Sampling rate reduction for 60 GHz UWB communication using compressive sensing

Conference Record - Asilomar Conference on Signals, Systems and Computers

Volumn , Issue , 2009, Pages 1125-1129

  Meng, Jia ^a   Ahmadi Shokouh, Javad ^b   Li, Husheng ^c   Charlson, E Joe ^a   Han, Zhu ^a   Noghanian, Sima ^d   Hossain, Ekram ^b  

^a UNIVERSITY OF HOUSTON   (United States)

^b UNIVERSITY OF MANITOBA   (Canada)

^c University of Tennessee   (United States)

^d University of North Dakota   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

A/D CONVERTER; ANALOG TO DIGITAL CONVERTERS; BASIC IDEA; BER PERFORMANCE; BIT ERROR RATE PERFORMANCE; BPSK MODULATION; EMERGING TECHNOLOGIES; HIGH PROBABILITY; HIGH SPEED SAMPLING; IMPLEMENTATION COST; MULTI-PATH EFFECT; NYQUIST RATE; RAKE RECEPTION; RECEIVED SIGNALS; SAMPLING RATES; SENSING SCHEMES; SHORT-RANGE COMMUNICATION; SIMULATION RESULT; SINGLE ANTENNA; SPARSE SIGNALS; SYSTEM MODELS; TIME DOMAIN; ULTRA-WIDEBAND COMMUNICATIONS; UWB COMMUNICATION; WIRELESS TRANSMISSIONS;

ANALOG TO DIGITAL CONVERSION; BROADBAND NETWORKS; COMPUTER SIMULATION; COST REDUCTION; MODEL STRUCTURES; SIGNAL RECONSTRUCTION; SPEED; TELECOMMUNICATION SYSTEMS;

BIT ERROR RATE;

EID: 77953851800     PISSN: 10586393     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ACSSC.2009.5470043     Document Type: Conference Paper

References (18)

1. E. Candes, J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Trans. on Information Theory, 52(2) pp. 489-509, February 2006.
2. E. Candes and T. Tao, "Near optimal signal recovery from random projections: Universal encoding strategies?," IEEE Trans. on Information Theory, 52(12), pp. 5406-5425, December 2006.
3. D. Donoho, "Compressed sensing," IEEE Trans. on Information Theory, 52(4), pp. 1289-1306, April 2006.
4. W. U. Bajwa, J. D. Haupt, G. M. Raz, S. J. Wright, R. D. Nowak, "Toeplitz-structured compressed sensing matrices," in Proc. IEEE/SP 14th Workshop on Statistical Signal Processing, 2007.
5. Y. Mostofi and P. Sen, "Compressed mapping of communication signal strength," in Proc. IEEE Military Communications Conference, San Diego, CA, November 2008.
6. Z. Tian, "Compressed wideband sensing in cooperative cognitive radio networks," in Proc. IEEE Globecom, pp. 1-5, New Orleans, Dec. 2008.
7. P. Smulders, "Exploiting the 60 GHz band for local wireless multimedia access: Prospects and future directions," IEEE Commun. Mag., vol. 40, no. 1, pp. 140-147, Jan. 2002.
8. P. F. M. Smulders, C. F. Li, H. Yang, E. F. T. Martijn and M. H. A. J. Herben, "60 GHz indoor radio propagation - comparison of simulation and measurement results," in Proc. IEEE 11th Symp. on Commun. and Vehi. Tech., 2004.
9. B. Neekzad, K. Sayrafian-Pour, J. Perez, J. S. Baras, "Comparison of ray tracing simulations and millimeter wave channel sounding measurements," in Proc. IEEE Int. Symp. Personal, Indoor and Mobile Radio Commun., pp. 1-5, 2007.
10. J. Meng, H. Li, and Z. Han, "Sparse event detection in wireless sensor networks using compressive sensing," in Proc. 43rd Annual Conference on Information Sciences and Systems (CISS), 2009.
11. S. Boyd and L. Vandenberghe, Convex optimization, Cambridge University Press, 2006. (http://www.stanford.edu/∼boyd/cvxbook.html).
12. Z. Han and K. J. R. Liu. Resource Allocation for Wireless Networks: Basics, Techniques, and Applications. Cambridge University Press, Cambridge, UK, 2008.
13. M. E. Tipping, "Sparse Bayesian learning and the relevance vector machine," Journal of Machine Learning Research, vol. 1, p.p. 211-244, Sept. 2001.
14. M. E. Tipping and A. C. Faul, "Fast marginal likelihood maximisation for sparse Bayesian models," in Proc. 9th International Workshop on Artificial Intelligence and Statistics, Key West, FL, Jan 3-6.
15. Rice University, L1-Related Optimization Project, available from http://www.caam.rice.edu/̃optimization/L1/.
16. R. G. Baraniuk, "Compressive sensing", lecture notes, Rice university.
17. Compressive sensing resource, http://www.dsp.ece.rice.edu/cs/.
18. D. J. C. MacKay, "A practical Bayesian framework for backprop networks," Neural Computation, 4:448-472, 1992.