基于集成学习的全双工中继系统安全中继选择方案研究

Ensemble Learning-Based Relay Selection Scheme in Full-Duplex Relay System for Secure Transmission

当处于无线信道状态信息(Channel State Information,CSI)快速变化或者多跳中继等应用场景时,利用集成学习算法解决安全中继选择问题能减少实时处理时延及计算复杂度.将合法信道和窃听信道的CSI作为训练模型输入,使系统安全容量到达最大的中继节点索引作为输出,把全双工中继系统安全中继选择问题转化为一个多类分类问题,并利用随机森林(Random Forest,RF)算法求解.安全中继选择方案的实现分为数据准备、模型建立和结果预测三个阶段.在数据准备阶段,由于RF算法要求训练模型输入为离散值,给出了均匀量化和非均匀量化两种特征提取法将CSI转化为离散值.最后,通过仿真实验验证方案性能.

Using the ensemble learning algorithm to solve the secure relay selection problem can reduce the real-time processing delay and computational complexity,when the wireless systems that with fast-changing channel state information(CSI)or multi-hop relay situation.The secure relay selection problem of a full-duplex relay system is modeled as a multi-class classification problem,which is solved by the random forest(RF)algorithm.The CSI of the legitimate channel and eavesdropping channel is taken as the input of the training model,and the index of the relay node which can maximize the system security capacity is taken as the output of the training model.The implementation of the proposed scheme is divided into three phases:training data preparation,model building and result prediction.In the training data preparation phase,as the input features demanded by the RF algorithm are discrete values,feature extraction methods based on both uniform and non-uniform quantization algorithms are proposed to transform the CSI into finite discrete values.Finally,simulation experiments are conducted to verify the performance of the proposed scheme.