Results 101 to 110 of about 251 (146)
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Concatenated convolutional codes with interleavers
IEEE Communications Magazine, 2003This article presents a tutorial overview of the class of concatenated convolutional codes with interleavers, also known as turbo-like codes. They are powerful codes, formed by a number of encoders connected through interleavers, endowed by a decoding algorithm that splits the decoding burden into separate decoding of each individual code.
S Benedetto, G Montorsi, D Divsalar
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An improved interleaver design technique for parallel concatenated convolutional codes [PDF]
IEEE International Conference on Communications, 2003. ICC '03., 2004 This paper is aimed at the problem of designing optimized interleavers for parallel concatenated convolutional codes (PCCC) that satisfy several requirements simultaneously: 1) designing interleavers tailored to the constituent codes of the PCCC; 2) improving the distance spectra of the resulting turbo codes which dominate their asymptotic performance;
Laddomada, Massimiliano, Daneshgaran, F.
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Modified Convolutional Interleavers for Parallel Concatenated Block Codes
IEEE Communications Letters, 2022Sina Vafi
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A cost-function based technique for design of good prunable interleavers for turbo codes [PDF]
IEEE Transactions on Wireless Communications, 2006 This paper addresses the design of semi-random, prunable interleavers for parallel concatenated convolutional codes (PCCC). The proposed technique is iterative and is based on the growth of a smaller interleaver up to the desired length N.
M Laddomada
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Optimized Prunable Single-Cycle Interleavers for Turbo Codes [PDF]
IEEE Transactions on Communications, 2004 This paper is aimed at the problem of designing optimized interleavers for parallel concatenated convolutional codes (PCCC) that satisfy several requirements simultaneously: 1) designing interleavers tailored to the constituent codes of the PCCC; 2 ...
F Daneshgaran, M Laddomada
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Estimation of Convolutional Interleaver in a Non-cooperative Context
2020 22nd International Conference on Advanced Communication Technology (ICACT), 2020In this paper, we propose the method for blind estimation of convolutional interleaver parameters in a noncooperative context by analysing repetitive patterns in interleaved sequences. Simulation results verify that proposed method could effectively estimate convolutional interleaver parameters in a noisy environment.
Yoonji Kim, Geunbae Kim, Dongweon Yoon
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Interleaved Group Convolutions
2017 IEEE International Conference on Computer Vision (ICCV), 2017In this paper, we present a simple and modularized neural network architecture, named interleaved group convolutional neural networks (IGCNets). The main point lies in a novel building block, a pair of two successive interleaved group convolutions: primary group convolution and secondary group convolution. The two group convolutions are complementary: (
Ting Zhang 0002 +3 more
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Deep Convolutional Network Based on Interleaved Fusion Group
IEEE Transactions on Cognitive and Developmental Systems, 2021It is known that the classification accuracy of the deep convolutional network can be remarkably improved by increasing its depth and width. However, as the network size increases, the number of network parameters will increase significantly, which results in network redundancy and performance degradation.
Enhui Lv +3 more
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Evaluation of interleaving effect on convolutional correcting code
2005 12th IEEE International Conference on Electronics, Circuits and Systems, 2005We know that the role of the technique of interleaving in the turbo codes is determining, this is due to the fact that the turbo codes have two (or more) decoder elements concatenated in parallel or serially. Each decoder alone can produce series of erroneous bits which, if not scattered on the entire data frame with an interleaving operation, degrade ...
Maher Kouraichi +3 more
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Interleaved Structured Sparse Convolutional Neural Networks
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018In this paper, we study the problem of designing efficient convolutional neural network architectures with the interest in eliminating the redundancy in convolution kernels. In addition to structured sparse kernels, low-rank kernels and the product of low-rank kernels, the product of structured sparse kernels, which is a framework for interpreting the ...
Guotian Xie +5 more
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