self)
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Abstract—The lazy Viterbi decoder is a maximumlikelihood decoder for block and stream convolutional codes. For many codes of practical interest, under reasonable noise conditions, the lazy decoder is much faster than the original Viterbi decoder. For a code of constraint length , the lazy
SingleSymbol Maximum Likelihood Decodable Linear STBCs
, 2006
"... SpaceTime block codes (STBC) from Orthogonal Designs (OD) and Coordinate Interleaved Orthogonal Designs ..."
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Cited by 81 (27 self)
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SpaceTime block codes (STBC) from Orthogonal Designs (OD) and Coordinate Interleaved Orthogonal Designs
Statistical pruning for nearmaximum likelihood decoding
 IEEE Transactions on Signal Processing
, 2007
"... In many communications problems, maximumlikelihood (ML) decoding reduces to nding the closest (skewed) lattice point in Ndimensions to a given point x 2 CN. In its full generality, this problem is known to be NPcomplete and requires complexity exponential in N. Recently, the expected complexity o ..."
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Cited by 15 (1 self)
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In many communications problems, maximumlikelihood (ML) decoding reduces to nding the closest (skewed) lattice point in Ndimensions to a given point x 2 CN. In its full generality, this problem is known to be NPcomplete and requires complexity exponential in N. Recently, the expected complexity
Efficient statistical pruning for maximum likelihood decoding
 in Proc. IEEE Int. Conf. on Acoustics Speech and Signal Process. (ICASSP
, 2003
"... ABSTRACT In many communications problems, maximumlikelihood (ML) decoding reduces to finding the closest (skewed) lattice point in Ndimensions to a given point x E CN. In its full generality, this problem is known to he NPcomplete and requires exponential complexity in iV. Recently, the expected ..."
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Cited by 10 (1 self)
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ABSTRACT In many communications problems, maximumlikelihood (ML) decoding reduces to finding the closest (skewed) lattice point in Ndimensions to a given point x E CN. In its full generality, this problem is known to he NPcomplete and requires exponential complexity in iV. Recently
A randomization method for quasi maximum likelihood decoding
 IN PROCEEDINGS OF THE 9TH CANADIAN WORKSHOP ON INFORMATION THEORY
, 2005
"... ... systems, MaximumLikelihood (ML) decoding is equivalent to £nding the closest lattice point in an N dimensional complex space. In [1], we have proposed several quasimaximum likelihood relaxation models for decoding in MIMO systems based on semide£nite programming. In this paper, we propose ran ..."
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Cited by 3 (0 self)
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... systems, MaximumLikelihood (ML) decoding is equivalent to £nding the closest lattice point in an N dimensional complex space. In [1], we have proposed several quasimaximum likelihood relaxation models for decoding in MIMO systems based on semide£nite programming. In this paper, we propose
An Efficient QuasiMaximum Likelihood Decoding for Finite Constellations
 in Conference on Information Sciences and Systems (CISS) 2005
, 2005
"... In MultiInput MultiOutput (MIMO) systems, maximumlikelihood (ML) decoding is equivalent to finding the closest lattice point in an N dimensional complex space. In general, this algorithm is shown to be NP hard. In this paper, we propose a quasimaximum likelihood algorithm based on SemiDefinite ..."
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Cited by 2 (2 self)
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In MultiInput MultiOutput (MIMO) systems, maximumlikelihood (ML) decoding is equivalent to finding the closest lattice point in an N dimensional complex space. In general, this algorithm is shown to be NP hard. In this paper, we propose a quasimaximum likelihood algorithm based on Semi
Results 1  10
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19,446