# Quantized compressed sensing for partial random circulant matrices

@article{Feng2017QuantizedCS, title={Quantized compressed sensing for partial random circulant matrices}, author={Joe-Mei Feng and Felix Krahmer and Rayan Saab}, journal={2017 International Conference on Sampling Theory and Applications (SampTA)}, year={2017}, pages={236-240} }

We provide the first analysis of a non-trivial quantization scheme for compressed sensing measurements arising from structured measurements. Specifically, our analysis studies compressed sensing matrices consisting of rows selected at random, without replacement, from a circulant matrix generated by a random subgaussian vector. We quantize the measurements using stable, possibly one-bit, Sigma-Delta schemes, and use a reconstruction method based on convex optimization. We show that the part of… CONTINUE READING

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