By Topic

Channel Selection in Virtual MIMO Wireless Sensor Networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Jing Liang ; Dept. of Electr. Eng., Univ. of Texas, Arlington, TX ; Qilian Liang

In this paper, we present two practical algorithms for selecting a subset of channels in virtual multiple-input-multiple-output (MIMO) wireless sensor networks (WSNs) to balance the MIMO advantage consumption of sensor cooperation. If intracluster node-to-node multihop needs be taken into account, the maximum spanning tree searching (MASTS) algorithm, with respect to the cross-layer design, always provides a path connecting all sensors. When the WSN is organized in a manner of cluster-to-cluster multihop, the singular-value decomposition-QR with threshold (SVD-QR-T) approach selects the best subset of transmitters while keeping all receivers active. The threshold is adaptive by means of fuzzy c-means (FCM). These two approaches are compared by simulation against the case without channel selection in terms of capacity, bit error rate (BER), and multiplexing gain with water filling or equal transmission power allocation. Despite less multiplexing gain, when water filling is applied, MASTS achieves higher capacity and lower BER than virtual MIMO without channel selection at moderate-to-high signal-to-noise ratio (SNR), whereas SVD-QR-T by FCM provides the lowest BER at high SNR; in the case of no water filling and equal transmission power allocation, MASTS still offers the highest capacity at moderate-to-high SNR, but SVD-QR-T by FCM achieves the lowest BER. Both algorithms provide satisfying performances with reduced resource consumption.

Published in:

Vehicular Technology, IEEE Transactions on  (Volume:58 ,  Issue: 5 )