基于量子遗传算法的认知无线电决策引擎研究

提出了基于量子遗传算法的认知无线电决策引擎，设计了待优化的多目标函数，利用量子遗传算法调整优化无线电参数，运用多载波系统对算法性能进行了仿真分析.实验结果表明该方法在收敛速度、收敛精度和算法稳定性上都明显优于经典遗传算法，在种群规模较小时仍然能获得很好性能，适合于实际实现.不同权重设置模式下仿真结果表明该方法能够在多个目标函数间进行权衡，参数调整结果与当前对目标函数的偏好一致.

A study of cognitive radio decision engine based on quantum genetic algorithm

One of the basic capabilities of cognitive radio is to adapt the radio parameters according to the changing environment and user needs. A cognitive radio decision engine based on quantum genetic algorithm is proposed, in which the radio parameters are adapted and optimized by quantum genetic algorithm. The multiple objective functions are designed and multi-carrier system is used for performance analysis. Experimental results show that the proposed method has better convergence, precision and stability than the classic genetic algorithm, and the good performance of the proposed method in small population size illustrates that it is suitable for hardware implementation. Simulation results under different weighting scenarios illustrate the trade-off between multiple objective functions and that the adapted parameter configuration is consistent with the weights of the objective functions.