Title page for ETD etd-05012012-135634

Type of Document Master's Thesis
Author Kaminski, Nicholas James
Author's Email Address njkamin@vt.edu
URN etd-05012012-135634
Title Performance Evaluation of Cognitive Radios
Degree Master of Science
Department Electrical and Computer Engineering
Advisory Committee
Advisor Name Title
Bostian, Charles W. Committee Chair
Bose, Tamal Committee Member
MacKenzie, Allen B. Committee Member
  • Performance Evaluation
  • Cognitive Engine
  • Cognitive Radio
Date of Defense 2012-04-20
Availability unrestricted
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 radio’s language. Some understanding of this language

and its limitations is required to fully understand the radio’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 actions’ 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.

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