Given an n-length input signal x, it is well known that its Discrete Fourier Transform (DFT), X,
can be computed in O (nlogn) complexity using a Fast Fourier Transform. If the spectrum X is …

We give an algorithm for ℓ 2/ℓ 2 sparse recovery from Fourier measurements using O (k log
N) samples, matching the lower bound of Do Ba-Indyk-Price-Woodruff'10 for non-adaptive …

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 …

In this paper, we consider the extensively studied problem of computing a k-sparse
approximation to the d-dimensional Fourier transform of a length n signal. Our algorithm …

Suppose an length signal has known frequency support of size. Given access to samples of
this signal, how fast can we compute the DFT? The answer to this question depends on the …

We consider the problem of finding the Discrete Fourier Transform (DFT) of N-length signals
with known frequency support of size k. When N is a power of 2 and the frequency support is …

We present a novel algorithm, named the 2D-FFAST, to compute a sparse 2D-Discrete
Fourier Transform (2D-DFT) featuring both low sample complexity and low computational …

In this paper, we consider the compressive sensing (CS) problem in the presence of noise.
The problem is to recover a K-sparse signal s∈ ℝ n from noisy linear measurements y= As+ …

The fundamental communication problem in the wireless Internet-of-Things (IoT) is to
discover a massive number of devices and to provide them with reliable access to shared …

In this paper we revisit the deterministic version of the Sparse Fourier Transform problem,
which asks to read only a few entries of x∈ ℂⁿ and design a recovery algorithm such that the …