The Experts below are selected from a list of 14832 Experts worldwide ranked by ideXlab platform
Antony Joseph - One of the best experts on this subject based on the ideXlab platform.
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least squares superposition codes of moderate dictionary size are reliable at rates up to capacity
IEEE Transactions on Information Theory, 2012Co-Authors: Antony Joseph, Andrew R. BarronAbstract:For the additive white Gaussian noise channel with average codeword power constraint, coding methods are analyzed in which the Codewords are sparse superpositions, that is, linear combinations of subsets of vectors from a given design, with the possible messages indexed by the choice of subset. Decoding is by least squares (maximum likelihood), tailored to the assumed form of Codewords being linear combinations of elements of the design. Communication is shown to be reliable with error probability exponentially small for all rates up to the Shannon capacity.
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toward fast reliable communication at rates near capacity with gaussian noise
arXiv: Information Theory, 2010Co-Authors: Andrew R. Barron, Antony JosephAbstract:For the additive Gaussian noise channel with average codeword power constraint, sparse superposition codes and adaptive successive decoding is developed. Codewords are linear combinations of subsets of vectors, with the message indexed by the choice of subset. A feasible decoding algorithm is presented. Communication is reliable with error probability exponentially small for all rates below the Shannon capacity.
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least squares superposition codes of moderate dictionary size reliable at rates up to capacity
arXiv: Information Theory, 2010Co-Authors: Andrew R. Barron, Antony JosephAbstract:For the additive white Gaussian noise channel with average codeword power constraint, new coding methods are devised in which the Codewords are sparse superpositions, that is, linear combinations of subsets of vectors from a given design, with the possible messages indexed by the choice of subset. Decoding is by least squares, tailored to the assumed form of linear combination. Communication is shown to be reliable with error probability exponentially small for all rates up to the Shannon capacity.
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least squares superposition codes of moderate dictionary size reliable at rates up to capacity
International Symposium on Information Theory, 2010Co-Authors: Andrew R. Barron, Antony JosephAbstract:Sparse superposition codes are developed for the additive white Gaussian noise channel with average codeword power constraint. Codewords are linear combinations of subsets of vectors, with the possible messages indexed by the choice of subset. Decoding is by least squares, tailored to the assumed form of linear combination. Communication is shown to be reliable with error probability exponentially small for all rates up to the Shannon capacity.
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sparse superposition codes fast and reliable at rates approaching capacity with gaussian noise
2010Co-Authors: Andrew R. Barron, Antony JosephAbstract:For the additive white Gaussian noise channel with average codeword power constraint, sparse superposition codes are developed. Both encoding and decoding are computationally feasible. The Codewords are linear combinations of subsets of vectors from a given dictionary, with the possible messages indexed by the choice of subset. An adaptive successive decoder is developed, with which communication is shown to be reliable with error probability exponentially small for all rates below the Shannon capacity.
Andrew R. Barron - One of the best experts on this subject based on the ideXlab platform.
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least squares superposition codes of moderate dictionary size are reliable at rates up to capacity
IEEE Transactions on Information Theory, 2012Co-Authors: Antony Joseph, Andrew R. BarronAbstract:For the additive white Gaussian noise channel with average codeword power constraint, coding methods are analyzed in which the Codewords are sparse superpositions, that is, linear combinations of subsets of vectors from a given design, with the possible messages indexed by the choice of subset. Decoding is by least squares (maximum likelihood), tailored to the assumed form of Codewords being linear combinations of elements of the design. Communication is shown to be reliable with error probability exponentially small for all rates up to the Shannon capacity.
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toward fast reliable communication at rates near capacity with gaussian noise
arXiv: Information Theory, 2010Co-Authors: Andrew R. Barron, Antony JosephAbstract:For the additive Gaussian noise channel with average codeword power constraint, sparse superposition codes and adaptive successive decoding is developed. Codewords are linear combinations of subsets of vectors, with the message indexed by the choice of subset. A feasible decoding algorithm is presented. Communication is reliable with error probability exponentially small for all rates below the Shannon capacity.
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least squares superposition codes of moderate dictionary size reliable at rates up to capacity
arXiv: Information Theory, 2010Co-Authors: Andrew R. Barron, Antony JosephAbstract:For the additive white Gaussian noise channel with average codeword power constraint, new coding methods are devised in which the Codewords are sparse superpositions, that is, linear combinations of subsets of vectors from a given design, with the possible messages indexed by the choice of subset. Decoding is by least squares, tailored to the assumed form of linear combination. Communication is shown to be reliable with error probability exponentially small for all rates up to the Shannon capacity.
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least squares superposition codes of moderate dictionary size reliable at rates up to capacity
International Symposium on Information Theory, 2010Co-Authors: Andrew R. Barron, Antony JosephAbstract:Sparse superposition codes are developed for the additive white Gaussian noise channel with average codeword power constraint. Codewords are linear combinations of subsets of vectors, with the possible messages indexed by the choice of subset. Decoding is by least squares, tailored to the assumed form of linear combination. Communication is shown to be reliable with error probability exponentially small for all rates up to the Shannon capacity.
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sparse superposition codes fast and reliable at rates approaching capacity with gaussian noise
2010Co-Authors: Andrew R. Barron, Antony JosephAbstract:For the additive white Gaussian noise channel with average codeword power constraint, sparse superposition codes are developed. Both encoding and decoding are computationally feasible. The Codewords are linear combinations of subsets of vectors from a given dictionary, with the possible messages indexed by the choice of subset. An adaptive successive decoder is developed, with which communication is shown to be reliable with error probability exponentially small for all rates below the Shannon capacity.
Juane Li - One of the best experts on this subject based on the ideXlab platform.
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a scheme for collective encoding and iterative soft decision decoding of cyclic codes of prime lengths applications to reed solomon bch and quadratic residue codes
IEEE Transactions on Information Theory, 2020Co-Authors: Khaled Abdelghaffar, Juane LiAbstract:A novel scheme is presented for encoding and iterative soft-decision decoding of cyclic codes of prime lengths. The encoding of a cyclic code of a prime length is performed on a collection of Codewords which are mapped through Galois Fourier transform into a codeword in a low-density parity-check code with a binary parity-check matrix for transmission. Using this matrix, binary iterative soft-decision decoding algorithm is applied to jointly decode a collection of Codewords from the cyclic code. The joint-decoding allows for information sharing among the received vectors corresponding to the Codewords in the collection during the iterative decoding process. For decoding Reed-Solomon and BCH codes of prime lengths, the proposed decoding scheme not only requires much lower decoding complexity than other soft-decision decoding algorithms for these codes, but also yields superior performance. The proposed decoding scheme can also achieve a joint-decoding gain over the maximum likelihood decoding of individual Codewords. The decoding scheme is also applied to quadratic residue codes.
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a novel coding scheme for encoding and iterative soft decision decoding of binary bch codes of prime lengths
Information Theory and Applications, 2018Co-Authors: Khaled Abdelghaffar, Juane LiAbstract:A novel coding scheme is presented for encoding and iterative soft-decision decoding of binary BCH codes of prime lengths. The encoding of such a BCH code is performed on a collection of Codewords which are mapped through Galois Fourier transform into a codeword of a nonbinary low-density parity-check (LDPC) code which has a binary parity-check matrix for transmission. Using this matrix, a binary iterative soft-decision decoding algorithm based on belief-propagation is applied to jointly decode a collection of Codewords of the BCH code. The joint-decoding allows information sharing among the received vectors corresponding to the Codewords in the collection during the iterative decoding process. For decoding a BCH code of prime length, the proposed decoding scheme not only requires low decoding complexity, but also yields superior performance. The proposed decoding scheme can achieve a joint-decoding gain over the maximum likelihood decoding of individual Codewords.
Dongliang Zheng - One of the best experts on this subject based on the ideXlab platform.
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Phase coding method for absolute phase retrieval with a large number of Codewords.
Optics express, 2012Co-Authors: Dongliang ZhengAbstract:A recently proposed phase coding method for absolute phase retrieval performs well because its codeword is embedded into phase domain rather than intensity. Then, the codeword can determine the fringe order for the phase unwrapping. However, for absolute phase retrieval with a large number of Codewords, the traditional phase coding method becomes not so reliable. In this paper, we present a novel phase coding method to tackle this problem. Six additional fringe images can generate more than 64(2(6)) unique Codewords for correct absolute phase retrieval. The novel phase coding method can be used for absolute phase retrieval with high frequency. Experiment results demonstrate the proposed method is effective.
Song Zhang - One of the best experts on this subject based on the ideXlab platform.
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novel phase coding method for absolute phase retrieval
Optics Letters, 2012Co-Authors: Yajun Wang, Song ZhangAbstract:This Letter presents a novel absolute phase recovery technique with phase coding. Unlike the conventional gray-coding method, the codeword is embedded into the phase and then used to determine the fringe order for absolute phase retrieval. This technique is robust because it uses phase instead of intensity to determine Codewords, and it could achieve a faster measurement speed, since three additional images can represent more than 8(23) unique Codewords for phase unwrapping. Experimental results will be presented to verify the performance of the proposed technique.
