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    Multiple Objective Fitness Functions for Cognitive Radio Adaptation

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    umi-ku-2533_1.pdf (887.0Kb)
    Issue Date
    2008-04-30
    Author
    Newman, Timothy R.
    Publisher
    University of Kansas
    Format
    142 pages
    Type
    Dissertation
    Degree Level
    PH.D.
    Discipline
    Electrical Engineering & Computer Science
    Rights
    This item is protected by copyright and unless otherwise specified the copyright of this thesis/dissertation is held by the author.
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    Abstract
    This thesis explores genetic algorithm and rule-based optimization techniques used by cognitive radios to make operating parameter decisions. Cognitive radios take advantage of intelligent control methods by using sensed information to determine the optimal set of transmission parameters for a given situation. We have chosen to explore and compare two control methods. A biologically-inspired genetic algorithm (GA) and a rule-based expert system are proposed, analyzed and tested using simulations. We define a common set of eight transmission parameters and six environment parameters used by cognitive radios, and develop a set of preliminary fitness functions that encompass the relationships between a small set of these input and output parameters. Five primary communication objectives are also defined and used in conjunction with the fitness functions to direct the cognitive radio to a solution. These fitness functions are used to implement the two cognitive control methods selected. The hardware resources needed to practically implement each technique are studied. It is observed, through simulations, that several trade offs exist between both the accuracy and speed of the final decision and the size of the parameter sets used to determine the decision. Sensitivity analysis is done on each parameter in order to determine the impact on the decision making process each parameter has on the cognitive engine. This analysis quantifies the usefulness of each parameter.
    URI
    http://hdl.handle.net/1808/4046
    Collections
    • Dissertations [4475]
    • Engineering Dissertations and Theses [1055]

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    KU Libraries
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    785-864-8983

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    Lawrence, KS 66045
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    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

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