By Topic

Statistical analysis and predictive modeling of industrial wireless coexisting environments

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

3 Author(s)
Shrestha, G.M. ; Inst. Ind. IT, OWL Univ. of Appl. Sci., Lemgo, Germany ; Ahmad, K. ; Meier, U.

Typically, cognitive radio systems either sense the channel just before transmission or perform this task periodically in order to remain aware about the operational environment. However, a channel sensed as `free' can become busy during the transmission of the cognitive system resulting in harmful collisions and unnecessary interruptions in the secondary user data transmission. As a solution, predictive based approaches has been proposed and has shown promising results in simulated environments. However, modeling real-time, dynamic, coexisting environments demand investigation with real-time demonstrators. This paper investigates industrial coexisting environments and illustrates the prediction model selection and its parameter estimation criteria. Based on the investigation a real-time testbed is implemented using a CC2500 TRX and MSP430 μC based platform.

Published in:

Factory Communication Systems (WFCS), 2012 9th IEEE International Workshop on

Date of Conference:

21-24 May 2012