Physical layer security in emerging wireless transmission systems




Bao, Tingnan

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Traditional cryptographic encryption techniques at higher layers require a certain form of information sharing between the transmitter and the legitimate user to achieve security. Besides, it also assumes that the eavesdropper has an insufficient computational capability to decrypt the ciphertext without the shared information. However, traditional cryptographic encryption techniques may be insufficient or even not suit- able in wireless communication systems. Physical layer security (PLS) can enhance the security of wireless communications by leveraging the physical nature of wireless transmission. Thus, in this thesis, we study the PLS performance in emerging wireless transmission systems. The thesis consists of two main parts. We first consider the PLS design and analysis for ground-based networks em- ploying random unitary beamforming (RUB) scheme at the transmitter. With RUB technique, the transmitter serves multiple users with pre-designed beamforming vectors, selected using limited channel state information (CSI). We study multiple-input single-output single-eavesdropper (MISOSE) transmission system, multi-user multiple-input multiple-output single-eavesdropper (MU-MIMOSE) transmission system, and massive multiple-input multiple-output multiple-eavesdropper (massive MI- MOME) transmission system. The closed-form expressions of ergodic secrecy rate and the secrecy outage probability (SOP) for these transmission scenarios are derived. Besides, the effect of artificial noise (AN) on secrecy performance of RUB-based transmission is also investigated. Numerical results are presented to illustrate the trade-off between performance and complexity of the resulting PLS design. We then investigate the PLS design and analysis for unmanned aerial vehicle (UAV)-based networks. We first study the secrecy performance of UAV-assisted relaying transmission systems in the presence of a single ground eavesdropper. We derive the closed-form expressions of ergodic secrecy rate and intercept probability. When multiple aerial and ground eavesdroppers are located in the UAV-assisted relaying transmission system, directional beamforming technique is applied to enhance the secrecy performance. Assuming the most general κ-μ shadowed fading channel, the SOP performance is obtained in the closed-form expression. Exploiting the derived expressions, we investigate the impact of different parameters on secrecy performance. Besides, we utilize a deep learning approach in UAV-based network analysis. Numerical results show that our proposed deep learning approach can predict secrecy performance with high accuracy and short running time.



physical layer security, deep learning, system performance analysis, UAV, beamforming