Genetic algorithm application in optimizing transmission parameters on adaptive mechanism of cognitive radio

Abstract

Cognitive radio (CR) technology introduces a revolutionary in wireless communication network and it is capable to operate in a continuously varying radio frequency (RF) environment that depends on multiple parameters. CR has the capability to sense, learn the environment and adapt intelligently to the most appropriate way for providing the optimize service that suit to the user’s requirements. Recent researches show that Genetic algorithms (GAs) that rooted in biological inspired are viable implementation technique for CR engine to optimize transmission parameters in a given wireless environment. In this work, GA is applied in adaptive mechanism of CR to perform optimization on transmitter parameters for physical (PHY) layer. The objective of optimization is to obtained optimum set of transmission parameters in order to meet quality of service (QoS) that defined by user in term of minimum transmit power, minimum bit error rate (BER) and maximum throughput. Fitness functions are developed to evaluate the performance of the GA in relation to transmission parameters that characterized. The characterization involves deriving chromosome structure that consists of transmission parameters gene. Finally, a MATLAB® code is developed for simulating the GA operations to achieve optimum set of transmission parameters for optimal radio communications. Simulation results show fitness score for minimum transmit power is 0.927174 with optimum transmit power 0.1768 mW and modulation 64 QAM. While the fitness score for minimum BER is 0.852842 with optimum transmit power 0.74 mW and modulation 8 QAM. Lastly, the fitness score for maximum throughput is 0.952603 with optimum transmit power 0.7144 mW and modulation 64 QAM.