Unsourced Random Access with Coded Compressed Sensing: Integrating AMP and Belief Propagation

TL;DR

This paper proposes a novel integrated decoding framework for unsourced random access with coded compressed sensing, where AMP and belief propagation work together dynamically, improving performance and enabling accurate error prediction.

Contribution

It introduces a new integrated AMP and belief propagation decoding scheme that operates jointly, unlike previous independent approaches, with redesigned codes for tractability.

Findings

Significant performance improvements over previous methods.

Accurate error prediction via state evolution equations.

Validated through analytical and numerical results.

Abstract

Sparse regression codes with approximate message passing (AMP) decoding have gained much attention in recent times. The concepts underlying this coding scheme extend to unsourced random access with coded compressed sensing (CCS), as first demonstrated by Fengler, Jung, and Caire. Specifically, their approach employs a concatenated coding framework with an inner AMP decoder followed by an outer tree decoder. In their original implementation, these two components work independently of each other, with the tree decoder acting on the static output of the AMP decoder. This article introduces a novel framework where the inner AMP decoder and the outer tree decoder operate in tandem, dynamically passing information back and forth to take full advantage of the underlying CCS structure. This scheme necessitates the redesign of the tree code as to enable belief propagation in a computationally tractable manner. The enhanced architecture exhibits significant performance benefits over a range of system parameters. The error performance of the proposed scheme can be accurately predicted through a set of equations, known as state evolution of AMP. These findings are supported both analytically and through numerical methods.