Computing a k-sparse n-length Discrete Fourier Transform using at most 4k samples and O(k log k) complexity

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 exactly k-sparse (where k <;<; n), can we do better? We show that asymptotically in k and n, when k is sub-linear in n (i.e., k ∝ nδ where 0 <; δ <; 1), and the support of the non-zero DFT coefficients is uniformly random, we can exploit this sparsity in two fundamental ways (i) sample complexity: we need only M = rk deterministically chosen samples of the input signal x (where r <; 4 when 0 <; δ <; 0.99); and (ii) computational complexity: we can reliably compute the DFT X using O(k log k) operations, where the constants in the big Oh are small. Our algorithm succeeds with high probability, with the probability of failure vanishing to zero asymptotically in the number of samples acquired, M. Our approach is based on filterless subsampling of the input signal x using a small set of carefully chosen uniform subsampling patterns guided by the Chinese Remainder Theorem (CRT). Specifically, our subsampling operation on x is designed to create aliasing patterns on the spectrum X that "look like" parity-check constraints of good erasure-correcting sparse-graph codes. We show how computing the sparse DFT X is equivalent to decoding of these sparse-graph codes and is low in both sample complexity and decoding complexity. We accordingly dub our algorithm the FFAST (Fast Fourier Aliasing-based Sparse Transform) algorithm. In our analysis, we rigorously connect our CRT based graph constructions to random sparse-graph codes based on a balls-and-bins model and analyze the convergence behavior of the latter using well-studied density evolution techniques from coding theory. We provide simulation results in Section IV that corroborate our theoretical findings, and validate the empirical performance of the FFAST algorithm.