A System for Compressive Sensing Signal Reconstruction

TL;DR

This paper introduces a hardware-efficient system for sparse signal reconstruction using a modified threshold method and a scalable QR decomposition approach, simplifying implementation and reducing complexity.

Contribution

It presents a novel hardware architecture that reformulates the minimization problem with a triangular R matrix, enabling easier and scalable implementation.

Findings

Efficient hardware implementation of sparse signal reconstruction.

Reduction in system complexity compared to traditional methods.

Scalable design adaptable to different sample sizes and sparsity levels.

Abstract

An architecture for hardware realization of a system for sparse signal reconstruction is presented. The threshold based reconstruction method is considered, which is further modified in this paper to reduce the system complexity in order to provide easier hardware realization. Instead of using the partial random Fourier transform matrix, the minimization problem is reformulated using only the triangular R matrix from the QR decomposition. The triangular R matrix can be efficiently implemented in hardware without calculating the orthogonal Q matrix. A flexible and scalable realization of matrix R is proposed, such that the size of R changes with the number of available samples and sparsity level.