Compressive sensing for wireless networks

Compressive Sensing for Wireless Networks

Volumn 9781107018839, Issue , 2010, Pages 1-293

  Han, Zhu ^a   Li, Husheng ^b   Yin, Wotao ^c  

^a University of Houston   (United States)

^b UNIVERSITY OF TENNESSEE   (United States)

^c Rice University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

SIGNAL PROCESSING; SIGNAL RECONSTRUCTION; STUDENTS; WIRELESS NETWORKS;

COMMUNICATIONS AND NETWORKINGS; COMPRESSIVE SENSING; DIMENSIONALITY REDUCTION; ENGINEERING PERSPECTIVE; LARGE DATASETS; NETWORK SCENARIO; SAMPLING RATES; SPARSE SIGNALS;

COMPRESSED SENSING;

EID: 84923117729     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1017/CBO9781139088497     Document Type: Book

References (408)

1. T. S. Rappaport, Wireless Communications: Principles and Practice, 2nd edn. Upper Saddle River, New Jersey, USA: Prentice Hall, 2002.
2. W. Yu, W. Rhee, S. Boyd, and J. M. Cioffi, “Iterative water-filling for Gaussian vector multiple access channels,” IEEE Trans. on Information Theory, vol. 50, no. 1, pp. 145-152, Jan. 2004.
3. R. Cendrillon, M. Moonen, J. Verlinden, T. Bostoen, and W. Yu, “Optimal multiuser spectrum management for digital subscriber lines,” in Proc. of IEEE International Conference on Communications, Paris, France, June 2004.
4. R. Cendrillon and M. Moonen, “Iterative spectrum balancing for digital subscriber lines,” in Proc. of IEEE International Conference on Communications, Seoul, Korea, May 2005.
5. J. Papandriopoulos and J. S. Evans, “Low-complexity distributed algorithms for spectrum balancing in multi-user DSL networks,” in Proc. of IEEE International Conference on Communications, Istanbul, Turkey, June 2006.
6. R. Cendrillon, J. Huang, M. Chiang, andM. Moonen, “Autonomous spectrum balancing for digital subscriber lines,” IEEE Trans. on Signal Processing, vol. 55, no. 8, pp. 4241-4257, Aug. 2007.
7. L. Wooyul, K. Youngjae, M. H. Brady, and J. M. Cioffi, “Band-preference dynamic spectrum management in a DSL environment,” in Proc. IEEE Global Telecommunications Conference, San Francisco, CA, USA, November-December 2006.
8. J. M. Cioffi, W. Rhee, M. Mohseni, and M. H. Brady, “Band preference in dynamic spectrum management,” in Proc. of Eurasip Conference on Signal Processing, Vienna, Austria, September 2004.
9. [Online] available: http://www.umtsworld.com.
10. [Online] available: http://www.cdg.org.
11. J. M. Cioffi, W. Rhee, M. Mohseni, and M. H. Brady, “IEEE standard for local and metropolitan area networks part 16: Air interface for fixed broadband wireless access systems IEEE Std. 802.16-2004” (Revision of IEEE Std. 802.16-2001), pp. 27-28, 2004.
12. J. M. Cioffi, W. Rhee, M. Mohseni, and M. H. Brady, “Achieving wireless broadband with WiMAX,” IEEE Computer Society, Tech. Rep., 2004.
13. [Online] available: http://www.bluetooth.com/bluetooth/.
14. J. M. Cioffi, W. Rhee, M. Mohseni, and M. H. Brady, “Federal communications commission report,” FCC, February 2002, revision of part 15 of the commission's rules regarding ultra-wideband transmission systems, first report and order, available at http://www.naic.edu/~astro/RXstatus/Lnarrow/fccUWB.pdf.
15. A. Batra et al., “Multi-band OFDM physical layer proposal for IEEE 802.15 task group 3a,” IEEE P802.15-03/268r3, Tech. Rep., March 2004.
16. C. Perkins, “Highly dynamic destination-sequenced distance-vector routing (DSDV) for mobile computers,” in Proc. of ACM Conference on Communications Architectures, Protocols and Applications, London, UK, September 1994.
17. C.-C. Chiang, H.-K. Wu, W. Liu, and M. Gerla, “Routing in clustered multihop, mobile wireless networks with fading channel,” in Proc. of IEEE Singapore International Conference on Networks, Singapore, April 1997.
18. S. Murthy and J. J. Garcia-Luna-Aceves, “An efficient routing protocol for wireless networks,” ACM Mobile Networks and App. J., Special Issue on Routing in Mobile Communication Networks, vol. 1, no. 2, pp. 183-197, Oct. 1996.
19. C. Perkins and E. Royer, “Ad-hoc on-demand distance vector routing,” in Proc. of the 2nd IEEE Workshop on Mobile Computing Systems and Applications, New Orleans, LA, USA, February 1999.
20. D. B. Johnson, “Routing in ad hoc networks of mobile hosts,” in Proc. of the Workshop on Mobile Computing Systems and Applications, Santa Cruz, CA, USA, December 1994.
21. V. D. Park and M. S. Corson, “A highly adaptive distributed routing algorithm for mobile wireless networks,” in Proc. IEEE Conf. on Comp. Comm., Kobe, Japan, April 1997.
22. C. K. Toh, “Associativity-based routing for ad hoc mobile networks,” Wireless Personal Communications: An International Journal, vol. 4, no. 1, pp. 103-139, Mar. 1997.
23. R. Dube, C. D. Rais, K. Y. Wang, and S. K. Tripathi, “Signal stability-based adaptive routing (SSA) for ad-hoc mobile networks,” IEEE Personal Communications Magazine, vol. 4, no. 1, pp. 36-45, Feb. 1997.
24. K. Menger, “Zur allgemeinen kurventheorie,” Fund. Math., vol. 10, pp. 96-115, 1927.
25. T. Camp, J. Boleng, and V. Davies, “A survey of mobility models for ad hoc network research,” Wireless Communications and Mobile Computing: Special issue on Mobile Ad Hoc Networking: Research, Trends and Applications, vol. 2, no. 5, pp. 483-502, Sept. 2002.
26. J. Y. L. Boudec and M. Vojnovic, “Perfect simulation and stationarity of a class of mobility models,” in Proc. IEEE Conf. on Comp. Comm., Miami, FL, USA, March 2005.
27. [Online] available: http://www.antd.nist.gov/wahn_ssn.shtml.
28. B. Krishnamachari, Networking Wireless Sensors. Cambridge, UK: Cambridge University Press, 2005.
29. S. Meguerdichian, F. Koushanfar, M. Potkonjak, and M. B. Srivastava, “Coverage problems in wireless ad hoc sensor networks,” in Proc. IEEE Conf. on Comp. Comm. Anchorage, AK, USA, April 2001.
30. B. Chen and P. K. Varshney, “A Bayesian sampling approach to decision fusion using hierarchical models,” IEEE Trans. On Signal Processing, vol. 50, no. 8, pp. 1809-1818, Aug. 2002.
31. M. Ozay and F. T. Y. Vural, “A theoretical analysis of feature fusion in stacked generalization,” in Proc. of Signal Processing and Communications Applications Conference, Antalya, Turkey, April 2009.
32. J. P. Walters, Z. Liang, W. Shi, and V. Chaudhary, Wireless Sensor Network Security: A Survey Security in Distributed, Grid, and Pervasive Computing. CRC Press, Auerbach Publications, 2006.
33. K. J. Kerpez, D. L. Waring, S. Galli, J. Dixon, and P. Madon, “Advanced DSL management,” IEEE Communications Magazine, vol. 41, no. 9, pp. 116-123, Sept. 2003.
34. D. Tse and P. Viswanath, Fundamentals of Wireless Communication. Cambridge, UK: Cambridge University Press, 2005.
35. L. Zheng and D. N. C. Tse, “Diversity and multiplexing: A fundamental tradeoff in multipleantenna channels,” IEEE Trans. on Information Theory, vol. 49, no. 5, pp. 1073-1096, May 2003.
36. S. M. Alamouti, “A simple transmit diversity technique for wireless communications,” IEEE Journal on Selected Areas in Communications, vol. 16, no. 8, pp. 1451-1458, Oct. 1998.
37. S. Haykin, “Cognitive radio: Brain-empowered wireless communications,” IEEE Journal on Selected Areas in Communications, vol. 23, no. 2, pp. 201-220, Feb. 2005.
38. T. Issariyakul and E. Hossain, “ORCA-MRT: An optimization-based approach for fair scheduling in multirate TDMA wireless networks,” IEEE Transactions on Wireless Communications, vol. 4, no. 6, pp. 2823-2835, Nov. 2005.
39. M. Andrews, “A survey of scheduling theory in wireless data networks,” in IMA Summer Workshop on Wireless Communications, 2005.
40. T. Ng, I. Stoica, and H. Zhang, “Packet fair queueing algorithms for wireless networks with location-dependent errors,” in IEEE International Conference on Computer Communications, San Francisco, CA, USA, March-April 1998.
41. S. Lu, V. Bharghavan, and R. Srikant, “Fair scheduling in wireless packet networks,” IEEE/ACM Transactions on Networking, vol. 7, no. 4, pp. 473-337, Aug. 1999.
42. T. Issariyakul, E. Hossain, and D. I. Kim, “Medium access control protocols for wireless mobile ad hoc networks: Issues and approaches,” Wireless Communications and Mobile Computing, vol. 3, no. 8, pp. 935-958, Nov. 2003.
43. A. C. V. Gummalla and J. O. Limb, “Wireless medium access control protocols,” IEEE Communications Surveys and Tutorials, vol. 3, no. 2, pp. 2-15, 2nd Quarter 2000.
44. S. Kumar, V. S. Raghavan, and J. Deng, “Medium access control protocols for ad hoc wireless networks: A survey,” Ad Hoc Networks, vol. 4, no. 3, pp. 326-358, May 2006.
45. W. Ye and J. Heidemann, Medium Access Control in Wireless Sensor Networks. Netherlands: Wireless Sensor Networks, Kluwer Academic Publishers, 2003.
46. A. Gupta and P. Mohapatra, “A survey on ultra wide band medium access control schemes,” Computer Networks, vol. 51, no. 11, pp. 2976-2993, Aug. 2007.
47. I. F. Akyildiz, J. McNair, L. Carrasco, and R. Puigjaner, “Medium access control protocols for multimedia traffic in wireless networks,” IEEE Network, vol. 13, no. 4, pp. 39-47, July-August 1999.
48. P. Roshanand J. Leary, 802.11 Wireless LAN Fundamentals. Cisco Press, Indianapolis, IN, 2004.
49. M. Satyanarayanan, “Pervasive computing: Vision and challenges,” Personal Communications, IEEE, vol. 8, no. 4, pp. 10-17, Aug. 2001.
50. R. Glidden, C. Bockorick, S. Cooper, C. Diorio, D. Dressler, V. Gutnik, C. Hagen, D. Hara, T. Hass, T. Humes, J. Hyde, R. Oliver, O. Onen, A. Pesavento, K. Sundstrom, andM. Thomas, “Design ofultra-low-cost UHF RFID tags for supply chain applications,” Communications Magazine, IEEE, vol. 42, no. 8, pp. 140-151, Aug. 2004.
51. L. Mo, Y. He, Y. Liu, J. Zhao, S.-J. Tang, X.-Y. Li, and G. Dai, “Canopy closure estimates with Greenorbs: Sustainable sensing in the forest,” in Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems, Berkeley, CA, USA, 2009.
52. N. B. Priyantha, A. Chakraborty, and H. Balakrishnan, “The cricket location-support system,” in Proceedings of the 6th Annual International Conference on Mobile Computing and Networking, Boston, MA, USA, 2000.
53. A. Savvides, C.-C. Han, and M. B. Strivastava, “Dynamic fine-grained localization in ad-hoc networks of sensors,” in Proceedings of the 7th Annual International Conference on Mobile Computing and Networking, Rome, Italy, 2001.
54. D. Niculescu and B. Nath, “Ad hoc positioning system (APS) using AOA,” Twenty-Second Annual Joint Conference of the IEEE Computer and Communications. San Francisco, CA, USA, March-3 April 2003.
55. L. Doherty, K. Pister, and L. El Ghaoui, “Convex position estimation in wireless sensor networks,” Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies. Anchorage, AK, USA, April 2001.
56. T. He, C. Huang, B. M. Blum, J. A. Stankovic, and T. Abdelzaher, “Range-free localization schemes for large scale sensor networks,” in Proceedings of the 9th Annual International Conference on Mobile Computing and Networking. San Diego, CA, USA, 2003.
57. L. Kleinrock and J. Silvester, “Optimum transmission radii for packet radio networks or why six is a magic number,” in NTC'78; National Telecommunications Conference, Birmingham, AL, USA, Dec. 1978.
58. L. M. Ni, Y. Liu, Y. C. Lau, and A. P. Patil, “Landmarc: Indoor location sensing using active RFID,” Wirel. Netw., vol. 10, no. 6, pp. 701-710, Nov. 2004.
59. Y. Liu and Z. Yang, Location, Localization, and Localizability: Location — Awareness Technology for Wireless Networks. Berlin: Springer, 2011.
60. Y. Shang, W. Ruml, Y. Zhang, and M. P. J. Fromherz, “Localization from mere connectivity,” in Proceedings of the 4th ACM International Symposium on Mobile Ad Hoc Networking & Computing, Annapolis, MD, USA, 2003.
61. D. Niculescu and B. Nath, “Dv based positioning in ad hoc networks,” Telecommunication Systems, vol. 22, no. 1-4, pp. 267-280, Jan.-Apr. 2003.
62. D. Moore, J. Leonard, D. Rus, and S. Teller, “Robust distributed network localization with noisy range measurements,” in Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems, Baltimore, MD, NY, USA, 2004.
63. H. L. V. Trees, Detection, Estimation, and Modulation Theory: Radar-Sonar Signal Processing and Gaussian Signals in Noise. Melbourne, FL, USA: Krieger Publishing Co., Inc., 1992.
64. J. Liu, Y. Zhang, and F. Zhao, “Robust distributed node localization with error management,” in Proceedings of the 7th ACM International Symposium on Mobile Ad Hoc Networking and Computing, Florence, Italy.
65. Z. Yang and Y. Liu, “Quality of trilateration: Confidence based iterative localization,” in Distributed Computing Systems, 2008. ICDCS’08. The 28th International Conference on, Beijing, China, June 2008, pp. 446-453.
66. C. Savarese, J. M. Rabaey, and K. Langendoen, “Robust positioning algorithms for distributed ad-hoc wireless sensor networks,” in Proceedings of the General Track of the Annual Conference on USENIX Annual Technical Conference. Monterey, CA, USA, 2002.
67. E. Candes, J. Romberg, and T. Tao, “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,” IEEE Transactions on Information Theory, vol. 52, pp. 489-509, Feb. 2006.
68. E. Candes, J. Romberg, and T. Tao, “Stable signal recovery from incomplete and inaccurate information,” Communications on Pure and Applied Mathematics, vol. 59, no. 8, pp. 1207-1233, Aug. 2006.
69. E. Candes and T. Tao, “Near optimal signal recovery from random projections: Universal encoding strategies,” IEEE Transactions on Information Theory, vol. 52, no. 1, pp. 54065425, Dec. 2006.
70. D. Donoho, “Compressed sensing,” IEEE Trans. on Information Theory, vol. 52, no. 4, pp. 1289-1306, Apr. 2006.
71. H. Nyquist, “Certain topics in telegraph transmission theory,” IEEE Trans. of the American Institute of Electrical Engineers, vol. 47, no. 2, pp. 617-644, Apr. 1928.
72. C. Shannon, “Communication in the presence of noise,” Proc. Institute of Radio Engineers, vol. 37, no. 1, pp. 10-21, 1949.
73. B. K. Natarajan, “Sparse approximate solutions to linear systems,” SIAM Journal on Computing, vol. 24, pp. 227-234, 1995.
74. M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing. Springer Verlag, Berlin, Heidelberg, Germany, 2010.
75. J. Starck, F. Murtagh, and J. Fadili, Sparse Image and Signal Processing: Wavelets, Curvelets, Morphological Diversity. Cambridge University Press, Cambridge, UK, 2010.
76. L. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D: Nonlinear Phenomena, vol. 60, no. 1-4, pp. 259-268, 1992.
77. J. Starck, E. Candes, and D. Donoho, “The curvelet transform for image denoising,” IEEE Transactions on Image Processing, vol. 11, no. 6, pp. 670-684, June 2002.
78. B. A. Olshausen and D. J. Field, “Emergence of simple-cell receptive field properties by learning a sparse code for natural images,” Nature, vol. 381, no. 6583, pp. 607-609, 1996.
79. K. Engan, S. Aase, and J. Husoy, “Multi-frame compression: Theory and design,” Signal Processing, vol. 80, no. 10, pp. 2121-2140, Oct. 2000.
80. M. Aharon, M. Elad, and A. Bruckstein, “k-SVD: An algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Transactions on Signal Processing, vol. 54, no. 11, pp. 4311-4322, Nov. 2006.
81. M. Yuan and Y. Lin, “Model selection and estimation in regression with grouped variables,” Journal of the Royal Statistical Society: Series B, vol. 68, no. 1, pp. 49-67, Apr. 2006.
82. J. Chen and X. Huo, “Theoretical results on sparse representations of multiple-measurement vectors,” IEEE Transactions on Signal Processing, vol. 54, no. 12, pp. 4634-4643, Dec. 2006.
83. F. Bach, “Consistency of the group lasso and multiple kernel learning,” The Journal of Machine Learning Research, vol. 9, pp. 1179-1225, 2008.
84. D. Malioutov, M. Cetin, and A. Willsky, “A sparse signal reconstruction perspective for source localization with sensor arrays,” IEEE Transactions on Signal Processing, vol. 53, no. 8, pp. 3010-3022, Aug. 2005.
85. S. Cotter, B. Rao, K. Engan, and K. Kreutz-Delgado, “Sparse solutions to linear inverse problems with multiple measurement vectors,” IEEE Transactions on Signal Processing, vol. 53, no. 7, pp. 2477-2488, July 2005.
86. J. Meng, W. Yin, H. Li, E. Hossain, and Z. Han, “Collaborative spectrum sensing from sparse observations in cognitive radio networks,” IEEE Journal on Selected Topics on Communications, special issue on Advances in Cognitive Radio Networking and Communications, vol. 29, no. 2, pp. 327-337, Feb. 2011.
87. M. Fazel, “Matrix rank minimization with applications,” Department of Electrical Engineering, Stanford University, Doctoral Thesis, 2002.
88. E. J. Candès and B. Recht, “Exact low-rank matrix completion via convex optimization,” in Proc. of IEEE Annual Allerton Conference on Communication, Control, and Computing, Monticello, IL, USA, September 2008, pp. 806-812.
89. B. Recht, M. Fazel, and P. Parrilo, “Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization,” SIAM Review, vol. 52, no. 3, pp. 471-501,2010.
90. Z. Liu and L. Vandenberghe, “Interior-point method for nuclear norm approximation with application to system identification,” SIAM Journal on Matrix Analysis and Applications, vol. 31, no. 3, pp. 1235-1256, 2009.
91. A. So and Y. Ye, “Theory of semidefinite programming for sensor network localization,” Mathematical Programming, vol. 109, no. 2, pp. 367-384, 2007.
92. C. Tomasi and T. Kanade, “Shape and motion from image streams under orthography: A factorization method,” International Journal of Computer Vision, vol. 9, no. 2, pp. 137-154, 1992.
93. T. Morita and T. Kanade, “A sequential factorization method for recovering shape and motion from image streams,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 8, pp. 858-867, 1997.
94. D. Goldberg, D. Nichols, B. Oki, and D. Terry, “Using collaborative filtering to weave an information tapestry,” Communications of the ACM, vol. 35, no. 12, pp. 61-70, 1992.
95. E. Candes, X. Li, Y. Ma, and J. Wright, “Robust principal component analysis?” Journal of the ACM, vol. 58, no. 3, pp. 1-37, May 2011.
96. R. Baraniuk, V. Cevher, M. Duarte, and C. Hegde, “Model-based compressive sensing,” IEEE Transactions on Information Theory, vol. 56, no. 4, pp. 1982-2001, Apr. 2010.
97. Y. Eldar and M. Mishali, “Robust recovery of signals from a structured union of subspaces,” IEEE Transactions on Information Theory, vol. 55, no. 11, pp. 5302-5316, Nov. 2009.
98. Y. Lu and M. Do, “Sampling signals from a union of subspaces,” Signal Processing Magazine, IEEE, vol. 25, no. 2, pp. 41-47, Mar. 2008.
99. E. Candes and J. Romberg, “Sparsity and incoherence in compressive sampling,” Inverse Problems, vol. 23, no. 3, pp. 969-985, 2007.
100. P. Feng and Y. Bresler, “Spectrum-blind minimum-rate sampling and reconstruction of multiband signals,” IEEE, International Conference on Acoustics, Speech and Signal Processing, Atlanta, GA, USA, 1996.
101. M. Vetterli, P. Marziliano, and T. Blu, “Sampling signals with finite rate of innovation,” IEEE Trans. on Signal Processing, vol. 50, no. 6, pp. 1417-1428, June 2002.
102. E. Candes and T. Tao, “Decoding by linear programming,” IEEE Transactions on Information Theory, vol. 51, no. 12, pp. 4203-4215, Dec. 2005.
103. Y. Zhang, “Theory of compressive sensing via l1-minimization: A non-RIP analysis and extensions,” Rice University CAAM Technical Report TR08-11, 2008.
104. E. Candes and Y. Plan, “A probabilistic and RIPless theory of compressed sensing,” IEEE Transactions on Information Theory, vol. 57, no. 11, pp. 7235-7254, Nov. 2010.
105. D. Donoho and X. Huo, “Uncertainty principles and ideal atomic decompositions,” IEEE Transactions on Information Theory, vol. 47, no. 7, pp. 2845-2862, Nov. 2001.
106. R. Gribonval and M. Nielsen, “Sparse representations in unions of bases,” IEEE Transactions on Information Theory, vol. 49, no. 12, pp. 3320-3325, Dec. 2003.
107. 1-minimization: Go over or under?” Rice University CAAM Technical Report TR05-09, 2005.
108. A. Cohen, W. Dahmen, and R. A. DeVore, “Compressed sensing and best k-term approximation,” Journal of the American Mathematical Society, vol. 22, no. 1, pp. 211-231, Jan. 2009.
109. E. Candes, “The restricted isometry property and its implications for compressed sensing,” Comptes Rendus Mathematique, vol. 346, no. 9-10, pp. 589-592, 2008.
110. S. Foucart and M. Lai, “Sparsest solutions of underdetermined linear systems via lq- minimization for 0 < q < 1,” Applied and Computational Harmonic Analysis, vol. 26, no. 3, pp. 395-407, 2009.
111. 1-minimization,” Applied and Computational Harmonic Analysis, vol. 29, no. 1, pp. 97-103, July 2010.
112. T. Cai, L. Wang, and G. Xu, “Shifting inequality and recovery of sparse signals,” IEEE Transactions on Signal Processing, vol. 58, no. 3, pp. 1300-1308, Mar. 2010.
113. 2k,” Applied and Computational Harmonic Analysis, vol. 31, no. 3, pp. 460-468, 2011.
114. M. Davenport, “PhD thesis: Random observations on random observations: Sparse signal acquisition and processing,” Rice University, Houston, TX, USA, 2010.
115. R. Baraniuk, M. Davenport, R. Devore, and M. Wakin, “A simple proof of the restricted isometry property for random matrices,” Constructive Approximation, vol. 28, no. 3, pp. 253-263, 2007.
116. S. Mendelson, A. Pajor, and N. Tomczak-Jaegermann, “Uniform uncertainty principle for Bernoulli and subgaussian ensembles,” Constructive Approximation, vol. 28, no. 3, pp. 277-289, 2008.
117. H. Rauhut, “Compressive sensing and structured random matrices,” Theoretical Foundations and Numerical Methods for Sparse Recovery, vol. 9, pp. 1-92, 2010.
118. J. Bourgain, S. Dilworth, K. Ford, S. Konyagin, and D. Kutzarova, “Explicit constructions of RIP matrices and related problems,” Duke Mathematical Journal, vol. 159, no. 1, pp. 145-185, 2011.
119. J. Haupt, L. Applebaum, and R. Nowak, “On the restricted isometry of deterministically subsampled Fourier matrices,” IEEE Conference on Information Sciences and Systems (CISS), Princeton, NJ, USA, 2010.
120. P. Indyk, “Explicit constructions for compressed sensing of sparse signals,” SIAM Symposium on Discrete Algorithms (SODA), pp. 30-33, 2008.
121. S. Vavasis, “Derivation of compressive sensing theorems from the spherical section property,” [online] available: www.student.math.uwaterloo.ca/~co769/simplif.pdf.
122. B. S. Kashin, “Diameters of some finite-dimensional sets and classes of smooth functions,” Mathematics of the USSR-Izvestiya, vol. 11, p. 317, 1977.
123. A. Garnaev and E. D. Gluskin, “The widths of a euclidean ball,” Dokl. Akad. Nauk SSSR, vol. 277, no. 5, pp. 1048-1052, 1984.
124. J. Tropp and A. Gilbert, “Signal recovery from random measurements via orthogonal matching pursuit,” IEEE Transactions on Information Theory, vol. 53, no. 12, pp. 4655-4666, Dec. 2007.
125. D. Donoho, Y. Tsaig, I. Drori, and J.-C. Starck, “Sparse solution of underdetermined linear equations by stagewise orthogonal matching pursuit,” IEEE Transactions on Information Theory, vol. 58, no. 2, pp. 1094-1121, Feb. 2012.
126. D. Needell and R. Vershynin, “Signal recovery from incomplete and inaccurate measurements via regularized orthogonal matching pursuit,” IEEE Journal of Selected Topics in Signal Processing, vol. 4, no. 2, pp. 310-316, Apr. 2010.
127. W. Dai and O. Milenkovic, “Subspace pursuit for compressive sensing: Closing the gap between performance and complexity,” IEEE Transactions on Information Theory, vol. 55, no. 5, pp. 2230-2249, May 2009.
128. D. Needell and J. Tropp, “Cosamp: Iterative signal recovery from incomplete and inaccurate samples,” Communications of the ACM, vol. 53, no. 12, pp. 93-100, Dec. 2010.
129. T. Blumensath and M. Davies, “Iterative hard thresholding for compressed sensing,” Applied and Computational Harmonic Analysis, vol. 27, no. 3, pp. 265-274, 2009.
130. T. Blumensath and M. Davies, “Normalized iterative hard thresholding: Guaranteed stability and performance,” Selected Topics in Signal Processing, IEEE Journal of, vol. 4, no. 2, pp. 298-309, Apr. 2010.
131. D. Du and F. Hwang, Combinatorial Group Testing and Its Applications. World Scientific Pub. Co. Inc., 2000.
132. R. Berinde, A. Gilbert, P. Indyk, H. Karloff, and M. Strauss, “Combining geometry and combinatorics: A unified approach to sparse signal recovery,” 46th Annual Allerton Conference on Communication, Control and Computing, Monticello, IL, USA, Sept. 2008.
133. A. Gilbert and P. Indyk, “Sparse recovery using sparse matrices,” Proceedings of the IEEE, vol. 98, no. 6, pp. 937-947, June 2010.
134. A. Gilbert, M. Strauss, J. Tropp, and R. Vershynin, “One sketch for all: Fast algorithms for compressed sensing,” Proceedings 39th ACM Symp. Theory of Computing, San Diego, 2007.
135. A. Gilbert, Y. Li, E. Porat, and M. Strauss, “Approximate sparse recovery: Optimizing time and measurements,” Proceedings of the 42nd Symposium on Theory of Computing, Cambridge, MA, USA, 2010.
136. Y. Zhang, “When is missing data recoverable?” Rice University CAAM Technical Report TR06-15, 2006.
137. W. P. Ziemer, Weakly Differentiable Functions: Sobolev Spaces and Functions of Bounded Variation, ser. Graduate Texts in Mathematics. Springer, 1989.
138. S. Kullback, “The Kullback-Leibler distance,” The American Statistician, vol. 41, no. 4, pp. 340-341, 1987.
139. S. Kullback, Information Theory and Statistics. Dover Pubns, Mineola, N.Y., USA, 1997.
140. D. Hosmer and S. Lemeshow, Applied Logistic Regression. Wiley-Interscience, New York, NY, USA, 2000.
141. J. Duchi, S. Shalev-Shwartz, Y. Singer, and T. Chandra, “Efficient projections onto the l1-ball for learning in high dimensions,” Proceedings of the 25th, International Conference on Machine Learning, Helsinki, Finland, 2008.
142. 1,∞ regularization,” in Proceedings ofthe 26th Annual International Conference on Machine Learning, Montreal, Quebec, Canada, June 2009.
143. J. Liu and J. Ye, “Efficient euclidean projections in linear time,” in Proceedings ofthe 26th Annual International Conference on Machine Learning, Montreal, Quebec, Canada, June 2009.
144. E. van den Berg and M. Friedlander, “Probing the pareto frontier for basis pursuit solutions,” SIAM Journal on Scientific Computing, vol. 31, no. 2, pp. 890-912, 2008.
145. E. van den Berg, M. Schmidt, M. Friedlander, and K. Murphy, “Group sparsity via lineartime projection,” Technical Report TR-2008-09, University of British Columbia, 2008.
146. S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge, UK: Cambridge University Press, 2004.
147. D. P. Bertsekas, Nonlinear Programming, 2nd edn. Belmont, Massachusetts: Athena Scientific, 1999.
148. D. Donoho, “De-noising by soft-thresholding,” IEEE Transactions on Information Theory, vol. 41, no. 3, pp. 613-627, May 1995.
149. J. Cai, E. J. Candès, and Z. Shen, “A singular value thresholding algorithm for matrix completion,” Stanford University, Tech. Rep., October 2008, [online] available: http://arxiv.org/abs/0810.3286.
150. S. Ma, D. Goldfarb, and L. Chen, “Fixed point and Bregman iterative methods for matrix rank minimization,” Mathematical Programming Series A and B, vol. 128, no. 1-2, pp. 321-353, June 2009.
151. J. Cai and S. Osher, “Fast singular value thresholding without singular value decomposition,” UCLA CAM Report 10-24, 2010.
152. J. Darbon and M. Sigelle, “Image restoration with discrete constrained total variation, Part I: fast and exact optimization,” Journal of Mathematical Imaging and Vision, vol. 26, no. 3, pp. 261-276, 2006.
153. D. Goldfarb and W. Yin, “Parametric maximum flow algorithms for fast total variation minimization,” SIAM Journal on Scientific Computing, vol. 31, no. 5, pp. 3712-3743, 2009.
154. T. F. Chan, H. M. Zhou, and R. H. Chan, “Continuation method for total variation denoising problems,” Advanced Signal Processing Algorithms, vol. 2563, no. 1, pp. 314-325, 1995.
155. A. Chambolle, “An algorithm for total variation minimization and applications,” Journal of Mathematical Imaging and Vision, vol. 20, no. 1-2, pp. 89-97, 2004.
156. A. Chambolle, “Total variation minimization and a class of binary MRF models,” Energy Minimization Methods in Computer Vision and Pattern Recognition, Lecture Notes in Computer Science 3757, pp. 136-152, 2005.
157. B. Wohlberg and P. Rodriguez, “An iteratively reweighted norm algorithm for minimization of total variation functionals,” IEEE Signal Processing Letters, vol. 14, no. 12, pp. 948-951, December 2007.
158. Y. Wang, J. Yang, W. Yin, and Y. Zhang, “A new alternating minimization algorithm for total variation image reconstruction,” SIAM Journal on Imaging Sciences, vol. 1, no. 3, pp. 248-272, 2008.
159. T. Goldstein and S. Osher, “The split Bregman method for l1 regularized problems,” SIAM Journal on Imaging Sciences, vol. 2, no. 2, pp. 323-343, 2009.
160. M. Figueiredo and R. Nowak, “An EM algorithm for wavelet-based image restoration,” IEEE Transactions on Image Processing, vol. 12, no. 8, pp. 906-916, Aug. 2003.
161. C. De Mol and M. Defrise, “A note on wavelet-based inversion algorithms,” Contemporary Mathematics, vol. 313, pp. 85-96, 2002.
162. 1-unified variational framework for image restoration,” European Conference on Computer Vision, Slovansky Ostrov, Prague 1, Czech Republic, May 2004.
163. J. Douglas and H. H. Rachford, “On the numerical solution of the heat conduction problem in 2 and 3 space variables,” Transactions of the American Mathematical Society, vol. 82, no. 2, pp. 421-439, 1956.
164. D. H. Peaceman and H. H. Rachford, “The numerical solution of parabolic elliptic differential equations,” SIAM Journal on Applied Mathematics, vol. 3, no. 1, pp. 28-41, 1955.
165. D. Gabay and B. Mercier, “A dual algorithm for the solution of nonlinear variational problems via finite-element approximations,” Computers and Mathematics with Applications, vol. 2, pp. 17-40, 1976.
166. J. Eckstein and D. P. Bertsekas, “On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators,” Mathematical Programming, vol. 55, no. 3, pp. 293-318, June 1992.
167. M. Fortin and R. Glowinski, Augmented Lagrangian Methods. Amsterdam, The Netherlands: North-Holland, 1983.
168. P. L. Combettes and V. R. Wajs, “Signal recovery by proximal forward-backward splitting,” SIAM Journal on Multiscale Modeling and Simulation, vol. 4, no. 4, pp. 1168-1200, 2005.
169. R. Rockafellar, Convex Analysis. Princeton, NJ: Princeton University Press, 1970.
170. S. Ma, W. Yin, Y. Zhang, and A. Chakraborty, “An efficient algorithm for compressed MR imaging using total variation and wavelets,” IEEE International Conference on Computer Vision and Pattern Recognition (CVPR'08), Anchorage, AK, USA, June 2008.
171. R. Tibshirani, M. Saunders, S. Rosset, J. Zhu, and K. Knight, “Sparsity and smoothness via the fused lasso,” Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 67, no. 1, pp. 91-108, 2005.
172. Z.-Q. Luo and P. Tseng, “On the linear convergence of descent methods for convex essentially smooth minimization,” SIAM Journal on Control and Optimization, vol. 30, no. 2, pp. 408-425, 1990.
173. E. T. Hale, W. Yin, and Y. Zhang, “Fixed-point continuation for l1-minimization: Methodology and convergence,” SIAM Journal on Optimization, vol. 19, no. 3, pp. 1107-1130, 2008.
174. 2),” Soviet Mathematics Doklady, vol. 27, no. 2, pp. 372-376, 1983.
175. Y. Nesterov, “Gradient methods for minimizing composite objective function,” www.optimization-online.org, CORE Discussion Paper 2007/76, 2007.
176. P. Tseng, “On accelerated proximal gradient methods for convex-concave optimization,” submitted to SIAM Journal on Optimization, 2008.
177. A. Beck and M. Teboulle, “A fast iterative shrinkage-thresholding algorithm for linear inverse problems,” SIAM Journal on Imaging Sciences, vol. 2, no. 1, pp. 183-202, 2009.
178. S. Becker, J. Bobin, and E. Candès, “NESTA: A fast and accurate first-order method for sparse recovery,” SIAM Journal of Imaging Science, vol. 4, no. 1, pp. 1-39, 2011.
179. W. Deng, W. Yin, and Y. Zhang, “Group sparse optimization by alternating direction method,” Rice University CAAM Technical Report TR11-06, 2011.
180. M. R. Hestenes, “Multiplier and gradient methods,” Journal of Optimization Theory and Applications, vol. 4, no. 5, pp. 303-320, 1969.
181. M. J. D. Powell, “A method for nonlinear constraints in minimization problems,” in Optimization, R. Fletcher, Ed. New York: Academic Press, 1972, pp. 283-298.
182. R. Glowinski and P. Le Tallec, Augmented Lagrangian and Operator-Splitting Methods in Nonlinear Mechanics. Philadelphia, Pennsylvania: SIAM, 1989.
183. J. Eckstein, “Nonlinear proximal point algorithms using Bregman functions, with applications to convex programming,” Mathematics of Operations Research, vol. 18, no. 1, pp. 202-226, 1993.
184. L. Bregman, “The relaxation method of finding the common points of convex sets and its application to the solution of problems in convex programming,” USSR Computational Mathematics and Mathematical Physics, vol. 7, no. 3, pp. 200-217, 1967.
185. W. Yin and S. Osher, “Error forgetting of Bregman iteration,” Rice University CAAM Technical Report TR12-03. Submitted to Journal of Scientific Computing, Special issue in honor of Stanley Osher's 70th birthday, 2012.
186. S. Osher, M. Burger, D. Goldfarb, J. Xu, and W. Yin, “An iterative regularization method for total variation-based image restoration,” SIAM Journal on Multiscale Modeling and Simulation, vol. 4, no. 2, pp. 460-489, 2005.
187. 1-problems in compressive sensing,” Rice University CAAM Technical Report TR09-37, 2009.
188. K. J. Arrow, L. Hurwicz, and H. Uzawa, Studies in Nonlinear Programming. Stanford, CA: Stanford University Press, 1958.
189. W. Yin, S. Osher, D. Goldfarb, and J. Darbon, “Bregman iterative algorithms for l1-minimization with applications to compressed sensing,” SIAM Journal on Imaging Sciences, vol. 1, no. 1, pp. 143-168, 2008.
190. S. Osher, Y. Mao, B. Dong, and W. Yin, “Fast linearized Bregman iteration for compressive sensing and sparse denoising,” Communications in Mathematical Sciences, vol. 8, no. 1, pp. 93-111,2010.
191. 1-norm minimization,” Mathematics of Computation, vol. 78, no. 268, pp. 2127-2136, 2009.
192. M. Friedlander and P. Tseng, “Exact regularization of convex programs,” SIAM Journal on Optimization, vol. 18, no. 4, pp. 1326-1350, 2007.
193. W. Yin, “Analysis and generalizations of the linearized Bregman method,” SIAM Journal on Imaging Sciences, vol. 3, no. 4, pp. 856-877, 2010.
194. 1 and nuclear-norm models with a globally linearly convergent algorithm,” Rice University CAAM Technical Report TR12-02. Submitted to SIAM Journal on Imaging Sciences, 2012.
195. H. Zhang, J. Cai, L. Cheng, and J. Zhu, “Strongly convex programming for exact matrix completion and robust principal component analysis,” Preprint, 2012.
196. R. T. Rockafellar, “The multiplier method of Hestenes and Powell applied to convex programming,” Journal of Optimization Theory and Applications, vol. 12, no. 6, pp. 555-562, 1973.
197. B. He, L. Liao, D. Han, and H. Yang, “A new inexact alternating directions method for monotone variational inequalities,” Mathematical Programming, vol. 92, no. 1, pp. 103-118, 2002.
198. W. Deng and W. Yin, “On the global linear convergence of the alternating direction method of multipliers,” Rice University CAAM Technical Report TR12-14, 2012.
199. G. Chen and M. Teboulle, “Convergence analysis of a proximal-like minimization algorithm using Bregman functions,” SIAM Journal on Optimization, vol. 3, no. 3, pp. 538-543, 1993.
200. X. Zhang, M. Burger, and S. Osher, “A unified primal-dual algorithm framework based on Bregman iteration,” Journal of Scientific Computing, vol. 46, no. 1, pp. 20-46, 2011.
201. S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Foundations and Trends in Machine Learning, vol. 3, no. 1, pp. 1-122, 2010.
202. D. Bertsekas and J. Tsitsiklis, Parallel and Distributed Computation: Numerical Methods, 2nd edn. Nashua, NH, Athena Scientific, 1997.
203. D. Goldfarb, S. Ma, and K. Scheinberg, “Fast alternating linearization methods for minimizing the sum of two convex functions,” Mathematical Programming, March 1997.
204. B. He and X. Yuan, “On non-ergodic convergence rate of Douglas-Rachford alternating direction method of multipliers,” Optimization Online, 2012.
205. T. Goldstein, B. OòDonoghue, and S. Setzer, “Fast alternating direction optimization methods,” UCLA CAM Report 12-35, 2012.
206. J. Eckstein and D. Bertsekas, “An alternating direction method for linear programming,” Laboratory for Information and Decision Systems, MIT, [online] available: http://hdl.handle.net/1721.1/3197, 1990.
207. J. Ortega and W. Rheinboldt, Iterative Solution of Nonlinear Equations in Several Variables. Amsterdam, The Netherlands: Academic Press, 1970.
208. C. Hildreth, “A quadratic programming procedure,” Naval Research Logistics Quarterly, vol. 4, no. 1, pp. 79-85, 1957.
209. J. Warga, “Minimizing certain convex functions,” Journal of the Society for Industrial and Applied Mathematics, vol. 11, no. 3, pp. 588-593, 1963.
210. A. Auslender, Optimisation: Methodes Numeriques. Masson, 1976.
211. R. Sargent and D. Sebastian, “On the convergence of sequential minimization algorithms,” Journal of Optimization Theory and Applications, vol. 12, no. 6, pp. 567-575, 1973.
212. S. Han, “A successive projection method,” Mathematical Programming, vol. 40, no. 1, pp. 1-14, 1988.
213. O. Mangasarian and R. Leone, “Parallel successive overrelaxation methods for symmetric linear complementarity problems and linear programs,” Journal of Optimization Theory and Applications, vol. 54, no. 3, pp. 437-446, 1987.
214. P. Tseng, “Dual coordinate ascent methods for non-strictly convex minimization,” Mathematical Programming, vol. 59, no. 1, pp. 231-247, 1993.
215. P. Tseng, “Convergence of a block coordinate descent method for nondifferentiable minimization,” Journal of Optimization Theory and Applications, vol. 109, no. 3, pp. 475-494, 2001.
216. P. Tseng and S. Yun, “A coordinate gradient descent method for nonsmooth separable minimization,” Mathematical Programming, vol. 117, no. 1, pp. 387-423, 2009.
217. Y. Li and S. Osher, “Coordinate descent optimization for l1 minimization with applications to compressed sensing: A greedy algorithm,” UCLA CAM Report 09-17, 2009.
218. M. Razaviyayn, M. Hong, and Z. Luo, “A unified convergence analysis of coordinatewise successive minimization methods for nonsmooth optimization,” Report of University of Minnesota, Twin Cities, 2012.
219. X. Wei, Y. Yuan, and Q. Ling, “DOA estimation using a greedy block coordinate descent algorithm,” Report of University of Science and Techonolgy of China, 2012.
220. B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, “Least angle regression,” Annals of Statistics, vol. 32, no. 2, pp. 407-499, 2004.
221. M. Best, “An algorithm for the solution of the parametric quadratic programming problem,” CORR Report 82-84, University of Waterloo, 1982.
222. L. Ghaoui, V. Viallon, and T. Rabbani, “Safe feature elimination in sparse supervised learning,” Arxiv preprint ar Xiv:1009.4219, 2010.
223. R. Tibshirani, J. Bien, J. Friedman, T. Hastie, N. Simon, J. Taylor, and R. Tibshirani, “Strong rules for discarding predictors in lasso-type problems,” Journal of the Royal Statistical Society: Series B, vol. 57, no. 7, pp. 245-266, 2012.
224. S. Wright, R. Nowak, and M. Figueiredo, “Sparse reconstruction by separable approximation,” IEEE Transactions on Signal Processing, vol. 57, no. 7, pp. 2479-2493, 2009.
225. Z. Wen, W. Yin, H. Zhang, and D. Goldfarb, “On the convergence of an active set method for l1-minimization,” Optimization Methods and Software, vol. 27, no. 6, pp. 1-20, 2011.
226. J. Nocedal and S. J. Wright, Numerical Optimization. New York: Springer-Verlag, 1999.
227. E. Candes, M. Wakin, and S. Boyd, “Enhancing sparsity by reweighted 1 minimization,” Journal ofFourier Analysis and Applications, vol. 14, no. 5, pp. 877-905, 2008.
228. R. Chartrand and W. Yin, “Iteratively reweighted algorithms for compressive sensing,” ICASSP'08, Las Vegas, NV USA, March-April 2008.
229. I. Daubechies, R. DeVore, M. Fornasier, and C. Gunturk, “Iteratively reweighted least squares minimization for sparse recovery,” Communications on Pure and Applied Mathematics, vol. 63, no. 1, pp. 1-38, 2010.
230. K. Mohan and M. Fazel, “Iterative reweighted least squares for matrix rank minimization,” 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, USA, October 2010.
231. M. Fornasier, H. Rauhut, andR. Ward, “Low-rank matrix recovery via iteratively reweighted least squares minimization,” SIAM Journal on Optimization, vol. 21, no. 4, pp. 1614-1640, 2011.
232. M.-J. Lai, Y. Xu, and W. Yin, “On unconstrained nonconvex minimization for sparse vector and low-rank matrix recovery,” Submitted to SIAM Journal on Numerical Analysis, 2011.
233. R. Chartrand and V. Staneva, “Restricted isometry properties and nonconvex compressive sensing,” Inverse Problems, vol. 24, no. 3, pp. 1-14, 2008.
234. M. Lai and L. Liu, “The null space property for sparse recovery from multiple measurement vectors,” Applied and Computational Harmonic Analysis, vol. 30, no. 3, pp. 402-406, 2011.
235. q-minimization,” Arxiv preprint:1005.0267, 2011.
236. p minimization,” Stanford University Technical Report 2010, 2010.
237. S. Foucart, “Hard thresholding pursuit: An algorithm for compressive sensing,” SIAM Journal on Numerical Analysis, vol. 49, no. 6, pp. 2543-2563, 2011.
238. Y. Wang and W. Yin, “Sparse signal reconstruction via iterative support detection,” SIAM Journal on Imaging Sciences, vol. 3, no. 3, pp. 462-491, 2010.
239. K. Toh and S. Yun, “An accelerated proximal gradient algorithm for nuclear norm regularized linear least squares problems,” Pacific Journal of Optimization, vol. 6, no. 3, pp. 615-640, 2010.
240. J. Yang and X. Yuan, “Linearized augmented Lagrangian and alternating direction methods for nuclear norm minimization,” Optimization Online, 2011.
241. R. Keshavan, A. Montanari, and S. Oh, “Matrix completion from a few entries,” Information Theory, IEEE Transactions on, vol. 56, no. 6, pp. 2980-2998, 2010.
242. Z. Wen, W. Yin, and Y. Zhang, “Solving a low-rank factorization model for matrix completion by a nonlinear successive over-relaxation algorithm,” Rice University CAAM Report TR10-07, 2010.
243. [Online] available: http://en.wikipedia.org/wiki/Analog-to-digitaLconverter.
244. J. A. Tropp, J. N. Laska, M. F. Duarte, J. K. Romberg, and R. G. Baraniuk, “Beyond Nyquist: Efficient sampling of sparse bandlimited signals,” IEEE Transactions on Information Theory, vol. 56, no. 1, pp. 520-544, January 2010.
245. S. Kirolos, J. Laska, M. Wakin, M. Duarte, D. Baron, T. Ragheb, Y. Massoud, and R. Baraniuk, “Analog-to-information conversion via random demodulation,” in IEEE Dallas Circuits and Systems Workshop (DCAS), Dallas, TX, 2006.
246. J.N. Laska, S. Kirolos, M. F. Duarte, T. S. Ragheb, R. G. Baraniuk, and Y. Massoud, “Analog-to-information conversion via random demodulation,” in IEEE International Symposium on Circuits and Systems, ISCAS, New Orleans, LA, May 2007.
247. M. Mishali and Y. C. Eldar, “From theory to practice: Sub-Nyquist sampling of sparse wideband analog signals,” IEEE Journal of Selected Topics in Signal Processing, vol. 4, no. 2, pp. 375-391, April 2010.
248. M. Mishali and Y. C. Eldar, “Expected rip: Conditioning of the modulated wideband converter,” in 2009 IEEE Information Theory Workshop, Sicily, Italy, 2009.
249. M. Mishali, Y. C. Eldar, and J. A. Tropp, “Efficient sampling of sparse wideband analog signals.” IEEE 25th Convention of Electrical and Electronics in Israel, Israel, 2008.
250. M. Mishali, Y. C. Eldar, and A. Elron, “Xampling: Signal acquisition and processing in union of subspaces,” IEEE Transactions on Signal Processing, vol. 59, no. 10, pp. 4719-4734, Oct. 2011.
251. T. Michaeli and Y. C. Eldar, “Xampling at the rate of innovation,” IEEE Transactions on Signal Processing, vol. 60, no. 3, pp. 1121-1133, May 2012.
252. K. Gedalyahu and Y. Eldar, “Time-delay estimation from low-rate samples: A union of subspaces approach,” IEEE Transactions on Signal Processing, vol. 58, no. 6, pp. 3017-3031, June 2011.
253. E. Matusiak and Y. Eldar, “Sub-Nyquist sampling of short pulses,” IEEE Transactions on Signal Processing, vol. 60, no. 3, pp. 1134-1148, Mar. 2012.
254. M. Mishali and Y. C. Eldar, “Xampling: Compressed sensing of analog signals,” Book Chapter, Compressed Sensing Theory and Applications. Cambridge, UK: Cambridge University Press, 2012.
255. M. Mishali, Y. Eldar, O. Dounaevsky, and E. Shoshan, “Xampling: Analog to digital at sub-Nyquist rates,” IETCircuits Devices System, vol. 5, no. 1, pp. 8-20, 2011.
256. M. A. Davenport, P. T. Boufounos, M. B. Wakin, and R. G. Baraniuk, “Signal processing with compressive measurements,” IEEE J. Sel. Topics Signal Process., vol. 4, no. 2, pp. 445-460, 2010.
257. T. Ragheb, S. Kirolos, J. Laska, A. Gilbert, M. Strauss, R. Baraniuk, and Y. Massoud, “Implementation models for analog-to-information conversion via random sampling,” in Midwest Symposium on Circuits and Systems, Montreal, Canada, 2007.
258. J. Laska, S. Kirolos, Y. Massoud, R. Baraniuk, A. Gilbert, M. Iwen, andM. Strauss, “Random sampling for analog-to-information conversion of wideband signals,” in IEEE Dallas/CAS Workshop on Design, Applications, Integration and Software, Dallas, TX, 2006.
259. J. A. Tropp, M. B. Wakin, M. F. Duarte, D. Baron, and R. G. Baraniuk, “Random filters for compressive sampling and reconstruction,” in IEEE International Conference on Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings, Toulouse, France, May 2006.
260. J. Meng, J. Ahmadi-Shokouh, H. Li, E. J. Charlson, Z. Han, S. Noghanian, and E. Hossain, “Sampling rate reduction for 60 GHZ UWB communication using compressive sensing,” in Asilomar Conference on Signals, Systems & Computers, Pacific Grove, CA, 2009.
261. M. Mishali and Y. C. Eldar, “Blind multiband signal reconstruction: Compressed sensing for analog signals,” IEEE Transactions on Signal Processing, vol. 57, no. 3, pp. 993-1009, March 2009.
262. S. R. Schnelle, J. P. Slavinsky, P. T. Boufounos, M. A. Davenport, and R. G. Baraniuks, “A compressive phase-locked loop,” in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Kyoto, Japan, 2012.
263. M. Davenport, S. Schnelle, J. Slavinsky, R. Baraniuk, M. Wakin, and P. Boufounos, “A wideband compressive radio receiver,” in The 2010 Military Communications Conference, San Jose, CA, 2010.
264. M. A. Davenport, J. N. Laska, J. R. Treichler, and R. G. Baraniuk, “The pros and cons of compressive sensing for wideband signal acquisition: Noise folding vs. dynamic range,” IEEE Transactions on Signal Processing, vol. 60, no. 9, pp. 4628-4642, Sept. 2012.
265. J. Treichler, M. Davenport, J. Laska, and R. Baraniuk, “Dynamic range and compressive sensing acquisition receivers,” in 7th U.S./Australia Joint Workshop on Defense Applications of Signal Processing (DASP), Coolum, Australia, 2011.
266. M. Lexa, M. Davies, and J. Thompson, “Reconciling compressive sampling systems for spectrally-sparse continuous-time signals,” IEEE Transactions on Signal Processing, vol. 60, no. 1, pp. 151-171, Jan. 2012.
267. P. Maechler, C. Studer, D. Bellasi, A. Maleki, A. Burg, N. Felber, H. Kaeslin, and R. G. Baraniuk, “VLSI implementation of approximate message passing for signal restoration and compressive sensing,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 2, no. 3, pp. 1-11, Oct. 2012.
268. X. Chen, Z. Yu, S. Hoyos, B. M. Sadler, and J. Silva-Martinez, “A sub-Nyquist rate sampling receiver exploiting compressive sensing,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 58, no. 3, pp. 507-520, Mar. 2011.
269. M. A. Davenport and M. B. Wakin, “Compressive sensing of analog signals using discrete prolate spheroidal sequences,” Applied and Computational Harmonic Analysis, vol. 33, no. 3, pp. 438-472, Nov. 2012.
270. A. Goldsmith, Wireless Communications. Cambridge, UK: Cambridge University Press, 2005.
271. R. Baraniuk, “Compressive sensing,” IEEE Signal Processing Magazine, vol. 24, no. 4, pp. 118-121, July 2007.
272. W U. Bajwa, J. Haupt, A. M. Sayeed, and R. Nowak, “Compressed channel sensing: A new approach to estimating sparse multipath channels,” Proceedings of the IEEE, vol. 98, no. 6, pp. 1058-1076, June 2010.
273. J. L. Paredes, G. R. Arce, and Z. Wang, “Ultra-wideband compressed sensing channel estimation,” IEEE Journal on Selected Topics in Signal Processing, vol. 1, no. 3, pp. 383-395, Oct. 2007.
274. C. R. Berger, Z. Wang, Z. Huang, and S. Zhou, “Application of compressive sensing to sparse channel estimation,” IEEE Communications Magazine, vol. 48, no. 11, pp. 164-174, Nov. 2010.
275. P. Zhang, Z. Hu, R. C. Qiu, and B. M. Sadler, “Compressive sensing based ultra-wideband communication system,” in Proc. of IEEE International Conference on Communications (ICC), Kyoto, Japan, June 2009.
276. J. Romberg, “Multiple channel estimation using spectrally random probes,” in Proc. SPIE Wavelets XIII, San Diego, CA, USA, 2009.
277. W U. Bajwa, A. Sayeed, and R. Nowak, “Compressed sensing of wireless channels in time, frequency, and space,” in Proc. of 42nd Asilomar Conf. Signals, Systems, and Computers, Pacific Grove, CA, 2008.
278. J. Meng, Y. Li, N. Nguyen, W Yin, and Z. Han, “Compressive sensing based high resolution channel estimation for OFDM system,” IEEE Journal ofSelected Topics in Signal Processing, special issue on Robust Measures and Tests Using Sparse Data for Detection and Estimation, vol. 6, no. 1, pp. 15-25, Feb. 2012.
279. C. R. Berger, J. Gomes, and J. M. F. Moura, “Study of pilot designs for cyclic-prefix OFDM on time-varying and sparse underwater acoustic channels,” in Proc. of IEEE Oceans, Spain, June 2011.
280. J. Huang, C. R. Berger, S. Zhou, and P. Willett, “Iterative sparse channel estimation and decoding for underwater MIMO-OFDM,” EURASIP Journal on Advances in Signal Processing, vol. 2010, no. 460379, 2010.
281. S. Gleichman and Y. C. Eldar, “Blind compressed sensing,” IEEE Transactions on Information Theory, vol. 57, no. 10, pp. 6958-6975, Oct. 2011.
282. S. Gleichman and Y. C. Eldar, “Multichannel blind compressed sensing,” in IEEE Sensor Array and Multichannel Signal Processing Workshop, Hoboken, NJ, June 2010.
283. G. Mileounis, B. Babadi, N. Kalouptsidis, and V Tarokh, “An adaptive greedy algorithm with application to nonlinear communications,” IEEE Transactions on Signal Processing, vol. 58, no. 6, pp. 2998-3007, June 2010.
284. Y. C. Eldar, P. Kuppinger, and H. Bolcskei, “Block-sparse signals: Uncertainty relations and efficient recovery,” IEEE Trans. Sig. Process., vol. 58, no. 6, pp. 3042-3054, June 2010.
285. D. Eiwena, G. Tauböck, F. Hlawatsch, and H. G. Feichtinger, “Group sparsity methods for compressive channel estimation in doubly dispersive multicarrier systems,” in 2010 IEEE Eleventh International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Vienna, Austria, June 2010.
286. J. Haupt, W. U. Bajwa, G. Raz, and R. Nowak, “Toeplitz compressed sensing matrices with application to sparse channel estimation,” IEEE Transactions on Information Theory, vol. 56, no. 11, pp. 5862-5875, Nov. 2010.
287. G. Tauböck, F. Hlawatsch, D. Eiwen, and H. Rauhut, “Compressive estimation of doubly selective channels in multicarrier systems: Leakage effects and sparsity-ehancing processing,” IEEE Journal on Selected Topics in Signal Processing, vol. 4, no. 2, pp. 255-271, Apr. 2010.
288. G. Tauböck and F. Hlawatsch, “Compressed sensing based estimation of doubly selective channels using a sparsity-optimized basis expansion,” in Proceedings of the 16th European Signal Processing Conference (EUSIPCO 2008), Lousanne, Switzerland, August 2008.
289. W. U. Bajwa, A. Sayeed, and R. Nowak, “Learning sparse doubly-selective channels,” in in Proc. of 46th Allerton conf. Communication, Control, and Computing, Monticello, IL, USA, October 2008.
290. O. Edfors, M. Sandell, J. J. V de Beek, D. Landström, and F. Sjöberg, “An introduction to orthogonal frequency division multiplexing,” Luleå Sweden: Luleå Tekniska Universitet, Tech. Rep., 1996.
291. S. Takaoka and F. Adachi, “Pilot-assisted adaptive interpolation channel estimation for OFDM signal reception,” in Proc. of IEEE Vehicular Technology Conference (VTC), Milan, Italy, May 2004.
292. J. Byun and N. P. Natarajan, “Adaptive pilot utilization for OFDM channel estimation in a time varying channel,” in Proc. of IEEE Wireless and Microwave Technology Conference (WAMICON), Clearwater, FL, USA, April 2009.
293. W. U. Bajwa, J. D. Haupt, G. M. Raz, S. J. Wright, and R. D. Nowak, “Toeplitz-structured compressed sensing matrices,” in Proc. of IEEE/SP Workshop on Statistical Signal Processing (SSP), Madison, WI, USA, August 2007.
294. J. Romberg, “Compressive sensing by random convolution,” SIAM J. Imaging Sci., vol. 2, no. 4, pp. 1098-1128, 2009.
295. J. K. Romberg and R. Neelamani, “Sparse channel separation using random probes,” Inverse Problem, vol. 26, no. 11, pp. 1-25, Nov. 2010.
296. C. R. Berger, S. Zhou, P. Willett, B. Demissie, and J. Heckenbach, “Compressed sensing for OFDM/MIMO radar,” in Proc. of IEEE Annual Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA, October 2008.
297. C. R. Berger, S. Zhou, and P. Willett, “Signal extraction using compressed sensing for passive radar with OFDM signals,” in Proc. of IEEE International Conference on Information Fusion, Cologne Germany, June-July 2008.
298. G. Tauböck and F. Hlawatsch, “A compressed sensing technique for OFDM channel estimation in mobile environments: Exploiting channel sparsity for reducing pilots,” in Proc. of IEEE International Conferenc on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas, NV, USA, April 2008.
299. C. R. Berger, S. Zhou, W. Chen, and P. Willett, “Sparse channel estimation for OFDM: Over-complete dictionaries and super-resolution methods,” in Proc. of IEEE International Workshop on Signal Process. Advances in Wireless Communications (SPAWC), Perugia, Italy, June 2009.
300. M. Mohammadnia-Avval, A. Ghassemi, and L. Lampe, “Compressive sensing recovery of nonlinearly distorted OFDM signals,” in Proc. of IEEE International Conference on Communications (ICC), Kyoto, Japan, June 2011.
301. P. Schniter, “A message-passing receiver for BICM-OFDM over unknown clustered-sparse channels,” IEEE Journal on Selected Topics in Signal Processing, vol. 5, no. 8, pp. 1462-1474, Dec. 2011.
302. A. Gomaa and N. Al-Dhahir, “A sparsity-aware approach for NBI estimation in MIMO- OFDM,” Wireless Communication, IEEE Transactions on, vol. 10, no. 6, pp. 1854-1862, June 2011.
303. M. Ledoux, Ed., The Concentration of Measure Phenomenon. American Mathematical Society, 2001.
304. W Guo and W. Yin, “Edgecs: Edge guided compressive sensing reconstruction,” Rice University, Tech. Rep., 2009.
305. M. Stojanovic, “On the relationship between capacity and distance in an underwater acoustic communication channel,” in Proceedings of the First ACM International Workshop on UnderWater Networks (WUWNet), Los Angeles, CA, USA, Sept. 2006.
306. E. M. Sozer, M. Stojanovic, and J. G. Proakis, “Underwater acoustic networks,” IEEE Journal of Oceanic Engineering, vol. 25, no. 1, pp. 72-83, Jan. 2000.
307. J. Preisig, “Acoustic propagation considerations for underwater acoustic communications network development,” in Proceedings of the 1st ACM International Workshop on Underwater Networks, Los Angeles, CA, USA, Sept. 2006.
308. P. C. Etter, Underwater Acoustic Modeling and Simulation, 3rd edn. UK: Spon Press, Taylor & Francis Group, 2003.
309. R. J. Urick, Principles of Underwater Sound, 3rd edn. New York: McGraw-Hill, 1983.
310. R. J. Urick, “Technical guides — speed of sound in sea-water,” in the National Physical Laboratory, [online] available: http://www.npl.co.uk/acoustics/techguides/soundseawater/content.html.
311. E. Hossain, D. Niyato, and Z. Han, Dynamic Spectrum Access in Cognitive Radio Networks. Cambridge, UK: Cambridge University Press, 2009.
312. L. Lai, H. E. Gamal, H. Jiang, and H. V. Poor, “Cognitive medium access: Exploration, exploitation and competition,” IEEE Transactions on Mobile Computing, vol. 10, no. 2, pp. 239-253, Feb. 2011.
313. Q. Zhao and A. Swami, “A decision-theoretic framework for opportunistic spectrum access,” IEEE Wireless Communications Magazine, vol. 14, no. 4, pp. 14-20, Aug. 2007.
314. D. Chen, S. Yin, Q. Zhang, M. Liu, and S. Li, “Mining spectrum usage data: A large-scale spectrum measurement study,” in Proceedings of the 15th Annual International Conference on Mobile Computing and Networking, Beijing, China, September 2009.
315. J. Riihijärvi, P. Mähönen, M. Wellens, and M. Gordziel, “Characterization and modelling of spectrum for dynamic spectrum access with spatial statistics and random fields,” in Proc. of IEEE 19th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Cannes, France, 2008.
316. M. Wellens, J. Riihijarvi, M. Gordziel, and P. Mähönen, “Spatial statistics of spectrum usage: From measurements to spectrum models,” in Proc. of IEEE International Conference on Communications (ICC), Bresden, Germany, June 2009.
317. H. Li, Z. Han, and Z. Zhang, “Communication over random fields: A statistical framework for cognitive radio networks,” in Proc. of IEEE Globe Communication Conference (Globecom), Houston, TX, USA, December 2011.
318. M. Wellens, J. Riihijärvi, and P. Mähönen, “Spatial statistics and models of spectrum use,” Elsevier Computer Communications, vol. 32, no. 18, pp. 1998-2011, Aug. 2009.
319. H. Li, “Reconstructing geographical-spectral pattern in cognitive radio networks,” in Proc. of the Fifth International Conference on Cognitive Radio Oriented Wireless Networks Communications (CROWNCOM), Cannes, France, June 2010.
320. E. J. Candés and B. Recht, “Exact matrix completion via convex optimization,” Magazine Communications of the ACM, vol. 55, no. 6, pp. 111-119, June 2012.
321. A. Oka and L. Lampe, “Compressed sensing of Gauss-Markov random field with wireless sensor networks,” in Proc. of Sensor Array and Multichannel Signal Processing Workshop, 2008. SAM 2008. 5th IEEE, Vancouver, Canada, July 2008.
322. V. Cevher, M. F. Duarte, C. Hegde, and R. G. Baraniuk, “Sparse signal recovery using Markov random fields,” in Proc. ofthe Workshop on Neural Information Processing Systems, Vancouver, Canada, Dec. 2008.
323. R. Kindermann and J. L. Snell, Markov Random Fields and Their Applications. American Mathematical Society, 1980.
324. W. Yin, Z. Wen, S. Li, J. Meng, and Z. Han, “Dynamic compressive spectrum sensing for cognitive radio networks,” in Proc. of Conference on Information Sciences and Systems (CISS), Baltimore, MD, USA, March 2011.
325. S. Burer and R. Monteiro, “Local mimima and convergence in low-rank semidefinite programming,” Mathematical Programming, vol. 103, no. 3, pp. 427-444, 2005.
326. N. Kalouptsidis, G. Mileounis, B. Babadi, and V. Tarokh, “Adaptive algorithms for sparse nonlinear channel estimation,” in IEEE/SP 15th Workshop on Statistical Signal Processing, Cardiff, United Kingdom, Sept. 2009.
327. H. F. Harmuth, Transmission of Information by Orthogonal Functions. New York, NY, USA, Springer, 1969.
328. X. Shen, R. C. Q. M. Guizani, and T. Le-Ngoc, Ultra-Wideband Wireless Communications and Networks. Hoboken, NJ, USA, Wiley, 2006.
329. J. Zhang, P. Orlik, Z. Sahinoglu, A. F. Molisch, and P. Kinney, “UWB systems for wireless sensor networks,” Proceedings of IEEE, vol. 97, no. 2, pp. 313-331, Feb. 2009.
330. D. Yang, H. Li, G. D. Peterson, and A. Fathy, “Compressive sensing TDOA for UWB positioning systems,” in Proc. of IEEE Radio and Wireless Symposium (RWS), Santa Clara, CA, USA, January 2011.
331. P. Zhang, H. Zhen, R. C. Qiu, andB. M. Sadler, “A compressed sensing based ultra-wideband communication system,” in Proc. of IEEE International Conference on Communications (ICC), Dresden, Germany, June 2009.
332. D. Yang, H. Li, G. D. Peterson, and A. Fathy, “Compressed sensing based UWB receiver: Hardware compressing and FPGA reconstruction,” in Proc. of Conference on Information Sciences and Systems (CISS), Baltimore, MD, USA, March 2009.
333. D. Pozar, Microwave Engineering. Hoboken, NY, USA: Wiley, 1998.
334. Y. Zhu, J. D. Zuegel, J. R. Marciante, and H. Wu, “Downlink scheduling using compressed sensing a reconfigurable, multi-gigahertz pulse shaping circuit based on distributed transversal filters,” in Proc. of IEEE International Symposium on Circuits and Systems, Island of Kos, Greece, May 2006.
335. M. E. Tipping, “Sparse Bayesian learning and the relevance vector machine,” Journal of Machine Learning Research, vol. 1, pp. 211-244, 2001.
336. S. Ji, Y. Xue, andL. Carin, “Bayesian compressive sensing,” IEEE Trans. Signal Processing, vol. 56, no. 6, pp. 1094-1121, June 2008.
337. A. Oka andL. Lampe, “A compressed sensing receiver for bursty communication with UWB impulse radio,” in Proc. of IEEE International Conference on Ultra-Wideband (ICUWB), Vancouver, Canada, Sept. 2009.
338. Z. Sahinoglu, S. Gezici, and I. Guvenc, Ultra-wideband Positioning Systems: Theoretical Limits, Ranging Algorithms and Protocols. Cambridge, UK: Cambridge University Press, 2011.
339. C. Feng, W. S. A. Au, S. Valaee, and Z. Tan, “Compressive sensing based positioning using rss of wlan access points,” in Proc. of IEEE Conference on Computer Communications (Infocom), San Diego, CA, USA, March 2010.
340. D. Milioris, G. Tzagkarakis, P. Jacquet, and P. Tsakalides, “Indoor positioning in wireless lans using compressinve sensing signal-strength fingerprints,” in Proc. of EUSIPCO, Barcelona, Spain, August-September 2011.
341. X. Li, Q. Han, V. Chakravarthy, and Z. Wu, “Joint spectrum sensing and primary user localization for cognitive radio via compressed sensing,” in Proc. of IEEE Military Communication Conference (MILCOM), San Jose, CA, USA, October-November 2010.
342. L. Liu, Z. Han, Z. Wu, and L. Qian, “Collaborative compressive sensing based dynamic spectrum sensing and mobile primary user localization in cognitive radio networks,” in Proc. of IEEE Global Telecommunications Conference (Globecom), Houston, TX, USA, December 2011.
343. C. Berrou, A. Glavieux, and P. Thitimajshima, “Near Shannon limit error-correcting coding and decoding: Turbo codes,” in Proc. of IEEE International Conference on Communications (ICC), Geneva, Switzerland, May 1993.
344. A. J. Viterbi, CDMA: Principles of Spread Spectrum Communication. Addison-Wesley, Boston, MA, USA, 1995.
345. S. Verdu, Multiuser Detection. Cambridge University Press, Cambridge, UK, 1998.
346. D. L. Donoho, Y. Tsaig, I. Drori, and J. Starck, “Sparse solution of underdetermined systems of linear equations by stagewise orthogonal matching pursuit,” IEEE Trans. on Information Theory, vol. 58, no. 2, pp. 1094-1121, Feb. 2012.
347. D. Divsalar, M. K. Simon, and D. Raphaeli, “Improved parallel interference cancellation for CDMA,” IEEE Trans. on Communications, vol. 46, no. 2, pp. 258-268, Feb. 1998.
348. A. L. C. Hui and K. B. Letaief, “Successive interference cancellation for multiuser asynchronous DS/CDMA detectors in multipath fading links,” IEEE Trans. on Communications, vol. 46, no. 3, pp. 384-391, Mar. 1998.
349. A. K. Fletcher, S. Rangan, and V. K. Goyal, “On-off random access channels: A compressed sensing framework,” preprint, 2009.
350. M. J. Wainwright, Sharp thresholds for high-dimensional and noisy recovery of sparsity. Univ. of California, Berkeley, Dept. of Statistics, Tech. Rep., 2008.
351. H. Wang and C. Leng, “Unified lasso estimation via least square approximation,” Journal of American Statistical Association, vol. 102, no. 479, pp. 1039-1048, Sept. 2007.
352. X. Liu, E. K. P. Chong, and N. B. Shroff, “A framework for opportunistic scheduling in wireless networks,” Computer Networks, vol. 41, no. 4, pp. 451-474, Mar. 2003.
353. S. R. Bhaskaran, L. Davis, A. Grant, S. Hanly, and P. Tune, “Downlink scheduling using compressed sensing,” in Proc. of IEEE Workshop on Information Theory, Taormina, Sicily, Italy, December 2009.
354. F. Fazel, M. Fazel, and M. Stojanovic, “Random access compressed sensing for energy- efficient underwater sensor networks,” IEEE Journal on Selected Areas in Communications, vol. 29, no. 8, pp. 1660-1670, Aug. 2011.
355. M. S. B. K. S. Lee, S.Pattern and A. Ortega, “Spatially-locallized compressed sensing and rotuing in multi-hop sensor networks,” in Proc. of 3rd International Conference on Geosensor Networks (GSN), Oxford, UK, 2009.
356. G. Staple and K. Werbach, “The end of spectrum scarcity,” IEEE Spectrum Archive, vol. 41, no. 3, pp. 48-52, Mar. 2004.
357. H. Kim and K. G. Shin, “Efficient discovery of spectrum opportunities with MAC-layer sensing in cognitive radio networks,” IEEE Trans. on Mobile Computing, vol. 7, no. 5, pp. 533-545, May 2008.
358. H. Kim and K. G. Shin, “Longley-rice methodology for evaluating TV coverage and interference,” Office of Engineering and Technology (OET), Federal Communications Commision, Tech. Rep., July OET Bulletin NO. 69.
359. S. M. Mishra, A. Sahai, and R. Brodersen, “Cooperative sensing among cognitive radios,” in Proc. of IEEE International Conference on Communications (ICC), Istanbul, Turkey, June 2006.
360. A. Ghasemi and E. S. Sousa, “Collaborative spectrum sensing for opportunistic access in fading environments,” in Proc. of IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, Baltimore, MD, USA, November 2005.
361. A. Ghasemi and E. S. Sousa, “Opportunistic spectrum access in fading channels through collaborative sensing,” Journal of Communications (JCM), vol. 2, no. 2, pp. 71-82, Mar. 2007.
362. W. Saad, Z. Han, M. Debbah, A. Hjørungnes, and T. Başar, “Coalitional games for distributed collaborative spectrum sensing in cognitive radio networks,” in Proc. of IEEE Conference on Computer Communications (INFOCOM), Rio de Janeiro, Brazil, April 2009.
363. G. Ghurumuruhan and Y. Li, “Cooperative spectrum sensing in cognitive radio: Part I: Two user networks,” IEEE Transactions on Wireless Communications, vol. 6, no. 6, pp. 2204-2213, June 2007.
364. G. Ghurumuruhan and Y. Li, “Cooperative spectrum sensing in cognitive radio: Part II: Multiuser networks,” IEEE Transactions on Wireless Communications, vol. 6, no. 6, pp. 2214-2222, June 2007.
365. J. Unnikrishnanand V. V. Veeravalli, “Cooperative sensing for primary detection in cognitive radio,” IEEE Journal of Selected Topics in Signal Processing, vol. 2, no. 1, pp. 18-27, Feb. 2008.
366. S. Cui, Z. Quan, and A. Sayed, “Optimal linear cooperation for spectrum sensing in cognitive radio networks,” IEEE Journal of Selected Topics in Signal Processing, vol. 2, no. 1, pp. 28-40, Feb. 2008.
367. W. Zhang, C. Sun, and K. B. Letaief, “Cooperative spectrum sensing for cognitive radios under bandwidth constraints,” in Proc. of IEEE Wireless Communications and Networking Conference, Hong Kong, China, March 2007.
368. C. H. Lee and W. Wolf, “Energy efficient techniques for cooperative spectrum sensing in cognitive radios,” in Proc. of IEEE Consumer Communications and Networking Conference (CCNC), Las Vegas, NV, USA, January 2008.
369. J. Meng, H. Li, and Z. Han, “Sparse event detection in wireless sensor networks using compressive sensing,” in Proc. of IEEE Conference on Information Sciences and Systems (CISS), Baltimore, MD, USA, March 2009.
370. R. H. Keshavanand S. Oh, “Matrix completion from a few entries,” in Proc. of International Symposium on Information Theory (ISIT), Seoul, Korea, July 2009.
371. D. Goldfarb and S. Ma, “Convergence of fixed point continuation algorithms for matrix rank minimization,” Department of IEOR, Columbia University, Tech. Rep., June 2009, [online] available: http://arxiv.org/abs/0906.3499.
372. M. F. Duarte, S. Sarvotham, M. B. Wakin, D. Baron, and R. Baraniuk, “Joint sparsity models for distributed compressed sensing,” in Proc. of Workshop on Signal Processing with Adaptive Sparse Structured Representations, Rennes, France, November 2005.
373. J. Tropp, A. C. Gilbert, and M. J. Strauss, “Simultaneous sparse approximation via greedy pursuit,” in Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Philadelphia, PA, USA, March 2005.
374. M. Fornasier and H. Rauhut, “Recovery algorithms for vector-valued data with joint sparsity constraints,” SIAM Journal on Numerical Analysis, vol. 46, no. 2, pp. 577-613, March 2008.
375. C. La and M. N. Do, “Signal reconstruction using sparse tree representation,” in Proc. SPIE Conference on Wavelet Applications in Signal and Image Processing, San Diego, CA, USA, August 2005.
376. E. Visotsky, S. Kuffner, and R. Peterson, “On collaborative detection of TV transmissions in support of dynamic spectrum sensing,” in Proc. of IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, Baltimore, MD, USA, November 2005.
377. W. Zhang and K. B. Letaief, “Cooperative spectrum sensing with transmit and relay diversity in cognitive networks,” IEEE Transactions on Wireless Communications, vol. 7, no. 12, pp. 4761-4766, Dec. 2008.
378. L. S. Cardoso, M. Debbah, P. Bianchi, and J. Najim, “Cooperative spectrum sensing using random matrix theory,” in Proc. of IEEE International Symposium on Wireless Pervasive Computing (ISWPC), Santorini, Greece, May 2008.
379. B. Wang, K. J. R. Liu, and T. Clancy, “Evolutionary game framework for behavior dynamics in cooperative spectrum sensing,” in IEEE Global Communication Conference, New Orleans, LA, USA, December 2008.
380. C. Sun, W. Zhang, and K. B. Letaief, “Cluster-based cooperative spectrum sensing in cognitive radio systems,” in Proc. of IEEE International Conference on Communications (ICC), Glasgow, Scotland, June 2007.
381. L. Cao and H. Zheng, “Distributed rule-regulated spectrum sharing,” IEEE Journal on Selected Areas in Communications: Special Issue on Cognitive Radio: Theory and Applications, vol. 26, no. 1, pp. 130-145, Jan. 2008.
382. Y. Liang, Y. Zeng, E. Peh, and A. T. Hoang, “Sensing-throughput tradeoff for cognitive radio networks,” IEEE Trans. on Wireless Communications, vol. 7, no. 4, pp. 1326-1337, Apr. 2008.
383. X. Zhou, J. Ma, Y. Li, Y. H. Kwon, and A. C. K. Soong, “Cooperative spectrum sensing with transmit and relay diversity in cognitive networks,” IEEE Trans. on Communications, vol. 58, no. 2, pp. 463-466, Feb. 2010.
384. Z. Tian, “Compressed wideband sensing in cooperative cognitive radio networks,” in Proc. of IEEE Global Telecommunications Conference (GLOBECOM), New Orleans, LO, USA, December 2008.
385. Y. Wang, Z. Tian, and C. Feng, “A two-step compressed spectrum sensing scheme for wideband cognitive radios,” in IEEE Global Telecommunications Conference, Miami, FL, USA, December 2010.
386. S. Hong, “Multi-resolution bayesian compressive sensing for cognitive radio primary user detection,” in IEEE Global Telecommunications Conference, December 2010.
387. F. Zeng, C. Li, and Z. Tian, “Distributed compressive spectrum sensing in cooperative multihop cognitive networks,” IEEE Journal of Selected Topics in Signal Processing, vol. 5, no. 1, pp. 37-38, Feb. 2010.
388. Z. Tian, E. Blasch, W. Li, G. Chen, and X. Li, “Performance evaluation of distributed compressed wideband sensing for cognitive radio networks,” in 11th International Conference on Information Fusion, Cologne, Germany, June-July 2008.
389. Z. Tian, G. Leus, and V. Lottici, “Joint dynamic resource allocation and waveform adaptation for cognitive networks,” IEEE Journal on Selected Areas in Communications, vol. 29, no. 2, pp. 443-454, Feb. 2011.
390. 1 minimization,” IEEE Journal of Selected Topics in Signal Processing, vol. 4, no. 2, pp. 421-434, Apr. 2010.
391. M. S. Asif and J. Romberg, “Dynamic updating for sparse time varying signals,” in 43rd Annual Conference on Information Sciences and Systems (CISS), Baltimore, MD, USA, March 2009.
392. M.S. Asif and J. Romberg, “Streaming measurements in compressive sensing: l1 filtering,” in 42nd Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA, October 2008.
393. N. Vaswani, “Kalman filtered compressed sensing,” in International Conference on Image Processing, San Diego, CA, USA, October 2008.
394. X. Li, V Chakravarthy, and Z. Wu, “Joint spectrum sensing and primary user localization for cognitive radio via compressed sensing,” in IEEE MILCOM, San Jose, CA, USA, October 2010.
395. J. A. Bazerque and G. B. Giannakis, “Distributed spectrum sensing for cognitive radio networks by exploiting sparsity,” IEEE Transations on Signal Processing, vol. 58, no. 3, pp. 1847-1862, Mar. 2010.
396. P. Drineas, R. Kannan, and M. Mahoney, “Fast Monte Carlo algorithms for matrices II: Computing low-rank approximations to a matrix,” SIAM Journal on Computing, vol. 36, no. 1, pp. 158-183,2006.
397. J. Meng, W. Yin, H. Li, E. Hossain, and Z. Han, “Collaborative spectrum sensing from sparse observations using matrix completion for cognitive radio networks,” in Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Dallas, TX, USA, March 2010.
398. Mosek ApS Inc., “The Mosek optimization tools, ver 4.” 2006.
399. R. Bixby, Z. Gu, and E. Rothberg, “Gurobi optimization, www.gurobi.com,” 2009-2011.
400. W. Yin, “Gurobi Mex: A MATLAB interface for Gurobi,” 2009-2010, [online] available: http://www.caam.rice.edu/~ wy1 /gurobimex.
401. W. Yin, "Gurobi Mex: A MATLAB interface for Gurobi" 2009-2010, [online] available: http://www.caam.rice.edu/~wy1/gurobimex.
402. W. Yin, S. P. Morgan, J. Yang, and Y. Zhang, “Practical compressive sensing with Toeplitz and circulant matrices,” in Proc. of Visual Communications and Image Processing, Huang Shan, An Hui, China, July 2010.
403. NTIA, “FCC frequency allocation chart,” 2003, [online] available: http://www.ntia.doc.gov/osmhome/allochrt.pdf.
404. NTIA, “Spectrum policy task force report,” Federal Communication Commission, Tech. Rep. 135, November 2002, eT Docket.
405. E. van den Berg and M. Friedlander, “Joint-sparse recovery from multiple measurements,” Department of Computer Science, University of British Columbia, Technical Report TR- 2009-07, Apr. 2009.
406. D. Coppersmith and S. Winograd, “Matrix multiplication via arithmetic progressions,” Journal of Symbolic Computation, vol. 9, no. 3, pp. 251-280, March 1990.
407. B. A. Munk, Frequency Selective Surfaces: Theory and Design. Wiley, Hoboken, NJ, USA, 2000.
408. Y. Zhang, J. Yang, and W. Yin, YALL1: your algorithm for L1, 2009, [online] available: http://yall1.blogs.rice.edu/.