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.
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references
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