Coded Demixing for Unsourced Random Access

TL;DR

This paper introduces coded demixing, a novel extension of coded compressed sensing, enabling joint recovery of signals sparse in multiple bases, with applications to unsourced random access in machine-type communication networks.

Contribution

It proposes a new coded demixing framework and an AMP-based recovery algorithm for multi-class URA, improving performance over traditional CCS methods.

Findings

Coded demixing effectively recovers signals sparse in multiple bases.

The proposed AMP algorithm achieves low complexity and high accuracy.

Numerical results demonstrate performance gains in heterogeneous URA networks.

Abstract

Unsourced random access (URA) is a recently proposed multiple access paradigm tailored to the uplink channel of machine-type communication networks. By exploiting a strong connection between URA and compressed sensing, the massive multiple access problem may be cast as a compressed sensing (CS) problem, albeit one in exceedingly large dimensions. To efficiently handle the dimensionality of the problem, coded compressed sensing (CCS) has emerged as a pragmatic signal processing tool that, when applied to URA, offers good performance at low complexity. While CCS is effective at recovering a signal that is sparse with respect to a single basis, it is unable to jointly recover signals that are sparse with respect to separate bases. In this article, the CCS framework is extended to the demixing setting, yielding a novel technique called coded demixing. A generalized framework for coded demixing is presented and a low-complexity recovery algorithm based on approximate message passing (AMP) is developed. Coded demixing is applied to heterogeneous multi-class URA networks and traditional single-class networks. Its performance is analyzed and numerical simulations are presented to highlight the benefits of coded demixing.