Google Scholar

##plugins.themes.academic_pro.article.sidebar##

Published
Jul 23, 2014
Download
PDF
Statistic
Read Counter : 40 Download : 39

Downloads

Download data is not yet available.

##plugins.themes.academic_pro.article.main##

Abstract

In this paper, we investigate the issue of cooperative node selection in MIMO communications for wireless ad hoc networks, where a source node is surrounded by multiple neighbors and all of them are equipped with a single antenna. Given constraints such as energy, delay and data rate, a source node dynamically chooses its cooperating nodes from its neighbors to form a virtual MIMO system with the destination node (which is assumed to have multiple antennas), as well as adaptively allocates the power level and adjusts the constellation size for each of the selected cooperative nodes. In order to optimize system performance, we jointly consider the optimization of all these parameters with given system constraints. We assume that the source node either has CSI, or has no CSI. Heuristic algorithms, such as maximal channel gain (MCG) and least channel correlation (LCC) algorithms are proposed in order to exploit available system information and to solve the constrained optimization problem.  

Keywords: Cooperative/virtual MIMO, ad hoc, correlation, QR decomposition, channel state information (CSI).

##plugins.themes.academic_pro.article.details##

How to Cite
Bani, H., & J.D, B. (2014). Cooperative MIMO Communication in Constrained Wireless Ad Hoc Networks. International Journal of Emerging Trends in Science and Technology, 1(05). Retrieved from http://igmpublication.org/ijetst.in/index.php/ijetst/article/view/229

References

1. H. Sui and J. R. Zeidler, “A robust coded MIMO FH-CDMA transceiver for mobile ad hoc networks,” IEEE J. Sel. Areas Commun., Sep. 2007.
2. H. Sui and J. R. Zeidler, “Information efficiency and transmission range optimization for coded MIMO FH-CDMA ad hoc networks in time varying environment,” IEEE Trans. Commun., vol. 57, Feb. 2009.
3. S. Cui, A. J. Goldsmith, and A. Bahai, “Energy-efficiency of MIMO and cooperative MIMO techniques in sensor networks,” IEEE J. Sel.Area Commun., vol. 22, no. 6, pp. 1089-1098, Aug. 2003.
4. M. Dohler, E. Lefranc, and H. Aghvami, “Space-time block codes for virtual antenna arrays,” in Proc. IEEE PIMRC, Sep. 2002.
5. S. K. Jayaweera, “Virtual MIMO-based cooperative communication for energy-constrained wireless sensor networks,” IEEE Trans. Wireless Commun., vol. 5, no. 5, pp. 984-989, May 2006.
6. J. N. Laneman and G. W. Wornell, “Distributed space-time-coded protocols for exploiting cooperative diversity in wireless networks,”
7. IEEE Trans. Inf. Theory, vol. 49, no. 10, pp. 2415-2425, Oct. 2003. Q. Zhou, H. Zhang, and H. Dai, “Adaptive spatial multiplexing techniques for distributed MIMO systems,” in Proc. CISS’04, Mar. 2004.
8. M. O. Hasna and M.-S. Alouini, “Optimal power allocation for relayed transmission over Rayleigh fading channels,” IEEE Trans. Wireless Commun,vol. 3, Nov. 2004.
9.J. Luo, R. S. Blum, L. Cimini, L. Greenstein, and A. Haimovich,“Power allocation in a transmit diversity system with mean channel gain information,”
10. IEEE Commun. Lett., vol. 9, no. 7, pp. 616-618, July 2005 M. Gudmundson ,“Correlation model for shadow fading in mobile radio systems,” Electron. Lett., vol. 27, no. 23, pp. 2145-2146, Nov. 1991.
11. Paulraj, R. Nabar, and D. Gore, Introduction to Space-Time Wireless Communications. Cambridge University Press, 2003.
12. John G. Proakis, Digital Communications, 4th edition. McGraw-Hill, 2000