SRI - Center for Uncertainty Quantification
in Computational Science & Engineering
Home
About
People
Faculty
Visiting Professors
Consultants
Research Scientists
Postdoctoral Fellows
Students
Visiting Students
Staff
Member of the Board
Previous Members
Research
Research Projects
Posters
Publications
Books
Book Chapters
Conference Proceedings
Manuscripts
Refereed Journals
Technical Reports
Events
Calendar
Gallery
KAUST UQ School 2016
Zavala's Seminar and Short Course
Grossmann’s Seminars and Short Course
UQ Annual Workshop 2016
UQ Annual Workshop 2015
Spatial Statistics Workshop 2014
UQ Annual Workshop 2014
UQ Annual Workshop 2013
News
Courses
Spring 2016
Summer 2015
Fall 2015
Seminars
Join Us
Links
Home
>
Publications
>
Conference Proceedings
>
Mean field games for cognitive radio networks
Publications
Mean field games for cognitive radio networks
Bibliography:
Bibliography
H. Tembine, R. Tempone, and P. Vilanova.
Mean field games for cognitive radio networks
. In American Control Conference (ACC), 2012, pages 6388 -6393, 2012.
Authors:
H. Tembine, R. Tempone, and P. Vilanova
Keywords:
Mean field games, cognitive radio networks.
Year:
2012
Abstract:
In this paper we study mobility effect and power saving in cognitive radio networks using mean field games. We consider two types of users: primary and secondary users. When active, each secondary transmitter-receiver uses carrier sensing and is subject to long-term energy constraint. We formulate the interaction between primary user and large number of secondary users as an hierarchical mean field game. In contrast to the classical large-scale approaches based on stochastic geometry, percolation theory and large random matrices, the proposed mean field framework allows one to describe the evolution of the density distribution and the associated performance metrics using coupled partial differential equations. We provide explicit formulas and algorithmic power management for both primary and secondary users. A complete characterization of the optimal distribution of energy and probability of success is given.
ISSN:
2012
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6314643
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6314643
No
Site Map
|
Privacy Policy
|
Terms of Use
|
Team Site
©
2021
King Abdullah University of Science and Technology,
All rights reserved.
SRI - Center for Uncertainty Quantification
in Computational Science & Engineering
http://
http://