基于AdaBoost提升学习的次优中继选择安全传输方案

AdaBoost learning-based suboptimal relay selection scheme for secure transmission

针对无线通信协作技术中多跳中继和子信道分配等复杂应用场景下系统时效性差和复杂度大的问题,在级联中继系统中提出了一种利用AdaBoost算法集成学习选择次优中继的安全传输方案。将合法信道和窃听信道的信道CSI作为训练模型的输入,使系统安全容量达到一定值的中继节点索引作为输出,把级联中继系统的次优中继选择问题转化为一个多类分类问题,并用基于AdaBoost加权表决的支持向量机求解。级联中继系统的次优中继选择方案可分为生成数据集、集成模型训练和结果预测3个阶段。在模型训练阶段,绘制分类准确率和查准率-查全率曲线,对比集成学习较个体学习在准确率方面具有更佳的性能。最后,通过仿真AdaBoost算法分类的中继索引,验证了集成学习方法进行次优中继选择具有更高的准确率,能有效降低系统时延和复杂度,提高中继协作系统的安全性能。

Using AdaBoost algorithm of boosting learning to solve the suboptimal relays selection can reduce the realtime processing delay and computational complexity in cascaded relaying system,when wireless communication channels are in complex application scenarios such as multi-hop relays and sub-channel assignment.The channel state information of the legitimate channel and the eavesdropping channel is used as the input of the training model,and the index of the relay nodes with a certain value of the security capacity of the system is used as the output to transform the suboptimal relay selection problem of the cascaded relay system into a multiclass classification problem,which is solved by Support Vector Machines based on AdaBoost weighted voting.The suboptimal relay selection scheme for the cascaded relay system can be divided into three phases:generation of dataset,ensemble model training and result prediction.Finally,by plotting the classification accuracy and P-R curves,it is verified that the integrated learning model has higher accuracy for suboptimal relay selection and can improve the performance of relay collaboration.