15 research outputs found

    Physical Layer Security in Integrated Sensing and Communication Systems

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    The development of integrated sensing and communication (ISAC) systems has been spurred by the growing congestion of the wireless spectrum. The ISAC system detects targets and communicates with downlink cellular users simultaneously. Uniquely for such scenarios, radar targets are regarded as potential eavesdroppers which might surveil the information sent from the base station (BS) to communication users (CUs) via the radar probing signal. To address this issue, we propose security solutions for ISAC systems to prevent confidential information from being intercepted by radar targets. In this thesis, we firstly present a beamformer design algorithm assisted by artificial noise (AN), which aims to minimize the signal-to-noise ratio (SNR) at the target while ensuring the quality of service (QoS) of legitimate receivers. Furthermore, to reduce the power consumed by AN, we apply the directional modulation (DM) approach to exploit constructive interference (CI). In this case, the optimization problem is designed to maximize the SINR of the target reflected echoes with CI constraints for each CU, while constraining the received symbols at the target in the destructive region. Apart from the separate functionalities of radar and communication systems above, we investigate sensing-aided physical layer security (PLS), where the ISAC BS first emits an omnidirectional waveform to search for and estimate target directions. Then, we formulate a weighted optimization problem to simultaneously maximize the secrecy rate and minimize the Cram\'er-Rao bound (CRB) with the aid of the AN, designing a beampattern with a wide main beam covering all possible angles of targets. The main beam width of the next iteration depends on the optimal CRB. In this way, the sensing and security functionalities provide mutual benefits, resulting in the improvement of mutual performances with every iteration of the optimization, until convergence. Overall, numerical results show the effectiveness of the ISAC security designs through the deployment of AN-aided secrecy rate maximization and CI techniques. The sensing-assisted PLS scheme offers a new approach for obtaining channel information of eavesdroppers, which is treated as a limitation of conventional PLS studies. This design gains mutual benefits in both single and multi-target scenarios

    International Conference on Continuous Optimization (ICCOPT) 2019 Conference Book

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    The Sixth International Conference on Continuous Optimization took place on the campus of the Technical University of Berlin, August 3-8, 2019. The ICCOPT is a flagship conference of the Mathematical Optimization Society (MOS), organized every three years. ICCOPT 2019 was hosted by the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) Berlin. It included a Summer School and a Conference with a series of plenary and semi-plenary talks, organized and contributed sessions, and poster sessions. This book comprises the full conference program. It contains, in particular, the scientific program in survey style as well as with all details, and information on the social program, the venue, special meetings, and more

    Trajectory Optimization and Guidance Design by Convex Programming

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    The field of aerospace guidance and control has recently been evolving from focusing on traditional laws and controllers to numerical algorithms with the aim of achieving onboard applications for autonomous vehicle systems. However, it is very difficult to perform complex guidance and control missions with highly nonlinear dynamic systems and many constraints onboard. In recent years, an emerging trend has occurred in the field of Computational Guidance and Control (CG&C). By taking advantage of convex optimization and highly efficient interior point methods, CG&C allows complicated guidance and control problems to be solved in real time and offers great potential for onboard applications. With the significant increase in computational efficiency, convex-optimization-based CG&C is expected to become a fundamental technology for system autonomy and autonomous operations. In this dissertation, successive convex approaches are proposed to solve optimal control programs associated with aerospace guidance and control, and the emphasis is placed on potential onboard applications. First, both fuel-optimal and time-optimal low-thrust orbit transfer problems are investigated by a successive second-order cone programming method. Then, this convex method is extended and improved to solve hypersonic entry trajectory optimization problems by taking advantage of line-search and trust-region techniques. Finally, the successive convex approach is modified to the design of autonomous entry guidance algorithms. Simulation results indicate that the proposed methodologies are capable of generating accurate solutions for low-thrust orbit transfer problems and hypersonic entry problems with fast computational speed. The proposed methods have great potential for onboard applications

    Dual-functional Cellular and Radar Transmission: Beyond Coexistence

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    We propose waveform design for a dual-functional multi-input-multi-output (MIMO) system, which carries out both radar target detection and multi-user communications using a single hardware platform. By enforcing both a constant modulus (CM) constraint and a similarity constraint with respect to referenced radar signals, we aim to minimize the downlink multiuser interference. Unlike conventional approaches which obtain suboptimal solutions to the generally NP-hard CM optimization problems involved, we propose a branch-and-bound method to efficiently find the global minimizer of the problem. Simulations show that the proposed algorithm significantly outperforms the state-of-art by achieving a favorable trade-off between radar and communication performance

    Non-convex Quadratically Constrained Quadratic Programming: Hidden Convexity, Scalable Approximation and Applications

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    University of Minnesota Ph.D. dissertation. September 2017. Major: Electrical Engineering. Advisor: Nicholas Sidiropoulos. 1 computer file (PDF); viii, 85 pages.Quadratically Constrained Quadratic Programming (QCQP) constitutes a class of computationally hard optimization problems that have a broad spectrum of applications in wireless communications, networking, signal processing, power systems, and other areas. The QCQP problem is known to be NP–hard in its general form; only in certain special cases can it be solved to global optimality in polynomial-time. Such cases are said to be convex in a hidden way, and the task of identifying them remains an active area of research. Meanwhile, relatively few methods are known to be effective for general QCQP problems. The prevailing approach of Semidefinite Relaxation (SDR) is computationally expensive, and often fails to work for general non-convex QCQP problems. Other methods based on Successive Convex Approximation (SCA) require initialization from a feasible point, which is NP-hard to compute in general. This dissertation focuses on both of the above mentioned aspects of non-convex QCQP. In the first part of this work, we consider the special case of QCQP with Toeplitz-Hermitian quadratic forms and establish that it possesses hidden convexity, which makes it possible to obtain globally optimal solutions in polynomial-time. The second part of this dissertation introduces a framework for efficiently computing feasible solutions of general quadratic feasibility problems. While an approximation framework known as Feasible Point Pursuit-Successive Convex Approximation (FPP-SCA) was recently proposed for this task, with considerable empirical success, it remains unsuitable for application on large-scale problems. This work is primarily focused on speeding and scaling up these approximation schemes to enable dealing with large-scale problems. For this purpose, we reformulate the feasibility criteria employed by FPP-SCA for minimizing constraint violations in the form of non-smooth, non-convex penalty functions. We demonstrate that by employing judicious approximation of the penalty functions, we obtain problem formulations which are well suited for the application of first-order methods (FOMs). The appeal of using FOMs lies in the fact that they are capable of efficiently exploiting various forms of problem structure while being computationally lightweight. This endows our approximation algorithms the ability to scale well with problem dimension. Specific applications in wireless communications and power grid system optimization considered to illustrate the efficacy of our FOM based approximation schemes. Our experimental results reveal the surprising effectiveness of FOMs for this class of hard optimization problems
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