The Experts below are selected from a list of 23271 Experts worldwide ranked by ideXlab platform
Baodian Wei - One of the best experts on this subject based on the ideXlab platform.
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statistical learning aided decoding of bmst tail biting Convolutional Code
International Symposium on Information Theory, 2019Co-Authors: Wenchao Lin, Suihua Cai, Baodian WeiAbstract:This paper is concerned with block Markov superposition transmission (BMST) of tail-biting Convolutional Code (TBCC). We propose a new decoding algorithm for BMST-TBCC, which integrates a serial list Viterbi algorithm (SLVA) with a soft check instead of conventional cyclic redundancy check (CRC). The basic idea is that, compared with an erroneous candidate Codeword, the correct candidate Codeword for the first sub-frame has less influence on the output of Viterbi algorithm for the second sub-frame. The threshold is then determined by statistical learning based on the introduced empirical divergence function. The numerical results illustrate that, under the constraint of equivalent decoding delay, the BMST-TBCC has comparable performance with the polar Codes. As a result, BMST-TBCCs may find applications in the scenarios of the streaming ultra-reliable and low latency communication (URLLC) data services.
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list decoding with statistical check for semi random block oriented Convolutional Code
Electronics Letters, 2019Co-Authors: Wenchao Lin, Baodian WeiAbstract:In this Letter, a list decoding algorithm is proposed for the semi-random block-oriented Convolutional Code (SRBO-CC). To deCode each sub-frame, a list of candidates are serially computed until finding a qualified one or achieving maximum list, where the correctness of the decoding candidate is checked by a statistical threshold. Simulation results show that SRBO-CC with list decoding is competitive with polar Codes and that the performance-complexity tradeoffs can be achieved by adjusting the statistical threshold.
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a low latency coding scheme semi random block oriented Convolutional Code
International Symposium on Turbo Codes and Iterative Information Processing, 2018Co-Authors: Wenchao Lin, Suihua Cai, Jiachen Sun, Baodian WeiAbstract:In this paper, we propose a low latency coding scheme called Semi-Random Block Oriented Convolutional Code (SRBO-CC) which is similar to the Block Markov Superposition Transmission (BMST) but has short layer length. The SRBO-CC has a block oriented encoding process, where the input sequence is firstly enCoded by a structured Code and then superimposed on the random transformation of the previous inputs. The SRBO-CC is typically non-decodable by the Viterbi algorithm. Therefore, we analyze the tree structure of the SRBO-CC and employ the sequential decoding algorithm to deCode the SRBO-CC. Simulation results show that the SRBO-CC performs well at low latency, suggesting that the SRBO-CC can be a promising solution for Ultra-Reliable and Low Latency Communication (URLLC).
Wenchao Lin - One of the best experts on this subject based on the ideXlab platform.
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statistical learning aided decoding of bmst tail biting Convolutional Code
International Symposium on Information Theory, 2019Co-Authors: Wenchao Lin, Suihua Cai, Baodian WeiAbstract:This paper is concerned with block Markov superposition transmission (BMST) of tail-biting Convolutional Code (TBCC). We propose a new decoding algorithm for BMST-TBCC, which integrates a serial list Viterbi algorithm (SLVA) with a soft check instead of conventional cyclic redundancy check (CRC). The basic idea is that, compared with an erroneous candidate Codeword, the correct candidate Codeword for the first sub-frame has less influence on the output of Viterbi algorithm for the second sub-frame. The threshold is then determined by statistical learning based on the introduced empirical divergence function. The numerical results illustrate that, under the constraint of equivalent decoding delay, the BMST-TBCC has comparable performance with the polar Codes. As a result, BMST-TBCCs may find applications in the scenarios of the streaming ultra-reliable and low latency communication (URLLC) data services.
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list decoding with statistical check for semi random block oriented Convolutional Code
Electronics Letters, 2019Co-Authors: Wenchao Lin, Baodian WeiAbstract:In this Letter, a list decoding algorithm is proposed for the semi-random block-oriented Convolutional Code (SRBO-CC). To deCode each sub-frame, a list of candidates are serially computed until finding a qualified one or achieving maximum list, where the correctness of the decoding candidate is checked by a statistical threshold. Simulation results show that SRBO-CC with list decoding is competitive with polar Codes and that the performance-complexity tradeoffs can be achieved by adjusting the statistical threshold.
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a low latency coding scheme semi random block oriented Convolutional Code
International Symposium on Turbo Codes and Iterative Information Processing, 2018Co-Authors: Wenchao Lin, Suihua Cai, Jiachen Sun, Baodian WeiAbstract:In this paper, we propose a low latency coding scheme called Semi-Random Block Oriented Convolutional Code (SRBO-CC) which is similar to the Block Markov Superposition Transmission (BMST) but has short layer length. The SRBO-CC has a block oriented encoding process, where the input sequence is firstly enCoded by a structured Code and then superimposed on the random transformation of the previous inputs. The SRBO-CC is typically non-decodable by the Viterbi algorithm. Therefore, we analyze the tree structure of the SRBO-CC and employ the sequential decoding algorithm to deCode the SRBO-CC. Simulation results show that the SRBO-CC performs well at low latency, suggesting that the SRBO-CC can be a promising solution for Ultra-Reliable and Low Latency Communication (URLLC).
Mao-chao Lin - One of the best experts on this subject based on the ideXlab platform.
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Minimal Trellis Modules and Equivalent
2006Co-Authors: Hung-hua Tang, Mao-chao Lin, Bartolomeu F. Uchôa FilhoAbstract:In this correspondence, it is shown that some Convolutional Codes with distinct memory sizes of minimal enCoders are equivalent in the sense that the minimal trellises of these Codes are the shifted versions of one another. For an binary Convolutional Code, the weight spectrum ob- tained from the minimal trellis may be slightly different from that obtained from the conventional Code trellis with -bit branches. Code search is con- ducted to find some good binary Convolutional Codes. Bounds on the trellis complexity, measured by the number of states and the number of branches in the minimal trellis module, of any Convolutional Code and its equivalent Codes are also derived. Index Terms—Error-correction coding, Convolutional Codes, minimal trellis, minimal trellis module, trellis complexity.
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Minimal Trellis Modules and Equivalent Convolutional Codes
IEEE Transactions on Information Theory, 2006Co-Authors: Hung-hua Tang, Mao-chao Lin, Bartolomeu F. Uchoa-filhoAbstract:In this correspondence, it is shown that some Convolutional Codes with distinct memory sizes of minimal enCoders are equivalent in the sense that the minimal trellises of these Codes are the shifted versions of one another. For an (n,k) binary Convolutional Code, the weight spectrum obtained from the minimal trellis may be slightly different from that obtained from the conventional Code trellis with n-bit branches. Code search is conducted to find some good (n,n-1) binary Convolutional Codes. Bounds on the trellis complexity, measured by the number of states and the number of branches in the minimal trellis module, of any Convolutional Code and its equivalent Codes are also derived.
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On (n, n-1) Convolutional Codes with low trellis complexity
IEEE Transactions on Communications, 2002Co-Authors: Hung-hua Tang, Mao-chao LinAbstract:We show that the state complexity profile of a Convolutional Code C is the same as that of the reciprocal of the dual Code of C in case that minimal enCoders for both Codes are used. Then, we propose an optimum permutation for any given (n, n-1) binary Convolutional Code that will yield an equivalent Code with the lowest state complexity. With this permutation, we are able to find many (n, n-1) binary Convolutional Codes which are better than punctured Convolutional Codes of the same Code rate and memory size by either lower decoding complexity or better weight spectra.
Hung-hua Tang - One of the best experts on this subject based on the ideXlab platform.
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Minimal Trellis Modules and Equivalent
2006Co-Authors: Hung-hua Tang, Mao-chao Lin, Bartolomeu F. Uchôa FilhoAbstract:In this correspondence, it is shown that some Convolutional Codes with distinct memory sizes of minimal enCoders are equivalent in the sense that the minimal trellises of these Codes are the shifted versions of one another. For an binary Convolutional Code, the weight spectrum ob- tained from the minimal trellis may be slightly different from that obtained from the conventional Code trellis with -bit branches. Code search is con- ducted to find some good binary Convolutional Codes. Bounds on the trellis complexity, measured by the number of states and the number of branches in the minimal trellis module, of any Convolutional Code and its equivalent Codes are also derived. Index Terms—Error-correction coding, Convolutional Codes, minimal trellis, minimal trellis module, trellis complexity.
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Minimal Trellis Modules and Equivalent Convolutional Codes
IEEE Transactions on Information Theory, 2006Co-Authors: Hung-hua Tang, Mao-chao Lin, Bartolomeu F. Uchoa-filhoAbstract:In this correspondence, it is shown that some Convolutional Codes with distinct memory sizes of minimal enCoders are equivalent in the sense that the minimal trellises of these Codes are the shifted versions of one another. For an (n,k) binary Convolutional Code, the weight spectrum obtained from the minimal trellis may be slightly different from that obtained from the conventional Code trellis with n-bit branches. Code search is conducted to find some good (n,n-1) binary Convolutional Codes. Bounds on the trellis complexity, measured by the number of states and the number of branches in the minimal trellis module, of any Convolutional Code and its equivalent Codes are also derived.
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On (n, n-1) Convolutional Codes with low trellis complexity
IEEE Transactions on Communications, 2002Co-Authors: Hung-hua Tang, Mao-chao LinAbstract:We show that the state complexity profile of a Convolutional Code C is the same as that of the reciprocal of the dual Code of C in case that minimal enCoders for both Codes are used. Then, we propose an optimum permutation for any given (n, n-1) binary Convolutional Code that will yield an equivalent Code with the lowest state complexity. With this permutation, we are able to find many (n, n-1) binary Convolutional Codes which are better than punctured Convolutional Codes of the same Code rate and memory size by either lower decoding complexity or better weight spectra.
H V Poor - One of the best experts on this subject based on the ideXlab platform.
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turbo iterative decoding of a unitary space time Code with a Convolutional Code
Vehicular Technology Conference, 2002Co-Authors: Sudharman K Jayaweera, H V PoorAbstract:A turbo decoding scheme for a noncoherent, unitary space-time modulated communication system employing an FEC Convolutional Code is proposed. The channel is assumed to be Rayleigh fading with neither the transmitter nor the receiver knowing the channel coefficients. The Convolutional enCoder is applied to the information symbols before performing unitary spacetime coding. Treating the combination of Convolutional Code and the unitary space-time Code as a serially concatenated turbo Code, it is shown that the performance can be improved by iterative decoding. Simulation results suggest that the iterative deCoder can offer significant performance gain over conventional, non-iterative methods and also can help close the performance gap between coherent (e.g. Alamouti type) and non-coherent techniques by a significant margin.