Physical layer security (PLS) is promising for secure short-packet transmissions in ultra-reliable and low-latency communications. The bottlenecks of applying PLS in practice include 1) lack of accurate channel state information (CSI) of both the intended user and the eavesdropper; 2) high computational complexity for solving optimization problems. To address the first issue, we compare the secure transmission rates of short packets in different scenarios (i.e., with/without eavesdropper’s instantaneous CSI and with/without channel estimation errors) and derive the closed-form optimal power control policy in a special case. To find numerical solutions in general cases, we apply an unsupervised deep learning method, which has low complexity after the training stage. Through numerical results, we obtain the following three key findings: 1) The learning-based power control policy approaches the closed-form optimal policy in the special case and outperforms two existing power control policies in general cases. 2) Knowing the instantaneous CSI of the eavesdropper only provides a marginal gain of the secure data rate in the high signal-to-noise ratio regime. 3) In the presence of channel estimation errors, the learning-based policy trained by the estimated channels can guarantee the average transmit power constraint, while the closed-form policy cannot.
Physical layer security (PLS) is promising for secure short-packet transmissions in ultra-reliable and low-latency communications. The bottlenecks of applying PLS in practice include 1) lack of accurate channel state information (CSI) of both the intended user and the eavesdropper; 2) high computational complexity for solving optimization problems. To address the first issue, we compare the secure transmission rates of short packets in different scenarios (i.e., with/without eavesdropper’s instantaneous CSI and with/without channel estimation errors) and derive the closed-form optimal power control policy in a special case. To find numerical solutions in general cases, we apply an unsupervised deep learning method, which has low complexity after the training stage. Through numerical results, we obtain the following three key findings: 1) The learning-based power control policy approaches the closed-form optimal policy in the special case and outperforms two existing power control policies in general cases. 2) Knowing the instantaneous CSI of the eavesdropper only provides a marginal gain of the secure data rate in the high signal-to-noise ratio regime. 3) In the presence of channel estimation errors, the learning-based policy trained by the estimated channels can guarantee the average transmit power constraint, while the closed-form policy cannot.
Physical layer security (PLS) is an approach that provides secrecy based on information-theoretic model which does not account for any computation capability assumption or pre-installed standardized secret key generation algorithm and it is a good additional protection on the top of the existing security scheme. This work includes four different topics which are about improving PLS with full-duplex radio. In the first topic, we develop a fast algorithms for computing power allocations in subcarriers, subject to power and rate constraints, to maximize the secrecy capacity of a three-node network consisting of a source, a full-duplex capable destination and an eavesdropper. The optimal power allocation at the destination is found to be significant especially when its power budget is high. The second topic is about the analysis of a two-phase scheme for secret information transmission with the technique of anti-eavesdropping channel estimation (ANECE), which requires full duplex transceiver and it can better handle Eve with any number of antennas. The analysis is based on the assumption that everyone has a prior statistical knowledge of the channel state information (CSI) anywhere and it yields lower and upper bounds on secure degrees of freedom as functions of the number of antennas on Eve and the size of information packet. For the third topic, we present optimal designs of the pilots for ANECE based on two criteria. The first is to optimize the minimum mean squared error (MMSE) channel estimation for the users, and the second is to maximize the mutual information between the pilot-driven signals observed by the users. Closed-form optimal pilots are shown under both criteria but subject to a symmetric and isotropic condition. Algorithms for computing the optimal pilots are shown for general cases. In the fourth topic, we analyze the secure degree of freedom and the asymptotic expression of the achievable secret key rate from a two-phase key generation scheme which consists of channel training phase and secure information transmission phase. Based on the asymptotic form, we develop an effective algorithm for coherence time allocation between the two phases to maximize the achievable secret key rate.
Physical layer security is a promising technique to ensure the confidentiality of short-packet communications, since no additional channel uses are needed. Motivated by the fact of finite coding blocklength in short-packet communications, we attempt to investigate the problem of how many the channel uses utilized for channel training should be allocated to perform secure communications. Based on the finite blocklength information theory, we derive a closed-form expression to approximate the average achievable secrecy throughput. To gain more insights, we also present the asymptotic average secrecy throughput under two special cases, i.e., high signal-to-noise ratio (SNR) and infinite blocklength. Moreover, we determine the optimal channel training length to maximize the average secrecy throughput under the reliability constraint and given blocklength. Numerical results are provided to validate the analysis and demonstrate that the performance gain achieved by the optimal channel training length is remarkable, relative to other benchmark schemes.
We consider a single-user secure data communication system. Data packets arriving at the transmitter are enqueued at a data queue to be transmitted to the receiver over a block fading channel, securely from an eavesdropper that listens to the transmitter over another independent block fading channel. Part of the data is secured by the available secrecy rate while the other part is encrypted by the key bits, enqueued at both the transmitter and the receiver. We first address two separate problems in this paper: (1) with an average power constraint, given any sample path of arrivals, how to admit the arrivals and allocate power such that the long term average secrecy rate is maximized and the maximum admission rate is achieved while the data queue is kept stable; (2) with infinite queue backlog, given any sample path of secrecy rate, how to control the data transmission rate and key generation such that a smooth transmission rate and then a small queueing delay are achieved. We propose a power controller, transmission controller and an admission controller based on simple index policies that do not rely on any prior statistical information on the data arrival process and channel conditions. We show that our controllers have a provably efficient performance and solve the above two problems simultaneously. Furthermore, for any given secrecy rate sample path and correspondingly admissible arrival, we provide a rate allocation policy which is sample-path queueing-delay optimal.
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.
As the technology and computing power advance day by day, physical layer security in wireless communications gains more importance. It allows the security of wireless communications based on information theoretical guarantees without the need for cryptography. One important practical challenge for physical layer security arises from the fact that no information about the passive eavesdroppers is available to the legitimate nodes. Without such information, optimizing the transmission parameters has always been a difficult task. In this study we aim to address various challenging aspects about this issue and propose a new perspective into the analysis of secrecy with a focus on jamming. We start from the case of eavesdropper's channel state information (CSI) known to users in a three-node single-antenna multi-subcarrier network and present an improved optimization algorithm compared to previous methods. Then we expand our horizon to the case of multiple-antenna nodes with eavesdropper's CSI unknown to users. We investigate a secure wireless communication scheme which combines two of the most effective strategies to combat eavesdropping, namely mixing information with artificial noise at the transmitter and jamming from a full-duplex receiver. While such setup has been investigated in related works, new and important insights are revealed in this study. We investigate the design of optimal jamming parameters to achieve higher secrecy.We then derive a closed-form expression for asymptotic normalized instantaneous secrecy rate of our model. Based on the results of the asymptotic analysis, we propose an alternative less complex tractable optimization problem for the design of optimal transmission parameters to achieve higher secrecy. We show that even with the optimal parameters, secrecy is compromised if Eve is able to increase her number of antennas. To address this issue, we provide a comprehensive analysis of a new scheme called anti-eavesdropping channel estimation (ANECE) which prevents Eve from acquiring accurate channel estimations. We show that the usage of such scheme, makes it extremely hard for Eve to drive secrecy to zero. Numerical analyses and simulations are presented to support the arguments made in this study.We then derive a closed-form expression for asymptotic normalized instantaneous secrecy rate of our model. Based on the results of the asymptotic analysis, we propose an alternative less complex tractable optimization problem for the design of optimal transmission parameters to achieve higher secrecy. We show that even with the optimal parameters, secrecy is compromised if Eve is able to increase her number of antennas. To address this issue, we provide a comprehensive analysis of a new scheme called anti-eavesdropping channel estimation (ANECE) which prevents Eve from acquiring accurate channel estimations. We show that the usage of such scheme, makes it extremely hard for Eve to drive secrecy to zero. Numerical analyses and simulations are presented to support the arguments made in this study.We then derive a closed-form expression for asymptotic normalized instantaneous secrecy rate of our model. Based on the results of the asymptotic analysis, we propose an alternative less complex tractable optimization problem for the design of optimal transmission parameters to achieve higher secrecy. We show that even with the optimal parameters, secrecy is compromised if Eve is able to increase her number of antennas. To address this issue, we provide a comprehensive analysis of a new scheme called anti-eavesdropping channel estimation (ANECE) which prevents Eve from acquiring accurate channel estimations. We show that the usage of such scheme makes it extremely hard for Eve to drive secrecy to zero. Numerical analyses and simulations are presented to support the arguments made in this study.
With the rapid development of wireless communications, security becomes extremely important. In many applications, each transmitter desires to send independent and confidential message to its intended receiver while ensuring mutual information-theoretic secrecy. In this dissertation, I study strategies for enhanced secrecy in wireless communication systems with limited rate feedback and transmitter cooperation. The two-user Gaussian channel model is considered under different scenarios. The transmitters both require channel state information (CSI), which is quantized at the receiver and fed back through the sum-rate-limited feedback channels. The quantization errors reduce the beamforming gain from the direct transmitter, and cause interference leakage from the cross transmitter. In the first scenario, I introduce the wiretap channel model where one receiver is a known eavesdropper, and a second transmitter is used to send a cooperative jamming signal to degrade the eavesdropper's channel. I consider two cases, one where no information about the eavesdroppers is available, and one where statistical CSI is available. With no information about the eavesdroppers, I show how to choose the allocation of feedback bits to the transmitters in order to maximize the amount of jamming power available to interfere with the eavesdroppers, subject to maintaining the lower bound on the rate at a minimum quality-of-service level. For the case of statistical CSI, I derive an approximate lower bound on the average secrecy rate, and optimize the bound to find a suitable bit allocation and the transmit power allocated to the transmitters. In the second scenario, I consider the interference channel model where the two transmitters are amenable to cooperation for improving the overall secrecy performance of the system. I derive an approximation for the average secrecy rate of each link, and optimize the sum secrecy rate over the transmit power and feedback bits allocated to the transmitters. Interestingly, increasing the transmit power beyond a certain point decreases the secrecy performance. When the transmitters have the same number of antennas, I derive the results in closed form. Simulations validate the theoretical analysis and demonstrate the significant performance gains that result from the use of optimal transmit power control and intelligent feedback bit allocation.