Joint Optimization for Secure and Reliable Communications in Finite Blocklength Regime

To realize ultra-reliable low latency communications with high spectral efficiency and security, we investigate a joint optimization problem for downlink communications with multiple users and eavesdroppers in the finite blocklength (FBL) regime. We formulate a multi-objective optimization problem to maximize a sum secrecy rate by developing a secure precoder and to minimize a maximum error probability and information leakage rate. The main challenges arise from the complicated multi-objective problem, non-tractable back-off factors from the FBL assumption, non-convexity and non-smoothness of the secrecy rate, and the intertwined optimization variables. To address these challenges, we adopt an alternating optimization approach by decomposing the problem into two phases: secure precoding design, and maximum error probability and information leakage rate minimization. In the first phase, we obtain a lower bound of the secrecy rate and derive a first-order Karush-Kuhn-Tucker (KKT) condition to identify local optimal solutions with respect to the precoders. Interpreting the condition as a generalized eigenvalue problem, we solve the problem by using a power iteration-based method. In the second phase, we adopt a weighted-sum approach and derive KKT conditions in terms of the error probabilities and leakage rates for given precoders. Simulations validate the proposed algorithm.Comment: 30 pages, 8 figure

Secure Precoding for Future Wireless Communication Systems

Department of Electrical EngineeringPhysical layer security has emerged a flourishing strategy to protect confidential information from eavesdroppers with lower computational complexity compared to cryptography. Secure precoding is a promising transmission method of physical layer security to improve security by exploiting an intrinsic attribute of wireless communications. The main goal of the secure precoding is to maximize secrecy rate in multi-input and multi-output (MIMO) systems with the presence of eavesdroppers. Unfortunately, there exists no optimal solution, and also solving the secrecy rate maximization problem becomes more challenging as networks involve a multi-user (MU) and multi-eavesdropper (ME) scenario because of its non-smoothness and non-convexity. In this thesis, I proposed a novel secure precoding algorithm for downlink MU-MIMO systems under ME threat to enhance the secrecy rate, and provide subsequent analyses for realizing ultra-reliable low latency communications (URLLC). By incorporating strong security, communication reliability, and latency, a multi-objective optimization problem is investigated in the finite blocklength (FBL) regime. The derived optimization problem aims to maximize the secrecy rate by designing a secure precoder, while simultaneously minimizing both the maximum error probability and the rate of information leakage. The proposed FBL-based optimization algorithm provides the significantly improved tradeoff among the security, the error probability, and information leakage rate. Therefore, the proposed algorithms can offer significantly improved security for future wireless communication systems.clos

Secure Internet-of-Things Communications: Joint Precoding and Power Control

In this paper, we consider a downlink internet-of-things (IoT) multiple-input multiple-output (MIMO) network wherein an access point (AP), multiple IoT users, and a single eavesdropper coexist. The eavesdropper attempts to wiretap confidential messages of the IoT users. In the considered system, we solve a sum secrecy rate maximization problem in the finite blocklength (FBL) regime. Due to the FBL, the secrecy rate has a back-off factor with respect to blocklength, decoding error probability, and information leakage, which makes the problem more challenging. The main challenges are: i) the problem is not tractable because of the back-off factor, ii) an objective function is inherently non-convex, and iii) information leakage by the eavesdropper needs to be considered. To address these difficulties, we first obtain a lower bound of the secrecy rate and transform the problem into a product of Rayleigh quotients form. Then, we derive a first-order Karush-Kuhn-Tucker (KKT) condition to find a local optimal solution and interpret the condition as a generalized eigenvalue problem. Consequently, we develop a low-complexity algorithm by adopting a generalized power iteration-based (GPI) method. Via simulations, we validate the secrecy rate performance of the proposed method for the short-packet IoT communication systems

Joint Precoding and Artificial Noise Design for MU-MIMO Wiretap Channels

Secure precoding superimposed with artificial noise (AN) is a promising transmission technique to improve security by harnessing the superposition nature of the wireless medium. However, finding a jointly optimal precoding and AN structure is very challenging in downlink multi-user multiple-input multiple-output wiretap channels with multiple eavesdroppers. The major challenge in maximizing the secrecy rate arises from the non-convexity and non-smoothness of the rate function. Traditionally, an alternating optimization framework that identifies beamforming vectors and AN covariance matrix has been adopted; yet this alternating approach has limitations in maximizing the secrecy rate. In this paper, we put forth a novel secure precoding algorithm that jointly and simultaneously optimizes the beams and AN covariance matrix for maximizing the secrecy rate when a transmitter has either perfect or partial channel knowledge of eavesdroppers. To this end, we first establish an approximate secrecy rate in a smooth function. Then, we derive the first-order optimality condition in the form of the nonlinear eigenvalue problem (NEP). We present a computationally efficient algorithm to identify the principal eigenvector of the NEP as a suboptimal solution for secure precoding. Simulations demonstrate that the proposed methods improve secrecy rate significantly compared to the existing methods

A Review of Deep Learning Applications for Railway Safety

Railways speedily transport many people and goods nationwide, so railway accidents can pose immense damage. However, the infrastructure of railways is so complex that its maintenance is challenging and expensive. Therefore, using artificial intelligence for railway safety has attracted many researchers. This paper examines artificial intelligence applications for railway safety, mainly focusing on deep learning approaches. This paper first introduces deep learning methods widely used for railway safety. Then, we investigated and classified earlier studies into four representative application areas: (1) railway infrastructure (catenary, surface, components, and geometry), (2) train body and bogie (door, wheel, suspension, bearing, etc.), (3) operation (railway detection, railroad trespassing, wind risk, train running safety, etc.), and (4) station (air quality control, accident prevention, etc.). We present fundamental problems and popular approaches for each application area. Finally, based on the literature reviews, we discuss the opportunities and challenges of artificial intelligence for railway safety

Effect of mass transfer and kinetics in ordered Cu-mesostructures for electrochemical CO2 reduction

Mass transfer, kinetics, and mechanism of electrochemical CO2 reduction have been explored on a model mesostructure of highly-ordered copper inverse opal (Cu-IO), which was fabricated by Cu electrodeposition in a hexagonally-closed packed polystyrene template. As the number of Cu-IO layers increases, the formation of C-2 products such as C2H4 and C2H5OH was significantly enhanced at reduced overpotentials (similar to 200 mV) compared to a planar Cu electrode. At the thickest layer, we observe for the first time the formation of acetylene (C2H2), which can be generated through a kinetically slow reaction pathway and be a key descriptor in the unveiling of the C-C coupling reaction mechanism. Based on our experimental observation, a plausible reaction pathway in Cu mesostructures is rationalized