Performance Evaluation of Cognitive Radios

• Main Author: Kaminski, Nicholas James
• Other Authors: Electrical and Computer Engineering
• Format: Others
• Published: Virginia Tech 2014
• Subjects: Performance Evaluation; Cognitive Engine; Cognitive Radio
• Online Access: http://hdl.handle.net/10919/32092; http://scholar.lib.vt.edu/theses/available/etd-05012012-135634/

This thesis presents a performance evaluation system for cognitive radio. It considers per- formance as a complex, multi-dimensional function. Typically such a function would take some record of actions as an argument; however, a key contribution of this work is the addition of background information to the domain of the performance function. Including this information generalizes the performance function across many radios and applications, with the additional cost of complicating the domain. Thus the presented evaluation system organizes the domain information into sets. These sets are divided into two categories, one capturing necessary information that is external to the radio and on capturing necessary information that internal to the radio. These categories highlight the fact that neither the true actions nor the true performance is directly observable at the onset of evaluation. This arises because a cognitive radio can only express its actions in terms of the available knobs and meters, which together form the radioâ s language. Some understanding of this language and its limitations is required to fully understand the radioâ s expression of its actions. This parallelism of actions and performance suggests implementing the evaluation method as a composite form of the performance function. The composite performance function is made up of two sub-functions, one of which producing action information and one of which pro- ducing performance information. Specifically, the first sub-function is used to determine general measures of the actionsâ influence on performance; these are labeled Measures of Effectiveness. The second sub-function uses these Measures of Effectiveness to determine application specific performance values, called Measures of Performance. This work covers both these measures in detail. Each measure is determined as the result of a neural network based interpolation. This thesis also provides an examination of artificial neural networks in the scope of performance evaluation. Once these concepts are explored, a walk-through evaluation is presented. The four phases are the Setup Phase, the Logging Phase, the Train- ing Phase, and the Evaluation Phase. Each phase is structured to provide the information necessary to determine the final performance. These phases detail the process of evaluation and discuss the realization of concepts explored earlier. This work concludes with a compar- ative evaluation example that proves the worth of the presented approach. A full evaluation system is outlined by this thesis and the foundational details for the system are explored in detail. === Master of Science