Compressive Sensing Matrix Design for Fast Encoding and Decoding via Sparse FFT

Abstract

Compressive sensing (CS) is proposed for signal sampling below the Nyquist rate based on the assumption that the signal is sparse in some transformed domain. Most sensing matrices (e.g., Gaussian random matrix) in CS, however, usually suffer from unfriendly hardware implementation, high computation cost, and huge memory storage. In this letter, we propose a… (More)
DOI: 10.1109/LSP.2018.2809693

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