Michael Lexa,
Mike Davies and
John Thompson just released two implementations this past week :

Synopsis
The MATLAB mfiles available for download on this page implement the power spectral density (PSD) estimation method proposed in:
M.A. Lexa, M.E. Davies, J.S. Thompson, Compressive and Noncompressive Power Spectral Density Estimation from Periodic Nonuniform Samples , 2011, Available on arXiv.
The method estimates the PSD of bandlimited, widesense stationary signals from subNyquist sampled data. The technique employs multicoset sampling and applies to spectrally sparse and nonsparse power spectra alike. For sparse density functions, compressed sensing theory is applied and the resulting compressive estimates exhibit better tradeoffs among the estimator's resolution, system complexity, and average sampling rate compared to their noncompressive counterparts. The estimator does not require signal reconstruction and can be directly obtained from solving either a least squares or a nonnegative least squares problem. The estimates are piecewise constant approximations whose resolutions (width of the piecewise constant segments) are controlled by the periodicity of the multicoset sampling. The estimates are statistically consistent, and the method is widely applicable.
The software allows one to reproduce the three examples given in the paper. Specific details are provided in the README.pdf file included in the download.
Note that the software is meant for researchers who want to quickly understand and experiment with the proposed method. Consequently, the MATLAB scripts have no error handling capabilities and will "break" if incorrect parameters values are entered or if assumptions are violated.

and

Synopsis
The CTSS Sampling Toolbox is a collection of MATLAB mfiles implementing three subNyquist, uniform sampling techniques for acquiring continuoustime spectrallysparse signals. Two of the techniques, the Random Demodulator (RD) and the Modulated Wideband Converter (MWC), leverage ideas from the theory of compressed sensing. The third, the Multicoset (MC) Sampler, predates compressed sensing, but shares many commonalities with the MWC.
The RD, the MWC, and the MC Sampler were originally proposed in the following works:
 J.A. Tropp, J.N. Laska, M.F. Duarte, J.K. Romberg, and R.G. Baraniuk, Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals, Information Theory, IEEE Transactions on , vol. 56, no. 1, pp. 520544, Jan 2010.
The toolbox is meant for researchers who want to quickly understand, experiment, and compare these sampling systems. Consequently, the MATLAB scripts have minimal error handling capabilities and will "break" if incorrect parameters values are entered or if assumptions are violated.

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