Abstract
In this paper, a deterministic sparse Fourier transform algorithm is presented which breaks the quadratic-in-sparsity runtime bottleneck for a large class of periodic functions exhibiting structured frequency support. These functions include, e.g., the often considered set of block frequency sparse functions of the form
as a simple subclass. Theoretical error bounds in combination with numerical experiments demonstrate that the newly proposed algorithms are both fast and robust to noise. In particular, they outperform standard sparse Fourier transforms in the rapid recovery of block frequency sparse functions of the type above.
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References
Akavia, A.: Deterministic sparse Fourier approximation via fooling arithmetic progressions. In: Proceedings of 23rd COLT, pp 381–393 (2010)
Akavia, A., Goldwasser, S., Safra, S.: Proving hard-core predicates using list decoding. FOCS 44, 146–159 (2003)
Bailey, J., Iwen, M.A., Spencer, C.V.: On the design of deterministic matrices for fast recovery of Fourier compressible functions. SIAM J. Matrix Anal. Appl. 33 (1), 263–289 (2012)
Bittens, S.: Sparse FFT for functions with short frequency support. Dolomites Res Notes Approx. 10, 43–55 (2017)
Bluestein, L.I.: A linear filtering approach to the computation of discrete Fourier transform. IEEE Trans. Audio Electroacoust. 18(4), 451–455 (1970)
Boufounos, P., Cevher, V., Gilbert, A.C., Li, Y., Strauss, M.J.: What’s the frequency, Kenneth?: Sublinear Fourier sampling off the grid RANDOM/APPROX (2012)
Bourgain, J., Dilworth, S., Ford, K., Konyagin, S., Kutzarova, D., et al.: Explicit constructions of RIP matrices and related problems. Duke Math. J. 159(1), 145–185 (2011)
Cevher, V., Kapralov, M., Scarlett, J., Zandieh, A.: An adaptive sublinear-time block sparse Fourier transform. arXiv:1702.01286(2017)
Cheraghchi, M., Indyk, P.: Nearly optimal deterministic algorithm for sparse Walsh-Hadamard transform. In: Proceedings of the 27th Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 298–317 (2016)
Choi, B., Christlieb, A., Wang, Y.: Multi-dimensional sublinear sparse Fourier algorithm. arXiv:160607407 (2016)
Christlieb, A., Lawlor, D., Wang, Y.: A multiscale sub-linear time Fourier algorithm for noisy data. Appl. Comput. Harmon. Anal. 40(3), 553–574 (2016)
Feng, P., Bresler, Y.: Spectrum-blind minimum-rate sampling and reconstruction of multiband signals. In: 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings, vol. 3, pp 1688–1691 (1996)
Foucart, S., Rauhut, H.: A Mathematical Introduction to Compressive Sensing. Birkhäuser Basel (2013)
Gilbert, A.C., Guha, S., Indyk, P., Muthukrishnan, M., Strauss, M.J.: Near-optimal sparse Fourier representations via sampling. STOC (2002)
Gilbert, A.C., Indyk, P., Iwen, M.A., Schmidt, L.: Recent developments in the sparse Fourier transform: a compressed Fourier transform for big data. IEEE Signal Process. Mag. 31(5), 91–100 (2014)
Gilbert, A.C., Muthukrishnan, M., Strauss, M.J.: Improved time bounds for near-optimal space Fourier representations. SPIE Conference, Wavelets (2005)
Hassanieh, H., Adib, F., Katabi, D., Indyk, P.: Faster GPS via the sparse Fourier transform MOBICOM (2012)
Hassanieh, H., Indyk, P., Katabi, D., Price, E.: Near-optimal algorithm for sparse Fourier transform STOC (2012)
Hassanieh, H., Indyk, P., Katabi, D., Price, E.: sFFT: sparse fast Fourier transform. http://groups.csail.mit.edu/netmit/sFFT/ (2012)
Hassanieh, H., Indyk, P., Katabi, D., Price, E.: Simple and practical algorithm for sparse Fourier transform SODA (2012)
Hassanieh, H., Shi, L., Abari, O., Hamed, E., Katabi, D.: GHz-wide sensing and decoding using the sparse Fourier transform INFOCOM (2014)
Heider, S., Kunis, S., Potts, D.: Veit, M.: A sparse prony FFT. In: Proceedings of the 10th International Conference on Sampling Theory and Applications (SAMPTA), pp. 572–575 (2013)
Hu, X., Iwen, M.A., Kim, H.: Rapidly computing sparse Legendre expansions via sparse Fourier transforms. Numer. Algorithms 74(4), 1029–1059 (2017)
Indyk, P., Kapralov, M., Price, E.: (Nearly) sample-optimal sparse Fourier transform SODA (2014)
Iwen, M.A.: Combinatorial sublinear-time Fourier algorithms. Found. Comput. Math. 10, 303–338 (2010)
Iwen, M.A.: Improved approximation guarantees for sublinear-time Fourier algorithms. Appl. Comput. Harmon. Anal. 34, 57–82 (2013)
Iwen, M.A.: MSU’s Sparse Fourier Repository. http://sourceforge.net/projects/aafftannarborfa/ (2013)
Iwen, M.A., Gilbert, A.C., Strauss, M.J.: Empirical evaluation of a sub-linear time sparse DFT Algorithm. Commun. Math. Sci. 5(4), 981–998 (2007)
Iwen, M.A., Spencer, C.V.: Improved bounds for a deterministic sublinear-time sparse Fourier algorithm conference on information systems (CISS) (2008)
Lang, S.: Algebra, revised 3rd edn. Graduate Texts in Mathematics. Springer, New York (2005)
Laska, J., Kirolos, S., Massoud, Y., Baraniuk, R., Gilbert, A.C., Iwen, M.A., Strauss, M.J.: Random sampling for analog-to-information conversion of wideband signals. In: 2006 IEEE Dallas/CAS Workshop On Design, Applications, Integration and Software, pp. 119–122 (2006)
Lawlor, D., Wang, Y., Christlieb, A.: Adaptive sub-linear time fourier algorithms. Advances in Adaptive Data Analysis 5(01), 1350,003 (2013)
Mansour, Y.: Randomized interpolation and approximation of sparse polynomials ICALP (1992)
Merhi, S., Zhang, R., Iwen, M.A., Christlieb, A.: A new class of fully discrete sparse Fourier transforms: faster stable implementations with guarantees. Fourier Anal. Appl. https://doi.org/10.1007/s00041-018-9616-4 (2018)
Mishali, M., Eldar, Y.C.: Blind multiband signal reconstruction: compressed sensing for analog signals. IEEE Trans. Signal Process. 57(3), 993–1009 (2009)
Mishali, M., Eldar, Y.C.: From Theory to Practice: Sub-Nyquist sampling of sparse wideband analog signals. IEEE J. Sel. Top. Sign. Process. 4(2), 375–391 (2010)
Mishali, M., Eldar, Y.C., Dounaevsky, O., Shoshan, E.: Xampling: analog to digital at sub-Nyquist rates. IET Circuits Devices Syst. 5(1), 8–20 (2011)
Mishali, M., Eldar, Y.C., Tropp, J.A.: Efficient sampling of sparse wideband analog signals. In: 2008 IEEE 25th Convention of Electrical and Electronics Engineers in Israel, pp. 290–294 (2008)
Montgomery, H., Vaughan, R.: Multiplicative Number Theory I: Classical Theory. Cambridge Studies in Advanced Mathematics. Cambridge university press, Cambridge (2007)
Morotti, L.: Explicit universal sampling sets in finite vector spaces. Appl. Comput. Harmon. Anal. 43(2), 354–369 (2017)
Plonka, G., Tasche, M.: Prony methods for recovery of structured functions. GAMM-Mitt. 37(2), 239–258 (2014)
Plonka, G., Wannenwetsch, K.: A deterministic sparse FFT algorithm for vectors with small support. Numer. Algorithms 71(4), 889–905 (2016)
Plonka, G., Wannenwetsch, K.: A sparse fast Fourier algorithm for real non-negative vectors. J. Comput. Appl. Math. 321, 532–539 (2017)
Plonka, G., Wannenwetsch, K., Cuyt, A., Lee, W.S.: Deterministic sparse FFT for M-sparse vectors https://doi.org/10.1007/s11075-017-0370-5 (2017)
Potts, D., Tasche, M., Volkmer, T.: Efficient spectral estimation by MUSIC and ESPRIT with application to sparse FFT. Frontiers in Applied Mathematics and Statistics 2, 1 (2016)
Rabiner, L.R., Schafer, R.W., Rader, C.M.: The chirp-z transform algorithm. IEEE Trans. Audio Electroacoust. 17, 86–92 (1969)
Segal, B., Iwen, M.A.: Improved sparse Fourier approximation results: faster implementations and stronger guarantees. Numer. Algorithms 63(2), 239–263 (2013)
Yenduri, P., Gilbert, A.C.: Compressive, collaborative spectrum sensing for wideband cognitive radios. https://doi.org/10.1109/ISWCS.2012.6328424 (2012)
Yenduri, P.K., Rocca, A.Z., Rao, A.S., Naraghi, S., Flynn, M.P., Gilbert, A.C.: A low-power compressive sampling time-based analog-to-digital converter. IEEE J. Em. Sel. Top. C. 2(3), 502–515 (2012)
Acknowledgements
The authors would like to thank both Felix Krahmer for introducing them at TUM in the summer of 2016 and Gerlind Plonka for her ongoing support, and particularly for her generosity in providing resources that aided in the writing of this paper.
Funding
Sina Bittens was supported in part by the DFG in the framework of the GRK 2088. Mark Iwen and Ruochuan Zhang were both supported in part by NSF DMS-1416752.
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Communicated by: Yang Wang
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Bittens, S., Zhang, R. & Iwen, M.A. A deterministic sparse FFT for functions with structured Fourier sparsity. Adv Comput Math 45, 519–561 (2019). https://doi.org/10.1007/s10444-018-9626-4
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DOI: https://doi.org/10.1007/s10444-018-9626-4
Keywords
- Sparse Fourier transform (SFT)
- Structured sparsity
- Deterministic constructions
- Approximation algorithms