A Coded Compressed Sensing Scheme for Unsourced Multiple Access

• Published in: IEEE transactions on information theory Vol. 66; no. 10; pp. 6509 - 6533
• Main Authors: Amalladinne, Vamsi K., Chamberland, Jean-Francois, Narayanan, Krishna R.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 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
• DOI: 10.1109/TIT.2020.3012948

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.