Abstract
Quantization is an indispensable part of digital signal processing and digital communications systems. To incorporate CS methods in these systems, it is thus necessary to analyze and evaluate them considering the effect of measurement quantization.
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References
P. Boufounos. Greedy sparse signal reconstruction from sign measurements. In Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers, 2009, pages 1305–1309, Nov. 2009.
P. Boufounos and R. Baraniuk. 1-bit compressive sensing. In Information Sciences and Systems, 2008. CISS 2008. 42nd Annual Conference on, pages 16–21, Mar. 2008.
W. Dai, H. V. Pham, and O. Milenkovic. Distortion-rate functions for quantized compressive sensing. In IEEE Information Theory Workshop on Networking and Information Theory, 2009. ITW 2009, pages 171–175, June 2009.
L. Jacques, D. Hammond, and J. Fadili. Dequantizing compressed sensing: When oversampling and non-gaussian constraints combine. IEEE Transactions on Information Theory, 57(1):559–571, Jan. 2011.
L. Jacques, J. Laska, P. Boufounos, and R. Baraniuk. Robust 1-bit compressive sensing via binary stable embeddings of sparse vectors. IEEE Transactions on Information Theory, PP, 2013. DOI 10.1109/TIT.2012.2234823. http://arxiv.org/abs/1104.3160 arXiv:1104.3160 [cs.IT].
J. Laska, Z. Wen, W. Yin, and R. Baraniuk. Trust, but verify: Fast and accurate signal recovery from 1-bit compressive measurements. IEEE Transactions on Signal Processing, 59(11):5289–5301, Nov. 2011a.
J. N. Laska, P. T. Boufounos, and R. G. Baraniuk. Finite range scalar quantization for compressive sensing. In Proceedings of International Conference on Sampling Theory and Applications (SampTA), Toulouse, France, May 18–22 2009.
J. N. Laska, P. T. Boufounos, M. A. Davenport, and R. G. Baraniuk. Democracy in action: Quantization, saturation, and compressive sensing. Applied and Computational Harmonic Analysis, 31(3):429–443, Nov. 2011b.
D. Needell and J. A. Tropp. CoSaMP: Iterative signal recovery from incomplete and inaccurate samples. Applied and Computational Harmonic Analysis, 26(3):301–321, 2009.
Y. Plan and R. Vershynin. One-bit compressed sensing by linear programming. http://arxiv.org/abs/1109.4299 arXiv:1109.4299, Sept. 2011.
Y. Plan and R. Vershynin. Robust 1-bit compressed sensing and sparse logistic regression: A convex programming approach. IEEE Transactions on Information Theory, 59(1):482–494, Jan. 2013.
J. Sun and V. Goyal. Optimal quantization of random measurements in compressed sensing. In IEEE International Symposium on Information Theory, 2009. ISIT 2009, pages 6–10, July 2009.
M. Yan, Y. Yang, and S. Osher. Robust 1-bit compressive sensing using adaptive outlier pursuit. IEEE Transactions on Signal Processing, 60(7): 3868–3875, July 2012.
A. Zymnis, S. Boyd, and E. Candès. Compressed sensing with quantized measurements. IEEE Signal Processing Letters, 17(2):149–152, Feb. 2010.
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Bahmani, S. (2014). 1-Bit Compressed Sensing. In: Algorithms for Sparsity-Constrained Optimization. Springer Theses, vol 261. Springer, Cham. https://doi.org/10.1007/978-3-319-01881-2_4
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DOI: https://doi.org/10.1007/978-3-319-01881-2_4
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