Efficient spectrum optimization and mobility in cognitive radio based inter-vehicle communcation system

An efficient spectrum optimization and spectrum mobility schemes have been proposed to enhance the potentials of cognitive radio based inter vehicle communication (IVC) system. At vehicular speed performing spectrum optimization and then spectrum mobility is a challenging chore. Spectrum mobility is very frequent in IVC due to extreme mobile nature of vehicles and unpredictable RF channel. A proficient white space optimization technique is a basic requirement to perform in time spectrum decision and so in time spectrum mobility. Genetic Algorithm (GA) is considered as one of the best optimization techniques for white space optimization. But at vehicular speed simple genetic algorithm (GA) has been botched to perform white space optimization in real time. To anticipate this problem we have already proposed Memory enabled genetic algorithm (MEGA) in our previous work. In this research work we have further improved the convergence time of MEGA by manipulating the generation gap and mutation operator. Simulation results have proven that enhanced memory enabled genetic algorithm (EMEGA) is 0.522 ms faster than MEGA. In the next phase of research an efficient spectrum mobility scheme using human emotion (fear) has been proposed. Simulation results reveal that using our proposed spectrum decision and spectrum mobility schemes a more efficient cognitive radio based vehicular networks can be tailored.