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Enhancing cognitive radio systems with robust reasoning
Author(s) -
Adamopoulou Evgenia,
Demestichas Konstantinos,
Demestichas Panagiotis,
Theologou Michael
Publication year - 2008
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.898
Subject(s) - computer science , control reconfiguration , cognitive radio , process (computing) , adaptation (eye) , transmission (telecommunications) , artificial intelligence , bayesian network , machine learning , distributed computing , telecommunications , wireless , embedded system , physics , optics , operating system
Cognitive radio systems dynamically reconfigure the algorithms and parameters they use, in order to adapt to the changing environment conditions. However, reaching proper reconfiguration decisions presupposes a way of knowing, with high enough assurance, the capabilities of the alternate configurations, especially in terms of achievable transmission capacity and coverage. The present paper addresses this problem, firstly, by specifying a complete process for extracting estimations of the capabilities of candidate configurations, in terms of transmission capacity and coverage, and, secondly, by enhancing these estimations with the employment of a machine learning technique. The technique is based on the use of Bayesian Networks, in conjunction with aneffective learning and adaptation strategy, and aims at extracting and exploiting knowledge and experience, in order to reach robust (i.e. stable and reliable) estimations of the configurations' capabilities. Comprehensive results of the proposed method are presented, in order to validate its functionality. Copyright © 2007 John Wiley & Sons, Ltd.