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KNOWLEDGE MANAGEMENT TOOLBOX Machine Learning for Cognitive Radio Networks

Stavroulaki, V, Bantouna, A, Kritikou, Y, Tsagkaris, K, Demestichas, P, Blasco, P, Bader, F, Dohler, M, Denkovski, D, Atanasovski, V, Gavrilovska, L and Moessner, K (2012) KNOWLEDGE MANAGEMENT TOOLBOX Machine Learning for Cognitive Radio Networks IEEE VEHICULAR TECHNOLOGY MAGAZINE, 7 (2). 91 - 99. ISSN 1556-6072

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Abstract

Learning mechanisms are essential for the attainment of experience and knowledge in cognitive radio (CR) systems, exposed to high dynamics with often unpredictable states [1]. These mechanisms can be associated with user and device profiles, context, and decisions. The focus learning user preferences is the dynamic inference and estimation of current and future user preferences. The acquisition and learning of context information encompasses mechanisms for the system to perceive its current status and conditions in its present environment, as well as estimating (and forecasting) the capabilities of available network configurations. Finally, learning related to decisions addresses the building of knowledge with respect to the efficiency of solutions that can be applied to specific situations encountered. Based on knowledge obtained through learning, decision-making mechanisms can become faster, since the CR system can learn and immediately apply solutions that have been identified as being efficient in the past. Moreover, knowledge obtained through learning mechanisms may be shared among nodes of a system. Thus, more reliable and more optimal decisions can be made by exploiting knowledge obtained through learning mechanisms. © 2005-2012 IEEE.

Item Type: Article
Additional Information: © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords: Science & Technology, Technology, Engineering, Electrical & Electronic, Telecommunications, Transportation Science & Technology, Engineering, Transportation, SYSTEMS
Related URLs:
Divisions: Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Communication Systems Research
Depositing User: Symplectic Elements
Date Deposited: 17 Oct 2012 17:27
Last Modified: 23 Sep 2013 19:40
URI: http://epubs.surrey.ac.uk/id/eprint/725537

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