17 citations · 17 across the 1 of their papers we have counts for
6 papers
Compressive sensing for dynamic spectrum access networks: Techniques and tradeoffs
J. N. Laska, W. F. Bradley, T. W. Rondeau +2
We explore the practical costs and benefits of CS for dynamic spectrum access (DSA) networks. Firstly, we review several fast and practical techniques for energy detection without…
Regime Change: Bit-Depth versus Measurement-Rate in Compressive Sensing
Jason N. Laska, Richard G. Baraniuk
The recently introduced compressive sensing (CS) framework enables digital signal acquisition systems to take advantage of signal structures beyond bandlimitedness. Indeed, the num…
The Pros and Cons of Compressive Sensing for Wideband Signal Acquisition: Noise Folding vs. Dynamic Range
Mark A. Davenport, Jason N. Laska, John R. Treichler +1
Compressive sensing (CS) exploits the sparsity present in many signals to reduce the number of measurements needed for digital acquisition. With this reduction would come, in theor…
Robust 1-Bit Compressive Sensing via Binary Stable Embeddings of Sparse Vectors
Laurent Jacques, Jason N. Laska, Petros T. Boufounos +1
The Compressive Sensing (CS) framework aims to ease the burden on analog-to-digital converters (ADCs) by reducing the sampling rate required to acquire and stably recover sparse si…
A simple proof that random matrices are democratic
Mark A. Davenport, Jason N. Laska, Petros T. Boufounos +1
The recently introduced theory of compressive sensing (CS) enables the reconstruction of sparse or compressible signals from a small set of nonadaptive, linear measurements. If pro…
Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals
Joel A. Tropp, Jason N. Laska, Marco F. Duarte +2
Wideband analog signals push contemporary analog-to-digital conversion systems to their performance limits. In many applications, however, sampling at the Nyquist rate is inefficie…