Learning-Based Interference Mitigation for Wireless Networks
Chen, Chun-cheng
This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.
Permalink
https://hdl.handle.net/2142/72049
Description
Title
Learning-Based Interference Mitigation for Wireless Networks
Author(s)
Chen, Chun-cheng
Issue Date
2009
Doctoral Committee Chair(s)
Vaidya, Nitin H.
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Computer Science
Abstract
Wireless networks have raised great attention in the past decades because they provide tether-free connectivity. Although much of the effort in wireless network research has been spent on reducing the interference among the communication nodes, the problem remains open. In this dissertation, we propose a learning-based approach to alleviate wireless interference. The principle of the learning-based approach is based on the observation that although wireless networks are usually complex and dynamic, information can still be extracted from the data measured in the past. By learning from what was observed in the past we can select the desired operational parameters, react intelligently, and achieve substantial performance gain. In particular, we, show that interference mitigation can be achieved in three different aspects: (1) collision avoidance, (2) channel rate adaptation, and (3) spatial reuse.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
