Type of Document Dissertation Author Shatila, Hazem Sarwat Author's Email Address email@example.com URN etd-04032012-045130 Title Adaptive Radio Resource Management in Cognitive Radio Communications using Fuzzy Reasoning Degree PhD Department Electrical and Computer Engineering Advisory Committee
Advisor Name Title Reed, Jeffrey Hugh Committee Chair Khedr, Mohamed E. Committee Co-Chair Beex, A. A. Louis Committee Member Shukla, Sandeep K. Committee Member Vullikanti, Anil Kumar S. Committee Member Keywords
- cognitive engine
- Wireless broadband
- cognitive radio
- opportunistic decision making.
- dynamic spectrum allocation
- fuzzy C-mean clustering (FCM)
- fuzzy logic
Date of Defense 2012-03-20 Availability unrestricted AbstractAs wireless technologies evolve, novel innovations and concepts are required to dynamically and automatically alter various radio parameters in accordance with the radio environment. These innovations open the door for cognitive radio (CR), a new concept in telecommunications. CR makes its decisions using an inference engine, which can learn and adapt to changes in radio conditions.
Fuzzy logic (FL) is the proposed decision-making algorithm for controlling the CR’s inference engine. Fuzzy logic is well-suited for vague environments in which incomplete and heterogeneous information is present. In our proposed approach, FL is used to alter various radio parameters according to experience gained from different environmental conditions. FL requires a set of decision-making rules, which can vary according to radio conditions, but anomalies rise among these rules, causing degradation in the CR’s performance. In such cases, the CR requires a method for eliminating such anomalies. In our model, we used a method based on the Dempster-Shafer (DS) theory of belief to accomplish this task. Through extensive simulation results and vast case studies, the use of the DS theory indeed improved the CR’s decision-making capability. Using FL and the DS theory of belief is considered a vital module in the automation of various radio parameters for coping with the dynamic wireless environment.
To demonstrate the FL inference engine, we propose a CR version of WiMAX, which we call CogMAX, to control different radio resources. Some of the physical parameters that can be altered for better results and performance are the physical layer parameters such as channel estimation technique, the number of subcarriers used for channel estimation, the modulation technique, and the code rate.
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