Abstract
In adaptive beamforming, the beam produced by sensor network is cumulative result of all sensor nodes in that network. To use beamforming in sensor network, phase synchronization and delay synchronization are the parameters that need to be addressed. In this paper we propose an adaptive algorithm that helps to achieve phase synchronization in order to produce collaborative beamforming in the presence of noise and interference in sensor network. The results show that adaptive filter is computationally efficient, works in the presence of noise and operates in such an environment where the actual beam pattern is known at the receiver side. It has also been noted that the filter produces output which is very close to its optimum value. It has further been shown that when the number of sensors increases, the noise power at the receiver decreases and that the interference power depends upon the ratio between the number of sensors and the number of interference sources.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ananthasubramaniam, B., Madhow, U.: Virtual radar imaging for sensor networks. In: Proc. 3rd International Symposium on Information Processing in Sensor Networks (IPSN 2004), April 26–27, pp. 294–300 (2004)
Gomez, J., Campbell, A.T., Naghshineh, M., Bisdikian, C.: Power aware routing in wireless packet networks. In: Proc. 1999 IEEE International Workshop on Mobile Multimedia Communications (MOMUC 1999), November 15–17, pp. 380–383 (1999)
Yipeng Tang, M., Valenti: Coded transmit macro diversity: block space-time codes over distributed antennas. Vehicular Technology Conference 2, 1435–1438 (2001)
Barriac, G., Mudumbai, R., Madhow, U.: Distributed beamforming for information transfer in sensor networks. IEEE/ACM Transactions on Networking (TON) 14(SI), 2725–2748 (2006)
Tummala, M., Chee, C., Vincent, P.: Distributed beamforming in wireless sensor Networks. In: Thirty-Ninth Asilomar Conference, October 28 - November 1, pp. 793–797 (2005)
Sendonaris, A., Erkip, E., Aazhang, B.: User cooperation diversity. part i. system description  51, 1927–1938 (November 2003)
Laneman, J., Wornell, G.: Distributed space-time-coded protocols for exploiting cooperative diversity in wireless networks 49, 2415–2425 (October 2003)
Mudumbai, R., Barriac, G., Madhow, U.: Spread-spectrum techniques for distributed space-time communication in sensor networks. In: Proc. 38th Asilomar Conference on Signals, Systems and Computers (Asilomar 2004), Pacific Grove, CA, November 7–10 (2004)
Ochiai, H., Mitran, P., Poor, H.V., Tarokh, V.: Collaborative beamforming in ad hoc networks. IEEE Trans. Signal Processing 53(1053-1058), 4110–4124 (2005)
Mudumbai, R., Hespanha, J., Madhow, U., Barriac, G.: Scalable Feedback Control for Distributed Beamforming in Sensor Networks. In: Proc. 2005 IEEE International Symposium on Information Theory (ISIT 2005), Adelaide, Australia (September 2005)
Mc Whirter, J.G., Shepherd, T.J.: Systolic Array Processor for MVDR Beamforming. IEE Proceedings for Radar and Signal Processing 136(2), 75–80 (1989)
Mudumbai, R., Barriac, G., Madhow, U.: On the feasibility of distributed beamforming in wireless networks. IEEE Trans. on Wireless Commun. 6(5), 1754–1763 (2007)
Albowicz, J., Chen, A., Lixia, Z.: Recursive position estimation in sensor networks. In: Ninth International Conference on Network Protocols, November 11-14, pp. 35–41 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Naqvi, H., Sulayman, M., Riaz, M. (2009). Adaptive Beamforming in Wireless Sensor Network in the Presence of Interference Sources. In: Ślęzak, D., Kim, Th., Chang, A.CC., Vasilakos, T., Li, M., Sakurai, K. (eds) Communication and Networking. FGCN 2009. Communications in Computer and Information Science, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10844-0_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-10844-0_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10843-3
Online ISBN: 978-3-642-10844-0
eBook Packages: Computer ScienceComputer Science (R0)