Globally Optimal Energy-Efficient Power Control and Receiver Design in Wireless Networks

The characterization of the global maximum of energy efficiency (EE) problems in wireless networks is a challenging problem due to the non-convex nature of investigated problems in interference channels. The aim of this work is to develop a new and general framework to achieve globally optimal solutions. First, the hidden monotonic structure of the most common EE maximization problems is exploited jointly with fractional programming theory to obtain globally optimal solutions with exponential complexity in the number of network links. To overcome this issue, we also propose a framework to compute suboptimal power control strategies characterized by affordable complexity. This is achieved by merging fractional programming and sequential optimization. The proposed monotonic framework is used to shed light on the ultimate performance of wireless networks in terms of EE and also to benchmark the performance of the lower-complexity framework based on sequential programming. Numerical evidence is provided to show that the sequential fractional programming framework achieves global optimality in several practical communication scenarios.Comment: Accepted for publication in the IEEE Transactions on Signal Processin

An exact method for a discrete multiobjective linear fractional optimization

Integer linear fractional programming problem with multiple objective MOILFP is an important field of research and has not received as much attention as did multiple objective linear fractional programming. In this work, we develop a branch and cut algorithm based on continuous fractional optimization, for generating the whole integer efficient solutions of the MOILFP problem. The basic idea of the computation phase of the algorithm is to optimize one of the fractional objective functions, then generate an integer feasible solution. Using the reduced gradients of the objective functions, an efficient cut is built and a part of the feasible domain not containing efficient solutions is truncated by adding this cut. A sample problem is solved using this algorithm, and the main practical advantages of the algorithm are indicated.multiobjective programming, integer programming, linear fractional programming, branch and cut

Duality in Fractional Programming Involving Locally Arcwise Connected and Related Functions

A nonlinear fractional programming problem is considered, where the functions involved are diferentiable with respect to an arc.Necessary and su±cient optimality conditions are obtained in terms of the right diferentials with respect to an arc of the functions. A dual is formulated and duality results are proved using concepts of locally arcwise connected, locally Q-connected and locally P-connected functions .Our results generalize the results obtained by Lyall, Suneja and Aggarwal, Kaul and Lyall and Kaul et.al.Generalized convexity; Fractional programming; Optimality conditions, Duality

Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication

The adoption of a Reconfigurable Intelligent Surface (RIS) for downlink multi-user communication from a multi-antenna base station is investigated in this paper. We develop energy-efficient designs for both the transmit power allocation and the phase shifts of the surface reflecting elements, subject to individual link budget guarantees for the mobile users. This leads to non-convex design optimization problems for which to tackle we propose two computationally affordable approaches, capitalizing on alternating maximization, gradient descent search, and sequential fractional programming. Specifically, one algorithm employs gradient descent for obtaining the RIS phase coefficients, and fractional programming for optimal transmit power allocation. Instead, the second algorithm employs sequential fractional programming for the optimization of the RIS phase shifts. In addition, a realistic power consumption model for RIS-based systems is presented, and the performance of the proposed methods is analyzed in a realistic outdoor environment. In particular, our results show that the proposed RIS-based resource allocation methods are able to provide up to $300\%$ higher energy efficiency, in comparison with the use of regular multi-antenna amplify-and-forward relaying.Comment: Accepted by IEEE TWC; additional materials on the topic are included in the 2018 conference publications at ICASSP (https://ieeexplore.ieee.org/abstract/document/8461496) and GLOBECOM 2018 (arXiv:1809.05397

An exact method for a discrete multiobjective linear fractional optimization

Integer linear fractional programming problem with multiple objective MOILFP is an important field of research and has not received as much attention as did multiple objective linear fractional programming. In this work, we develop a branch and cut algorithm based on continuous fractional optimization, for generating the whole integer efficient solutions of the MOILFP problem. The basic idea of the computation phase of the algorithm is to optimize one of the fractional objective functions, then generate an integer feasible solution. Using the reduced gradients of the objective functions, an efficient cut is built and a part of the feasible domain not containing efficient solutions is truncated by adding this cut. A sample problem is solved using this algorithm, and the main practical advantages of the algorithm are indicated

An Alternative Solution to Multi Objective Linear Fractional Programming Problem by Using Geometric Programming Technique

In this study, we have proposed an alternative solution to the multi objective linear fractional programming problems. This method deals with every objective of multi objective linear fractional programming problems gradually by using geometric programming technique to find the pareto optimal solution. The proposed solution procedure has been used in numeric examples and results have been compared with the real solution values. Keywords: multi objective, fractional programming, geometric programmin

A Case Study on Solutions of Linear Fractional Programming Problems

In some decision making problems, objective function can be defined as the ratio of two linear functional subjects to given constraints. These types of problems are known as linear fractional programming problems. The importance of linear fractional programming problems comes from the fact that many real life problems can be expressed as the ratio of physical or economical values represented by linear functions, for example traffic planning, game theory and production planning etc. In this article, correspond to a production planning problem the mathematical model developed, is a linear fractional programming and in order to solve it, various fractional programming techniques has been used. Finally result is compared with the solution obtained by graphical method. To illustrate the efficiency of stated method a numerical example has given