Rank-Two Beamforming and Power Allocation in Multicasting Relay Networks

In this paper, we propose a novel single-group multicasting relay beamforming scheme. We assume a source that transmits common messages via multiple amplify-and-forward relays to multiple destinations. To increase the number of degrees of freedom in the beamforming design, the relays process two received signals jointly and transmit the Alamouti space-time block code over two different beams. Furthermore, in contrast to the existing relay multicasting scheme of the literature, we take into account the direct links from the source to the destinations. We aim to maximize the lowest received quality-of-service by choosing the proper relay weights and the ideal distribution of the power resources in the network. To solve the corresponding optimization problem, we propose an iterative algorithm which solves sequences of convex approximations of the original non-convex optimization problem. Simulation results demonstrate significant performance improvements of the proposed methods as compared with the existing relay multicasting scheme of the literature and an algorithm based on the popular semidefinite relaxation technique

Wireless Powered Communication Networks

The limited life time of batteries is a crucial issue in energy-constrained wireless communications. Recently, the radio frequency (RF) wireless energy transfer (WET) technique has been developed as a new practical method to extend the life time of wireless communication networks. Inspired by this, wireless-powered communication network (WPCN) has attracted much attention. Therefore, in this thesis, we consider practical WET and wireless-powered information transmission in WPCNs. First we investigate a WPCN with two nodes, in which an access point (AP) exchanges information with a wireless-powered user. The user is assumed to have no embedded energy supply and needs to harvest energy from RF signals broadcast by the AP. Differing from existing work that focuses on the design of wireless-powered communication with one-way information flow, we deal with a more general scenario where both the AP and the user have information to transmit. Considering that the AP and user can work in either half-duplex or full-duplex mode as well as having two practical receiver architectures at the user side, we propose five elementary communication protocols for the considered system. Moreover, we define the concept of a throughput region to characterize the tradeoff between the uplink and downlink throughput in all proposed protocols. Numerical simulations are finally performed to compare the throughput regions of the proposed five elementary protocols. To further the study on WPCN, we investigate a wireless-powered two-way relay system, in which two wireless-powered sources exchange information through a multi-antenna relay. Both sources are assumed to have no embedded energy supply and thus first need to harvest energy from the radio frequency signals broadcast by the relay before exchanging their information via the relay. We aim to maximize the sum throughput of both sources by jointly optimizing the time switching duration, the energy beamforming vector and the precoding matrix at the relay. The formulated problem is non-convex and hard to solve in its original form. Motivated by this, we simplify the problem by reducing the number of variables and by decomposing the precoding matrix into a transmit vector and a receive vector. We then propose a bisection search, a 1-D search and an iterative algorithm to optimize each variable. Numerical results show that our proposed scheme can achieve higher throughput than the conventional scheme without optimization on the beamforming vector and precoding matrix at the relay. Due to the high attenuation of RF energy over a long distance, RF based wireless-powered communication is usually designed for low-power scenarios, e.g., wireless-powered sensor networks. Recently, magnetic induction (MI) based WET has been proposed to wirelessly transfer a large amount of energy. Inspired by this, we investigate MI based WET in WPCN. Specifically, we study a MI based wireless-powered relaying network, in which a MI source transmits information to a MI destination, with the help of a MI based wireless powered relay. We propose four active relaying schemes, which consider different relaying modes and different energy harvesting receiver architectures at the relay. We then aim to maximize the end-to-end throughput of each scheme by using a bisection search, a water-filling algorithm, a Lagrange multiplier, quasi-convex programming and an iterative algorithm. We compare the proposed active relaying schemes with passive relaying. Numerical results show that the proposed relaying schemes with a decode-and-forward relaying mode significantly improve the throughput over passive relaying

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

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