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Jeanfrancois Chamberland - One of the best experts on this subject based on the ideXlab platform.
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A Coded Compressed Sensing Scheme for Unsourced Multiple Access
IEEE Transactions on Information Theory, 2020Co-Authors: Vamsi K Amalladinne, Jeanfrancois Chamberland, Krishna R NarayananAbstract:This article introduces a novel scheme, termed Coded compressed sensing, for unsourced multiple-access communication. The proposed divide-and-conquer approach leverages recent advances in compressed sensing and forward error correction to produce a novel uncoordinated access paradigm, along with a computationally efficient decoding algorithm. Within this framework, every active device partitions its data into several sub-Blocks and, subsequently, adds redundancy using a systematic Linear Block Code. Compressed sensing techniques are then employed to recover sub-Blocks up to a permutation of their order, and the original messages are obtained by stitching fragments together using a tree-based algorithm. The error probability and computational complexity of this access paradigm are characterized. An optimization framework, which exploits the tradeoff between performance and computational complexity, is developed to assign parity-check bits to each sub-Block. In addition, two emblematic parity bit allocation strategies are examined and their performances are analyzed in the limit as the number of active users and their corresponding payloads tend to infinity. The number of channel uses needed and the computational complexity associated with these allocation strategies are established for various scaling regimes. Numerical results demonstrate that Coded compressed sensing outperforms other existing practical access strategies over a range of operational scenarios.
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A Coupled Compressive Sensing Scheme for Uncoordinated Multiple Access.
arXiv: Signal Processing, 2018Co-Authors: Vamsi K Amalladinne, Avinash Vem, Dileep Kumar Soma, Krishna R Narayanan, Jeanfrancois ChamberlandAbstract:This article introduces a novel communication scheme, termed Coded compressed sensing, for unsourced multiple-access communication. The proposed divide-and-conquer approach leverages recent advances in compressed sensing and forward error correction to produce a novel uncoordinated access paradigm, along with a computationally efficient decoding algorithm. Within this framework, every active device partitions its data into several sub-Blocks and, subsequently, adds redundancy using a systematic Linear Block Code. Compressed sensing techniques are then employed to recover sub-Blocks up to a permutation of their order, and the original messages are obtained by stitching fragments together using a tree-based algorithm. The error probability and computational complexity of this access paradigm are characterized. An optimization framework, which exploits the tradeoff between performance and computational complexity, is developed to assign parity-check bits to each sub-Block. In addition, two emblematic parity bit allocation strategies are examined and their performances are analyzed in the limit as the number of active users and their corresponding payloads tend to infinity. The number of channel uses needed and the computational complexity associated with these allocation strategies are established for various scaling regimes. Numerical results demonstrate that Coded compressed sensing outperforms other existing practical access strategies over a range of operational scenarios.
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a coupled compressive sensing scheme for unsourced multiple access
International Conference on Acoustics Speech and Signal Processing, 2018Co-Authors: Vamsi K Amalladinne, Avinash Vem, Dileep Kumar Soma, Krishna R Narayanan, Jeanfrancois ChamberlandAbstract:This article introduces a novel paradigm for the unsourced multiple-access communication problem. This divide-and-conquer approach leverages recent advances in compressive sensing and forward error correction to produce a computationally efficient algorithm. Within the proposed framework, every active device first partitions its data into several subBlocks, and subsequently adds redundancy using a systematic Linear Block Code. Compressive sensing techniques are then employed to recover sub-Blocks, and the original messages are obtained by connecting pieces together using a low-complexity tree-based algorithm. Numerical results suggest that the proposed scheme outperforms other existing practical coding schemes. Measured performance lies approximately 4.3 dB away from the Polyanskiy achievability limit, which is obtained in the absence of complexity constraints.
Vamsi K Amalladinne - One of the best experts on this subject based on the ideXlab platform.
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A Coded Compressed Sensing Scheme for Unsourced Multiple Access
IEEE Transactions on Information Theory, 2020Co-Authors: Vamsi K Amalladinne, Jeanfrancois Chamberland, Krishna R NarayananAbstract:This article introduces a novel scheme, termed Coded compressed sensing, for unsourced multiple-access communication. The proposed divide-and-conquer approach leverages recent advances in compressed sensing and forward error correction to produce a novel uncoordinated access paradigm, along with a computationally efficient decoding algorithm. Within this framework, every active device partitions its data into several sub-Blocks and, subsequently, adds redundancy using a systematic Linear Block Code. Compressed sensing techniques are then employed to recover sub-Blocks up to a permutation of their order, and the original messages are obtained by stitching fragments together using a tree-based algorithm. The error probability and computational complexity of this access paradigm are characterized. An optimization framework, which exploits the tradeoff between performance and computational complexity, is developed to assign parity-check bits to each sub-Block. In addition, two emblematic parity bit allocation strategies are examined and their performances are analyzed in the limit as the number of active users and their corresponding payloads tend to infinity. The number of channel uses needed and the computational complexity associated with these allocation strategies are established for various scaling regimes. Numerical results demonstrate that Coded compressed sensing outperforms other existing practical access strategies over a range of operational scenarios.
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A Coupled Compressive Sensing Scheme for Uncoordinated Multiple Access.
arXiv: Signal Processing, 2018Co-Authors: Vamsi K Amalladinne, Avinash Vem, Dileep Kumar Soma, Krishna R Narayanan, Jeanfrancois ChamberlandAbstract:This article introduces a novel communication scheme, termed Coded compressed sensing, for unsourced multiple-access communication. The proposed divide-and-conquer approach leverages recent advances in compressed sensing and forward error correction to produce a novel uncoordinated access paradigm, along with a computationally efficient decoding algorithm. Within this framework, every active device partitions its data into several sub-Blocks and, subsequently, adds redundancy using a systematic Linear Block Code. Compressed sensing techniques are then employed to recover sub-Blocks up to a permutation of their order, and the original messages are obtained by stitching fragments together using a tree-based algorithm. The error probability and computational complexity of this access paradigm are characterized. An optimization framework, which exploits the tradeoff between performance and computational complexity, is developed to assign parity-check bits to each sub-Block. In addition, two emblematic parity bit allocation strategies are examined and their performances are analyzed in the limit as the number of active users and their corresponding payloads tend to infinity. The number of channel uses needed and the computational complexity associated with these allocation strategies are established for various scaling regimes. Numerical results demonstrate that Coded compressed sensing outperforms other existing practical access strategies over a range of operational scenarios.
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a coupled compressive sensing scheme for unsourced multiple access
International Conference on Acoustics Speech and Signal Processing, 2018Co-Authors: Vamsi K Amalladinne, Avinash Vem, Dileep Kumar Soma, Krishna R Narayanan, Jeanfrancois ChamberlandAbstract:This article introduces a novel paradigm for the unsourced multiple-access communication problem. This divide-and-conquer approach leverages recent advances in compressive sensing and forward error correction to produce a computationally efficient algorithm. Within the proposed framework, every active device first partitions its data into several subBlocks, and subsequently adds redundancy using a systematic Linear Block Code. Compressive sensing techniques are then employed to recover sub-Blocks, and the original messages are obtained by connecting pieces together using a low-complexity tree-based algorithm. Numerical results suggest that the proposed scheme outperforms other existing practical coding schemes. Measured performance lies approximately 4.3 dB away from the Polyanskiy achievability limit, which is obtained in the absence of complexity constraints.
Krishna R Narayanan - One of the best experts on this subject based on the ideXlab platform.
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A Coded Compressed Sensing Scheme for Unsourced Multiple Access
IEEE Transactions on Information Theory, 2020Co-Authors: Vamsi K Amalladinne, Jeanfrancois Chamberland, Krishna R NarayananAbstract:This article introduces a novel scheme, termed Coded compressed sensing, for unsourced multiple-access communication. The proposed divide-and-conquer approach leverages recent advances in compressed sensing and forward error correction to produce a novel uncoordinated access paradigm, along with a computationally efficient decoding algorithm. Within this framework, every active device partitions its data into several sub-Blocks and, subsequently, adds redundancy using a systematic Linear Block Code. Compressed sensing techniques are then employed to recover sub-Blocks up to a permutation of their order, and the original messages are obtained by stitching fragments together using a tree-based algorithm. The error probability and computational complexity of this access paradigm are characterized. An optimization framework, which exploits the tradeoff between performance and computational complexity, is developed to assign parity-check bits to each sub-Block. In addition, two emblematic parity bit allocation strategies are examined and their performances are analyzed in the limit as the number of active users and their corresponding payloads tend to infinity. The number of channel uses needed and the computational complexity associated with these allocation strategies are established for various scaling regimes. Numerical results demonstrate that Coded compressed sensing outperforms other existing practical access strategies over a range of operational scenarios.
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A Coupled Compressive Sensing Scheme for Uncoordinated Multiple Access.
arXiv: Signal Processing, 2018Co-Authors: Vamsi K Amalladinne, Avinash Vem, Dileep Kumar Soma, Krishna R Narayanan, Jeanfrancois ChamberlandAbstract:This article introduces a novel communication scheme, termed Coded compressed sensing, for unsourced multiple-access communication. The proposed divide-and-conquer approach leverages recent advances in compressed sensing and forward error correction to produce a novel uncoordinated access paradigm, along with a computationally efficient decoding algorithm. Within this framework, every active device partitions its data into several sub-Blocks and, subsequently, adds redundancy using a systematic Linear Block Code. Compressed sensing techniques are then employed to recover sub-Blocks up to a permutation of their order, and the original messages are obtained by stitching fragments together using a tree-based algorithm. The error probability and computational complexity of this access paradigm are characterized. An optimization framework, which exploits the tradeoff between performance and computational complexity, is developed to assign parity-check bits to each sub-Block. In addition, two emblematic parity bit allocation strategies are examined and their performances are analyzed in the limit as the number of active users and their corresponding payloads tend to infinity. The number of channel uses needed and the computational complexity associated with these allocation strategies are established for various scaling regimes. Numerical results demonstrate that Coded compressed sensing outperforms other existing practical access strategies over a range of operational scenarios.
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a coupled compressive sensing scheme for unsourced multiple access
International Conference on Acoustics Speech and Signal Processing, 2018Co-Authors: Vamsi K Amalladinne, Avinash Vem, Dileep Kumar Soma, Krishna R Narayanan, Jeanfrancois ChamberlandAbstract:This article introduces a novel paradigm for the unsourced multiple-access communication problem. This divide-and-conquer approach leverages recent advances in compressive sensing and forward error correction to produce a computationally efficient algorithm. Within the proposed framework, every active device first partitions its data into several subBlocks, and subsequently adds redundancy using a systematic Linear Block Code. Compressive sensing techniques are then employed to recover sub-Blocks, and the original messages are obtained by connecting pieces together using a low-complexity tree-based algorithm. Numerical results suggest that the proposed scheme outperforms other existing practical coding schemes. Measured performance lies approximately 4.3 dB away from the Polyanskiy achievability limit, which is obtained in the absence of complexity constraints.
Jon Feldman - One of the best experts on this subject based on the ideXlab platform.
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a new Linear programming approach to decoding Linear Block Codes
IEEE Transactions on Information Theory, 2008Co-Authors: Kai Yang, Xiaodong Wang, Jon FeldmanAbstract:In this paper, we propose a new Linear programming formulation for the decoding of general Linear Block Codes. Different from the original formulation given by Feldman, the number of total variables to characterize a parity-check constraint in our formulation is less than twice the degree of the corresponding check node. The equivalence between our new formulation and the original formulation is proven. The new formulation facilitates to characterize the structure of Linear Block Codes, and leads to new decoding algorithms. In particular, we show that any fundamental polytope is simply the intersection of a group of the so-called minimum polytopes, and this simplified formulation allows us to formulate the problem of calculating the minimum Hamming distance of any Linear Block Code as a simple Linear integer programming problem with much less auxiliary variables. We then propose a branch-and-bound method to compute a lower bound to the minimum distance of any Linear Code by solving a corresponding Linear integer programming problem. In addition, we prove that, for the family of single parity-check (SPC) product Codes, the fractional distance and the pseudodistance are both equal to the minimum distance. Finally, we propose an efficient algorithm for decoding SPC product Codes with low complexity and maximum-likelihood (ML) decoding performance.
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cascaded formulation of the fundamental polytope of general Linear Block Codes
International Symposium on Information Theory, 2007Co-Authors: Kai Yang, Xiaodong Wang, Jon FeldmanAbstract:We propose a new Linear programming formulation for the decoding of general Linear Block Codes. Different from the original formulation given by J. Feldman (2003), the number of total variables to characterize a parity-check constraint in our formulation is less than twice the degree of the corresponding check node. The equivalence between our new formulation and the original formulation is proven. Moreover, we show that any fundamental polytope is simply the intersection of a group of so-called minimum polytopes. Based on this, we propose a branch-and-bound method to compute a non-trivial lower bound to the minimum distance of a Linear Block Code with affordable complexity.
Dileep Kumar Soma - One of the best experts on this subject based on the ideXlab platform.
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A Coupled Compressive Sensing Scheme for Uncoordinated Multiple Access.
arXiv: Signal Processing, 2018Co-Authors: Vamsi K Amalladinne, Avinash Vem, Dileep Kumar Soma, Krishna R Narayanan, Jeanfrancois ChamberlandAbstract:This article introduces a novel communication scheme, termed Coded compressed sensing, for unsourced multiple-access communication. The proposed divide-and-conquer approach leverages recent advances in compressed sensing and forward error correction to produce a novel uncoordinated access paradigm, along with a computationally efficient decoding algorithm. Within this framework, every active device partitions its data into several sub-Blocks and, subsequently, adds redundancy using a systematic Linear Block Code. Compressed sensing techniques are then employed to recover sub-Blocks up to a permutation of their order, and the original messages are obtained by stitching fragments together using a tree-based algorithm. The error probability and computational complexity of this access paradigm are characterized. An optimization framework, which exploits the tradeoff between performance and computational complexity, is developed to assign parity-check bits to each sub-Block. In addition, two emblematic parity bit allocation strategies are examined and their performances are analyzed in the limit as the number of active users and their corresponding payloads tend to infinity. The number of channel uses needed and the computational complexity associated with these allocation strategies are established for various scaling regimes. Numerical results demonstrate that Coded compressed sensing outperforms other existing practical access strategies over a range of operational scenarios.
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a coupled compressive sensing scheme for unsourced multiple access
International Conference on Acoustics Speech and Signal Processing, 2018Co-Authors: Vamsi K Amalladinne, Avinash Vem, Dileep Kumar Soma, Krishna R Narayanan, Jeanfrancois ChamberlandAbstract:This article introduces a novel paradigm for the unsourced multiple-access communication problem. This divide-and-conquer approach leverages recent advances in compressive sensing and forward error correction to produce a computationally efficient algorithm. Within the proposed framework, every active device first partitions its data into several subBlocks, and subsequently adds redundancy using a systematic Linear Block Code. Compressive sensing techniques are then employed to recover sub-Blocks, and the original messages are obtained by connecting pieces together using a low-complexity tree-based algorithm. Numerical results suggest that the proposed scheme outperforms other existing practical coding schemes. Measured performance lies approximately 4.3 dB away from the Polyanskiy achievability limit, which is obtained in the absence of complexity constraints.
