A Coupled Compressive Sensing Scheme for Unsourced Multiple Access

TL;DR

This paper proposes a new compressive sensing-based scheme for unsourced multiple access that efficiently recovers messages from multiple devices, outperforming existing practical coding schemes and approaching theoretical limits.

Contribution

It introduces a coupled compressive sensing framework combining data partitioning, error correction, and tree-based decoding for unsourced multiple access.

Findings

Outperforms existing practical coding schemes.

Achieves performance within 4.3 dB of the Polyanskiy limit.

Provides a computationally efficient decoding algorithm.

Abstract

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 sub-blocks, 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.