Cart (Loading....) | Create Account
Close category search window
 

Distributed Estimation of Channel Gains in 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
$31 $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)
Ramanan, S. ; Dept. of Electr. & Comput. Eng., Drexel Univ., Philadelphia, PA, USA ; Walsh, J.M.

We consider the problem of distributed channel estimation in a sensor network which employs a random sleep strategy to conserve energy. If the network nodes are randomly placed at unknown positions, some prior information about the channel gains can be obtained due to the path loss effect. When considered from a single node perspective this prior information is uninformative because there are on the order of links to estimate, while there are on the order of parameters to specify the unknown node positions. However, from a network wide channel estimation perspective, there are on the order of channel gains, but these are heavily influenced by only an order of position parameters. We show that expectation propagation (EP) can provide a distributed channel gain estimation algorithm which makes effective use of this prior information together with standard channel training methods. Exploiting prior information significantly improves estimate performance, as is evidenced by comparison with the prior-information-blind diffusion LMS algorithm. Provided simulation results affirm this conclusion even when shadowing is included and path loss exponents are mismatched or unknown. As communication and computation are both expensive at sensor nodes, we detail the message passing, computation, and memory requirements of both algorithms.

Published in:

Signal Processing, IEEE Transactions on  (Volume:58 ,  Issue: 6 )

Date of Publication:

June 2010

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.