Energy-Efficient Coordinated Multi-Cell Multigroup Multicast Beamforming with Antenna Selection

This paper studies energy-efficient coordinated beamforming in multi-cell multi-user multigroup multicast multiple-input single-output systems. We aim at maximizing the network energy efficiency by taking into account the fact that some of the radio frequency chains can be switched off in order to save power. We consider the antenna specific maximum power constraints to avoid non-linear distortion in power amplifiers and user-specific quality of service (QoS) constraints to guarantee a certain QoS levels. We first introduce binary antenna selection variables and use the perspective formulation to model the relation between them and the beamformers. Subsequently, we propose a new formulation which reduces the feasible set of the continuous relaxation, resulting in better performance compared to the original perspective formulation based problem. However, the resulting optimization problem is a mixed-Boolean non-convex fractional program, which is difficult to solve. We follow the standard continuous relaxation of the binary antenna selection variables, and then reformulate the problem such that it is amendable to successive convex approximation. Thereby, solving the continuous relaxation mostly results in near-binary solution. To recover the binary variables from the continuous relaxation, we switch off all the antennas for which the continuous values are smaller than a small threshold. Numerical results illustrate the superior convergence result and significant achievable gains in terms of energy efficiency with the proposed algorithm.Comment: 6 pages, 5 figures, accepted to IEEE ICC 2017 - International Workshop on 5G RAN Desig

An exact solution method for binary equilibrium problems with compensation and the power market uplift problem

We propose a novel method to find Nash equilibria in games with binary decision variables by including compensation payments and incentive-compatibility constraints from non-cooperative game theory directly into an optimization framework in lieu of using first order conditions of a linearization, or relaxation of integrality conditions. The reformulation offers a new approach to obtain and interpret dual variables to binary constraints using the benefit or loss from deviation rather than marginal relaxations. The method endogenizes the trade-off between overall (societal) efficiency and compensation payments necessary to align incentives of individual players. We provide existence results and conditions under which this problem can be solved as a mixed-binary linear program. We apply the solution approach to a stylized nodal power-market equilibrium problem with binary on-off decisions. This illustrative example shows that our approach yields an exact solution to the binary Nash game with compensation. We compare different implementations of actual market rules within our model, in particular constraints ensuring non-negative profits (no-loss rule) and restrictions on the compensation payments to non-dispatched generators. We discuss the resulting equilibria in terms of overall welfare, efficiency, and allocational equity

A convex OPF approximation for selecting the best candidate nodes for optimal location of power sources on DC resistive networks

This paper proposes a convex approximation approach for solving the optimal power flow (OPF) problem in direct current (DC) networks with constant power loads by using a sequential quadratic programming approach. A linearization method based on the Taylor series is used for the convexification of the power balance equations. For selecting the best candidate nodes for optimal location of distributed generators (DGs) on a DC network, a relaxation of the binary variables that represent the DGs location is proposed. This relaxation allows identifying the most important nodes for reducing power losses as well as the unimportant nodes. The optimal solution obtained by the proposed convex model is the best possible solution and serves for adjusting combinatorial optimization techniques for recovering the binary characteristics of the decision variables. The solution of the non-convex OPF model is achieved via GAMS software in conjunction with the CONOPT solver; in addition the sequential quadratic programming model is solved via quadprog from MATLAB for reducing the estimation errors in terms of calculation of the power losses. To compare the results of the proposed convex model, three metaheuristic approaches were employed using genetic algorithms, particle swarm optimization, continuous genetic algorithms, and black hole optimizers. © 2019 Karabuk UniversityUniversidad Tecnológica de Pereira, UTP: C2018P020, Departamento Administrativo de Ciencia, Tecnología e Innovación, COLCIENCIAS, Department of Science, Information Technology and Innovation, Queensland Government, DSIT

Dynamic Role Switching Scheme with Joint Trajectory and Power Control for Multi-UAV Cooperative Secure Communication

Due to the high flexibility and mobility, unmanned aerial vehicles (UAVs) can be deployed as aerial relays touring to serve ground users (GUs), especially when the ground base station is temporally damaged. However, the broadcasting nature of wireless channels makes such communication vulnerable to be wiretapped by malicious eavesdropping users (EUs). Besides the collecting offloading data for legitimate GUs, UAVs are also expected to be friendly jammers, i.e., generating artificial noise (AN) to deteriorate the wiretapping of EUs. With this in mind, a novel role switching scheme (RSS) is proposed in the paper to guarantee the secure communication by the cooperation of multiple UAVs, where each UAV is allowed to switch its role as a collector or a jammer autonomously to explore a wider trajectory space. It’s worthy to be noticed that the joint optimization for the trajectory of UAVs and the transmission power of GUs and UAVs with role switching scheme is a non-convex mixed integer non-linear programming (MINLP) problem. Since the relaxation of binary variables will lead the solution dropping into local minimum, a deep reinforcement learning (DRL) combined successive convex approximate (SCA) algorithm is further designed to maximize the achievable secrecy rate (ASR) of GUs. Numerical results illustrate that compared with the role fixed scheme (RFS) and relaxation based SCA approaches, the proposed DRL-SCA algorithm endows UAVs the capacity to fly close enough to target users (both GUs and EUs) with less moving distance which brings better ASR and less energy consumption

Explicit Model Predictive Control of Hybrid Systems using Multi-parametric Mixed Integer Polynomial Programming

Hybrid systems are dynamical systems characterized by the simultaneous presence of discrete and continuous variables. Model-based control of such systems is computationally demanding. To this effect, explicit controllers which provide control inputs as a set of functions of the state variables have been derived, using multiparametric programming mainly for the linear systems. Hybrid polynomial systems are considered resulting in a Mixed Integer Polynomial Programming problem. Treating the initial state of the system as a set of bounded parameters, the problem is reformulated as a multiparametric Mixed Integer Polynomial optimization (mp-MIPOPT) problem. A novel algorithm for mp-MIPOPT problems is proposed and the exact explicit control law for polynomial hybrid systems is computed. The key idea is the computation of the analytical solution of the optimality conditions while the binary variables are treated as relaxed parameters. Finally, using symbolic calculations exact nonconvex critical regions are compute

Scheduling of EV Battery Swapping, II: Distributed Solutions

In Part I of this paper, we formulate an optimal scheduling problem for battery swapping that assigns to each electric vehicle (EV) a best station to swap its depleted battery based on its current location and state of charge. The schedule aims to minimize a weighted sum of EVs’ travel distance and electricity generation cost over both station assignments and power flow variables, subject to EV range constraints, grid operational constraints, and ac power flow equations. We propose there a centralized solution based on second-order cone programming relaxation of optimal power flow and generalized Benders decomposition that is applicable when global information is available. In this paper, we propose two distributed solutions based on the alternating direction method of multipliers and dual decomposition, respectively, that are suitable for systems where the distribution grid, stations, and EVs are managed by separate entities. Our algorithms allow these entities to make individual decisions, but coordinate through privacy-preserving information exchanges to solve a convex relaxation of the global problem. We present simulation results to show that both algorithms converge quickly to a solution that is close to optimum after discretization

Energy Efficiency Maximization for C-RANs: Discrete Monotonic Optimization, Penalty, and l0-Approximation Methods

We study downlink of multiantenna cloud radio access networks (C-RANs) with finite-capacity fronthaul links. The aim is to propose joint designs of beamforming and remote radio head (RRH)-user association, subject to constraints on users' quality-of-service, limited capacity of fronthaul links and transmit power, to maximize the system energy efficiency. To cope with the limited-capacity fronthaul we consider the problem of RRH-user association to select a subset of users that can be served by each RRH. Moreover, different to the conventional power consumption models, we take into account the dependence of baseband signal processing power on the data rate, as well as the dynamics of the efficiency of power amplifiers. The considered problem leads to a mixed binary integer program (MBIP) which is difficult to solve. Our first contribution is to derive a globally optimal solution for the considered problem by customizing a discrete branch-reduce-and-bound (DBRB) approach. Since the global optimization method requires a high computational effort, we further propose two suboptimal solutions able to achieve the near optimal performance but with much reduced complexity. To this end, we transform the design problem into continuous (but inherently nonconvex) programs by two approaches: penalty and \ell_{0}-approximation methods. These resulting continuous nonconvex problems are then solved by the successive convex approximation framework. Numerical results are provided to evaluate the effectiveness of the proposed approaches.Comment: IEEE Transaction on Signal Processing, September 2018 (15 pages, 12 figures