An enhanced decoding algorithm for coded compressed sensing

TL;DR

This paper presents an improved decoding algorithm for coded compressed sensing that enhances performance and reduces computational complexity by integrating fragment recovery and stitching processes using the parity structure.

Contribution

It introduces a novel decoding scheme where fragment recovery and stitching are combined, leveraging the parity structure to optimize the search space.

Findings

Significant reduction in computational complexity.

Improved recovery performance.

Effective use of parity structure for dynamic search space restriction.

Abstract

Coded compressed sensing is an algorithmic framework tailored to sparse recovery in very large dimensional spaces. This framework is originally envisioned for the unsourced multiple access channel, a wireless paradigm attuned to machine-type communications. Coded compressed sensing uses a divide-and-conquer approach to break the sparse recovery task into sub-components whose dimensions are amenable to conventional compressed sensing solvers. The recovered fragments are then stitched together using a low complexity decoder. This article introduces an enhanced decoding algorithm for coded compressed sensing where fragment recovery and the stitching process are executed in tandem, passing information between them. This novel scheme leads to gains in performance and a significant reduction in computational complexity. This algorithmic opportunity stems from the realization that the parity structure inherent to coded compressed sensing can be used to dynamically restrict the search space of the subsequent recovery algorithm.