Shatila, Hazem Sarwat2014-03-142014-03-142012-03-20etd-04032012-045130http://hdl.handle.net/10919/26618As 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.In CopyrightWiMAXcognitive engineWireless broadbandCognitive radio networksopportunistic decision making.dynamic spectrum allocationfuzzy C-mean clustering (FCM)fuzzy logicDissertationhttp://scholar.lib.vt.edu/theses/available/etd-04032012-045130/