International Journal of Communications, Network and System Sciences

Volume 6, Issue 11 (November 2013)

ISSN Print: 1913-3715   ISSN Online: 1913-3723

Google-based Impact Factor: 0.84  Citations  h5-index & Ranking

PSO for CWSN Using Adaptive Channel Estimation

HTML  XML Download Download as PDF (Size: 398KB)  PP. 472-477  
DOI: 10.4236/ijcns.2013.611049    3,853 Downloads   5,414 Views  


Wireless Sensor Network (WSN) is used in various applications. A main performance factor for WSN is the battery life that depends on energy consumption on the sensor. To reduce the energy consumption, an energy efficient transmission technique is required. Cluster Wireless Sensor Network (CWSN) groups the sensors that have the best channel condition and form a MIMO system. This leads to enhancing the transmission and hence reducing energy consumed by the sensor. In CWSN systems multiple signals are combined at the transmitter and transmitted by using multiple antennas according to channel condition. CWSN requires a good estimation of the Channel State Information (CSI) to implement a powerful and efficient system. Channel Estimation technique should be used to better form the CWSN and make use of the MIMO features. Adaptive Channel Estimation (ACE) is used to enhance the BER performance of the CWSN by utilizing the retransmission feature devised in this paper and feeding the CSI obtained to further enhance the clustering algorithm. We use Particle Swarm Optimization (PSO) algorithm to find the optimal cluster members according to a fitness function that derived from the channel condition. Too many calculations and operations are required in exhaustive search algorithms to form the optimal cluster arrangement. It shows that optimal cluster formation can be implemented fast and efficiently by using the PSO.

Share and Cite:

J. Rahhal, "PSO for CWSN Using Adaptive Channel Estimation," International Journal of Communications, Network and System Sciences, Vol. 6 No. 11, 2013, pp. 472-477. doi: 10.4236/ijcns.2013.611049.

Cited by

No relevant information.

Copyright © 2021 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.