AbstractsComputer Science

This thesis explores genetics algorithm and PSO optimization techniques used by newlinecognitive radios to make operating parameter decisions. Cognitive radios take newlineadvantage of intelligent control schemes by using sensed information to determine the newlineoptimal set of transmission parameters for a given situation. This thesis has chosen to newlineexplore and compare three control methods. A biologically inspired genetic algorithm newline(Base-10 GA), a Particle Swarm Optimizations (PSO) and hybrid of Base-10 GA and newlinePSO are proposed, analyzed and tested using simulations. This work defines a newlinecommon set of six parameters used by cognitive radios, and develops a set of fitness newlinefunctions that include the relationships between a small set of these input and output newlineparameters. Four key communication objectives are also defined and used in newlinecombination with the fitness functions to direct the cognitive radio to a solution. It is newlineobserved, through simulations, that several trade offs exist between both the accuracy newlineand speed of the final decision and the size of the parameter sets used to determine the newlinedecision. This thesis also explores the Spectrum mobility by use of DNA-inspired newlinealgorithm and security aspects of cognitive radio, which is most challenging issue in newlineresearch of cognitive radio paradigm. DNA-inspired algorithm deals with a sequence newlinealignment of two DNA sequences that realized the goal given by Hykins. For the newlinesecurity in cognitive radio, a Fingerprint-based Defense against Primary User newlineEmulation Attacks has been explored using SIMULINK modal.%%%