Optimization Of QoS Parameters In Cognitive Radio Using Adaptive Genetic Algorithm

Abstract

Genetic algorithm based optimization rely on explicit relationships between parameters, observations and criteria. GA based optimization when donein cognitive radio can provide a criteria to accommodate the secondary users in best possible space in the spectrum by interacting with the dynamic radio environment at real time. In this paper we have proposed adaptive genetic algorithm with adapting cro ssover and mutation parameters for the reasoning engine in cognitive radio to obtain the optimum radio configurations. This method ensure better controlling of the algorithm parameters and hence the increasing the performance. The main advantage of geneticalgorithm over other soft computing techniques is its multi – objective handling capability. We focus on spectrum management with a hypothesis that inputs are provided by either sensing information from the radio environment or the secondary user. Also thQoS requirements condition is also specified in the hypothesis. The cognitive radio will sense the radio frequency parameter from the environment and the reasoning engine in the cognitive radio will take the required decisions in order to provide new spe ctrum allocation as demanded by the user. The transmission parameters which can be taken into consideration are modulation method, bandwidth, data rate, symbol rate, power consumption etc. We simulated cognitive radio engine which is driven by genetic algo rithm to determine the optimal set of radio transmission parameters. We have fitness objectives to guide one system to an optimal state. These objectives are combined to one multi– objective fitness function using weighted sum approach so that each object ive can be represented by a rank which represents the importance of each objective. We have transmission parameters as decision variables and environmental parameters are used as inputs to the objective function. We have compared the proposed adaptive gene tic algorithm (AGA) with conventional genetic algorithm (CGA) with same set of conditions. MATLAB simulations were used to analyze the scenarios.