基于人工神经网络的联合中继和干扰选择策略研究

针对多中继通信网络,中继选择算法效率低及潜在窃听节点的安全威胁问题。提出一种基于人工神经网络的联合中继和干扰节点选择策略。首先,采用解码转发(decode-and-forward, DF)中继协议,构建存在窃听节点的多中继协作通信网络,结合协作干扰策略,推导得到系统安全中断概率的闭合表达式;然后,对神经网络进行训练,将相关节点的信道状态信息(channel state information, CSI)作为输入对模型进行训练,获得最优模型参数;最后,利用部分数据集验证模型,仿真结果表明对最优节点选择的正确率能够达到93%以上。与传统选择方案相比,所提方案实现复杂度降低且计算时间明显减少,并且有效改善了系统的安全性能。

Research on joint relay and jammer selection strategy based on artificial neural network

Aiming at the problems of low efficiency of relay selection algorithm and security threat of potential eavesdropping nodes in multi-relay communication network. A joint relay and jammer selection strategy based on artificial neural network is proposed. Firstly, the decode-and-forward ( DF ) relay protocol is adopted to construct the multi-relay cooperative communication network with eavesdropper, and the closed form expression of the security outage probability is derived by combination with the cooperative jamming strategy. Then, the neural network is trained, and the channel state information (CSI) of the relevant nodes is taken as the input data to train the model to obtain the optimal model parameters. Finally, some data sets are used to verify the model, and the simulation results show that the accuracy of optimal nodes selection can reach more than 93%. Compared with the traditional selection scheme based on exhaustive search and support vector machine, the proposed scheme reduces the implementation complexity and computation time significantly, and effectively improve the security performance of the system.