Browse > Article

A Novel Multiple Access Scheme via Compressed Sensing with Random Data Traffic  

Mao, Rukun (Department of Electrical Engineering and Computer Science, University of Tennessee Knoxville)
Li, Husheng (Department of Electrical Engineering and Computer Science, University of Tennessee Knoxville)
Publication Information
Abstract
The problem of compressed sensing (CS) based multiple access is studied under the assumption of random data traffic. In many multiple access systems, i.e., wireless sensor networks (WSNs), data arrival is random due to the bursty data traffic for every transmitter. Following the recently developed CS methodology, the technique of compressing the transmitter identities into data transmissions is proposed, such that it is unnecessary for a transmitter to inform the base station its identity and its request to transmit. The proposed compressed multiple access scheme identifies transmitters and recovers data symbols jointly. Numerical simulations demonstrate that, compared with traditional multiple access approaches like carrier sense multiple access (CSMA), the proposed CS based scheme achieves better expectation and variance of packet delays when the traffic load is not too small.
Keywords
Bursty traffic; compressed sensing (CS); multiple access;
Citations & Related Records

Times Cited By Web Of Science : 0  (Related Records In Web of Science)
Times Cited By SCOPUS : 1
연도 인용수 순위
  • Reference
1 A. De Simone and S. Nanda, "Wireless data: Systems, standards, services," Wireless Netw., vol. 1, pp. 241–254, no. 3, Feb. 1996.
2 M. Davenport, M. Wakin, and R. Baraniuk, "Detection and estimation with compressive measurements," Rice ECE Department Tech. Rep. TREE0610, Nov. 2006.
3 J. C. Ye, "Compressed sensing shape estimation of star-shaped objects in Fourier image," IEEE Signal Process. Lett., vol. 14, iss. 10, pp. 750–753, Oct. 2007.
4 D. M. Malioutov, S. Sanghavi, and A. S. Willsky, "Compressed sensing with sequential observations," in Proc. ICASSP, Las Vegas, NV, Mar. 2008.
5 D. L. Donoho, M. Elad, and V. Temlyakov, "Stable recovery of sparse overcomplete representations in the presence of noise," IEEE Trans. Inf. Theory, vol. 52, no.1, pp.6–18, Jan. 2006.
6 P. Boufounos, M. Duarte, and R. Baraniuk, "Sparse signal reconstruction from noisy compressive measurements using cross validation," in Proc. SSP, 2007.
7 Y. Ling and D. Meng, "Study on improved truncated binary exponential back-off collision resolution algorithm," Compu. Sci. and Netw. Security, vol. 6, no. 11, Nov. 2006.
8 S. Chen, D. L. Donoho and M. Saunders, "Atomic decomposition by basis pursuit," SIAM Sci. Comput., vol. 20, pp. 33–61, Jan. 1999.
9 Y. C. Pati, R. Rezaiifar, and P. S. Krishnaprasad, "Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition," Signals, Sys. and Comput., vol. 1, pp. 40–44, Nov. 1993.
10 J. Tropp and A. Gilbert, "Signal recovery from random measurements via orthogonal matching pursuit," IEEE Trans. Inf. Theory, vol. 53, no. 12, pp. 4655–4666, Dec. 2007.
11 E. Candes, J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Trans. Inf. Theory, vol. 52, pp. 489–509, Feb. 2006.
12 D. L. Donoho, Y. Tsaig, I. Drori, and J. Starck, "Sparse solution of underdetermined linear equations by stagewise orthogonal matching pursuit," Tech. Rep., Mar. 2006.
13 E. J. Candes and T. Tao, "Near optimal signal recovery from random projections: Universal encoding strategies," IEEE Trans. Inf. Theory, vol. 52, no. 12, pp. 5406–5425, Dec. 2006.
14 W. Wang, M. Garofalakis, and K. Ramchandran, "Distributed sparse random matrix projections for refinable approximation," in Proc. IPSN, Cambridge, MA, Apr. 2007.
15 D. L. Donoho, "Compressed sensing," IEEE Trans. Inf. Theory, vol. 52, pp. 1289–1306, July 2006.
16 W. Lu and N. Vaswani, "Modified basis pursuit denoising for noisy compressed sensing with partially known support," in Proc. ICASSP, Dallas, TX, Mar. 2010.
17 D. L. Donoho, "For most large underdetermined systems of linear equations, the minimal l1-norm solution is also the sparsest solution," Commun. Pure Appl. Math, vol. 59, no. 6, pp.797–829, Mar. 2006.   DOI   ScienceOn
18 Y. Tsaig and D. L. Donoho, "Extensions of compressed sensing," Signal Process., vol. 86, pp. 533–548, July 2006.   DOI   ScienceOn
19 K. C. Chen, "Medium access control of wireless LANs for mobile computing," IEEE Netw., pp. 50–63, Sep. 1994.   DOI   ScienceOn
20 D. Yang, H. Li, G. D. Peterson, and A. Fathy, "UWB acquision in locationing systems: Compressed sensing and turbo signal reconstruction," in Proc. CISS, Baltimore, MD, Mar. 2009.