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Relay selection scheme based on machine learning for enhancing the physical layer secrecy in cognitive radio networks
Telecommunication Systems ( IF 2.5 ) Pub Date : 2021-06-29 , DOI: 10.1007/s11235-021-00806-w
Hefdhallah Sakran

In this paper, a machine learning schemes are proposed to enhance the physical layer security in cognitive radio network in the presence of an eavesdropper and primary user. Firstly, we apply the support vector machine (SVM) based scheme as a machine learning based scheme to classify and select a relay node to assist the secondary user transmitter (SU-Tx) and maximize the secrecy rate and satisfy the interference constraint at PU. Then, we develop a deep neural network (DNN) based scheme to classify and select the best relay to assist the SU-Tx. Compared to the conventional optimal selection (OS) scheme, we prove that the proposed DNN-based scheme can achieve the same secrecy performance and the proposed scheme can substantially reduce the feedback overhead. Moreover, the proposed scheme based on SVM can achieve a good performance with small complexity compared to DNN and conventional OS. In contrast, the conventional OS requires knowledge of the eavesdropper's channel, which is impractical, whereas the proposed scheme based on DNN and SVM do not assume knowledge of the eavesdropper's channel so our proposed scheme is less complex with a small feedback overhead, e.g., at least 58% feedback overhead could be reduced.



中文翻译:

增强认知无线电网络物理层保密性的基于机器学习的中继选择方案

在本文中,提出了一种机器学习方案,以在存在窃听者和主要用户的情况下增强认知无线电网络中的物理层安全性。首先,我们应用基于支持向量机 (SVM) 的方案作为基于机器学习的方案来分类和选择中继节点来辅助次用户发射机 (SU-Tx) 并最大化保密率并满足 PU 的干扰约束。然后,我们开发了一个基于深度神经网络 (DNN) 的方案来分类和选择最佳继电器来辅助 SU-Tx。与传统的最优选择(OS)方案相比,我们证明了所提出的基于 DNN 的方案可以实现相同的保密性能,并且所提出的方案可以大大减少反馈开销。而且,与 DNN 和传统 OS 相比,基于 SVM 的建议方案可以以较小的复杂度实现良好的性能。相比之下,传统的操作系统需要窃听者信道的知识,这是不切实际的,而基于 DNN 和 SVM 的建议方案不假设窃听者信道的知识,因此我们提出的方案不太复杂,反馈开销很小,例如,至少可以减少 58% 的反馈开销。

更新日期:2021-06-29
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