基于遗传算法的D2D通信安全控制研究

D2D Communication Security Control based on Genetic Algorithm

针对Device-to-Device(D2D)通信作为LTE-Advanced中的新技术之一,带动了蜂窝网络的发展。D2D通信具有提高频谱利用率,增大蜂窝网络覆盖范围,减少基站工作负担等诸多作用,采用传统方法很难将其收敛到状态空间的最优解,进而出现控制性能不佳的情况。利用遗传算法在随机泛函的学习样本约束下,求得遗传算法的D2D通信安全控制最优解。采用位置矢量适应度更新方法进行D2D通信安全控制研究训练。研究结果表明:在随机离散的条件下,采用新该方法可有效降低由所述观测数据造成的错误,并且进一步让计算开销可以进一步降低,从而让D2D通信安全控制优化。

D2 D(Device-to-Device)communication,as one of the new technologies in LTE-Advanced,drives the development of cellular networks.D2 D communication has many functions such as improving spectrum utilization,increasing coverage of cellular networks,and reducing the workload of base stations.However,it is difficult to obtain an optimal solution of the state space by using the traditional communication method,and then the control performance is not good.By using genetic algorithm,the optimal solution of D2 D communication security control based on genetic algorithm is obtained under the learn-sample constraints of random functionals.The D2 D communication security control research training is done by using the position vector moderate update method.The research results indicate that under random discrete conditions,the new method can effectively reduce the errors caused by the observed data,and further the computational overhead,thus optimizing the D2 D communication security control.