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Title:Learning-Based Interference Mitigation for Wireless Networks
Author(s):Chen, Chun-cheng
Subject(s):wireless networks
Abstract:Wireless networks have raised great attention in the past decades because they provide tether-free connectivity. Although much of the e ort 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 di erent aspects: (1) collision avoidance, (2) channel rate adaptation, and (3) spatial reuse.
Issue Date:2009-03
Genre:Technical Report
Type:Text
URI:http://hdl.handle.net/2142/11532
Other Identifier(s):UIUCDCS-R-2009-3047
Rights Information:You are granted permission for the non-commercial reproduction, distribution, display, and performance of this technical report in any format, BUT this permission is only for a period of 45 (forty-five) days from the most recent time that you verified that this technical report is still available from the University of Illinois at Urbana-Champaign Computer Science Department under terms that include this permission. All other rights are reserved by the author(s).
Date Available in IDEALS:2009-04-23


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