A Coded Compressed Sensing Scheme for Unsourced Multiple Access

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 c...

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Vydáno v:IEEE transactions on information theory Ročník 66; číslo 10; s. 6509 - 6533
Hlavní autoři: Amalladinne, Vamsi K., Chamberland, Jean-Francois, Narayanan, Krishna R.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.10.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Communication
Complexity
Compressed sensing
Computational complexity
Decoding
Encoding
Error correction
forward error correction
Maximum likelihood decoding
Optimization
Parity
Partitioning algorithms
Payloads
Permutations
Redundancy
Stitching
unsourced multiple-access
ISSN:0018-9448, 1557-9654
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Shrnutí: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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ISSN:0018-9448
1557-9654
DOI:10.1109/TIT.2020.3012948