A Hierarchical Stitching Algorithm for Coded Compressed Sensing

TL;DR

This paper introduces a hierarchical stitching algorithm for coded compressed sensing that improves scalability and hardware implementation, providing an error probability bound for better reliability in large-scale sensing tasks.

Contribution

It proposes a new hierarchical stitching algorithm for CCS, enhancing parallelization and hardware implementation over previous tree coding methods.

Findings

Algorithm simplifies hardware implementation and parallelization.

Provides an upper bound on recovery error probability.

Improves scalability for large sensing matrices.

Abstract

Recently, a novel coded compressed sensing (CCS) approach was proposed in [1] for dealing with the scalability problem for large sensing matrices in massive machine-type communications. The approach is to divide the compressed sensing (CS) problem into smaller CS sub-problems. However, such an approach requires stitching the results from the sub-problems to recover the result in the original CS problem. For this stitching problem, we propose a hierarchical stitching algorithm that is easier to implement in hardware for parallelization than the tree coding algorithm in [1]. For our algorithm, we also derive an upper bound on the probability of recovery errors.