Title page for ETD etd-12082006-141855

Type of Document Dissertation
Author Neel, James O'Daniell
Author's Email Address janeel@vt.edu
URN etd-12082006-141855
Title Analysis and Design of Cognitive Radio Networks and Distributed Radio Resource Management Algorithms
Degree PhD
Department Electrical and Computer Engineering
Advisory Committee
Advisor Name Title
Reed, Jeffrey Hugh Committee Chair
Buehrer, Richard Michael Committee Member
DaSilva, Luiz A. Committee Member
Giles, Robert H. Jr. Committee Member
MacKenzie, Allen B. Committee Member
  • Sensor Networks
  • 802.22
  • 802.11
  • Interference Reducing Network
  • Potential Games
  • Game Theory
  • SDR
  • Software Radio
  • Cognitive Radio
  • Distributed Radio Resource Management
  • Ad-hoc Network
  • Dynamic Frequency Selection
  • Power Control
Date of Defense 2006-09-06
Availability unrestricted
Cognitive radio is frequently touted as a platform for implementing dynamic distributed radio resource management algorithms. In the envisioned scenarios, radios react to measurements of the network state and change their operation according to some goal driven algorithm. Ideally this flexibility and reactivity yields tremendous gains in performance. However, when the adaptations of the radios also change the network state, an interactive decision process is spawned and once desirable algorithms can lead to catastrophic failures when deployed in a network.

This document presents techniques for modeling and analyzing the interactions of cognitive radio for the purpose of improving the design of cognitive radio and distributed radio resource management algorithms with particular interest towards characterizing the algorithms' steady-state, convergence, and stability properties. This is accomplished by combining traditional engineering and nonlinear programming analysis techniques with techniques from game to create a powerful model based approach that permits rapid characterization of a cognitive radio algorithm's properties. Insights gleaned from these models are used to establish novel design guidelines for cognitive radio design and powerful low-complexity cognitive radio algorithms.

This research led to the creation of a new model of cognitive radio network behavior, an extensive number of new results related to the convergence, stability, and identification of potential and supermodular games, numerous design guidelines, and several novel algorithms related to power control, dynamic frequency selection, interference avoidance, and network formation. It is believed that by applying the analysis techniques and the design guidelines presented in this document, any wireless engineer will be able to quickly develop cognitive radio and distributed radio resource management algorithms that will significantly improve spectral efficiency and network and device performance while removing the need for significant post-deployment site management.

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