A framework for globally optimal energy-efficient resource allocation in wireless networks (original) (raw)
Related papers
Energy Efficiency in Wireless Networks via Fractional Programming Theory
Foundations and Trends® in Communications and Information Theory, 2015
Boldface upper-case and lowercase letters denote matrices and vectors, respectively. x , x T , x H denote Euclidean norm, transpose, and conjugate transpose of the n-dimensional column vector x = {x i } n i=1. 0 n and 1 n denote an all zero and an all one n-dimensional vector, respectively. Component-wise vector ordering is used, i.e. x y means x i ≥ y i , for all i = 1,. .. , N. tr(X), X T , X H , |X|, X −1 , X + , X denote trace, transpose, conjugate transpose, determinant, inverse, pseudo-inverse, and Frobenius norm of the matrix X. I n , O m,n , and diag(x) denote the identity matrix of order n, an all zero m × n matrix, and a diagonal matrix with x on the diagonal, respectively. Löwner matrix order is used, i.e. X Y means X − Y is positive semidefinite. ⊗ denotes the Kronecker matrix product. When applied to a set S, the symbol |S| denotes the cardinality of S. E, R, and C denote statistical expectation, the field of real numbers, and the field of complex numbers. R + and R ++ denote the set of nonnegative real numbers and the set of positive real numbers, respectively. We say that a function f (p) is o(p) if lim p→+∞ f (p) p = 0.
Optimal Throughput-Oriented Power Control by Linear Multiplicative Fractional Programming
2008
This paper studies optimal power control for throughput maximization in wireless ad hoc networks. Optimal power control problem in ad hoc networks is known to be nonconvex due to the co-channel interference between links. As a result, a global optimal solution is difficult to obtain. Previous work either simplified the problem by assuming that the signalto-interference-and-noise-radio (SINR) of each and every link is much higher than 1, or settled for suboptimal solutions. In contrast, we propose a novel methodology to compute the global optimal power allocation in a general SINR regime. In particular, we formulate the problem into an equivalent linear multiplicative fractional programming (LMFP). A global optimization algorithm, referred to as LMFP-based power allocation (LBPA) algorithm, is proposed to solve the LMFP with reasonable computational complexity. Our analysis proves that the LBPA algorithm is guaranteed to converge to a global optimal solution. Through extensive simulations, we show that the proposed algorithm significantly improves the throughput of wireless networks compared with existing ones.
Globally Optimal Energy-Efficient Power Control and Receiver Design in Wireless Networks
IEEE Transactions on Signal Processing, 2017
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.
Energy-Efficient Resource Allocation for Wireless Powered Communication Networks
IEEE Transactions on Wireless Communications, 2016
This paper considers a wireless powered communication network (WPCN), where multiple users harvest energy from a dedicated power station and then communicate with an information receiving station. Our goal is to investigate the maximum achievable energy efficiency (EE) of the network via joint time allocation and power control while taking into account the initial battery energy of each user. We first study the EE maximization problem in the WPCN without any system throughput requirement. We show that the EE maximization problem for the WPCN can be cast into EE maximization problems for two simplified networks via exploiting its special structure. For each problem, we derive the optimal solution and provide the corresponding physical interpretation, despite the non-convexity of the problems. Subsequently, we study the EE maximization problem under a minimum system throughput constraint. Exploiting fractional programming theory, we transform the resulting non-convex problem into a standard convex optimization problem. This allows us to characterize the optimal solution structure of joint time allocation and power control and to derive an efficient iterative algorithm for obtaining the optimal solution. Simulation results verify our theoretical findings and demonstrate the effectiveness of the proposed joint time and power optimization.
Optimal power allocation for green cognitive radio: fractional programming approach
IET Communications, 2013
In this study, the problem of determining the power allocation that maximizes the energy efficiency of cognitive radio network is investigated using differential evolution algorithm with constraint handling technique. The energy-efficient fractional objective is defined in terms of bits per Joule per Hertz. The proposed constrained fractional programming problem is a non-linear nonconvex optimization problem. Nature inspired algorithms like Differential Evolution (DE) can describe and resolve complex relationships from intrinsically very simple initial conditions with little or no knowledge of the search space. In simulation results, the effect of different system parameters (interference threshold level, number of primary users and number of secondary users) on the performance of the proposed algorithm is investigated.
Fractional Programming for Communication Systems—Part I: Power Control and Beamforming
IEEE Transactions on Signal Processing, 2018
Fractional programming (FP) refers to a family of optimization problems that involve ratio term(s). This two-part paper explores the use of FP in the design and optimization of communication systems. Part I of this paper focuses on FP theory and on solving continuous problems. The main theoretical contribution is a novel quadratic transform technique for tackling the multiple-ratio concave-convex FP problem-in contrast to conventional FP techniques that mostly can only deal with the single-ratio or the max-min-ratio case. Multiple-ratio FP problems are important for the optimization of communication networks, because system-level design often involves multiple signal-to-interference-plus-noise ratio terms. This paper considers the applications of FP to solving continuous problems in communication system design, particularly for power control, beamforming, and energy efficiency maximization. These application cases illustrate that the proposed quadratic transform can greatly facilitate the optimization involving ratios by recasting the original nonconvex problem as a sequence of convex problems. This FP-based problem reformulation gives rise to an efficient iterative optimization algorithm with provable convergence to a stationary point. The paper further demonstrates close connections between the proposed FP approach and other well-known algorithms in the literature, such as the fixed-point iteration and the weighted minimum mean-square-error beamforming. The optimization of discrete problems is discussed in Part II of this paper.
Energy-Efficient Power Allocation With Individual and Sum Power Constraints
IEEE Transactions on Wireless Communications, 2018
In this paper, we investigate the power allocation in a multiuser wireless system to maximize the Energy efficiency (EE), while meeting the power constrains of each individual user as well as the whole system. Specifically, a geometric ceiled-waterfilling algorithm is proposed to solve this non-linear fractional optimization problem, which can compute exact solutions with a low degree of polynomial computational complexity. Optimality of the proposed algorithm is strictly proved with mathematic analysis. In addition, the proposed algorithm is further extended to the general case with the minimum system-level throughput constraint, considering the quality of service (QoS) requirement. To the best of our knowledge, no prior algorithm in the open literature offered such optimal solutions to the target problems, with the merit of exactness and the efficiency. Simulation results demonstrate that the proposed power allocation algorithms can improve the energy efficiency by nearly 50%, compared with the conventional Dinkelbach's method with the same amount of computations.
Framework for Link-Level Energy Efficiency Optimization with Informed Transmitter
IEEE Transactions on Wireless Communications, 2000
The dramatic increase of network infrastructure comes at the cost of rapidly increasing energy consumption, which makes optimization of energy efficiency (EE) an important topic. Since EE is often modeled as the ratio of rate to power, we present a mathematical framework called fractional programming that provides insight into this class of optimization problems, as well as algorithms for computing the solution. The main idea is that the objective function is transformed to a weighted sum of rate and power. A generic problem formulation for systems dissipating transmit-independent circuit power in addition to transmit-dependent power is presented. We show that a broad class of EE maximization problems can be solved efficiently, provided the rate is a concave function of the transmit power.
IEEE Transactions on Communications, 2000
In this paper, the joint power and subcarrier allocation problem is solved in the context of maximizing the energy-efficiency (EE) of a multiuser , multi-relay orthogonal frequency division multiple access (OFDMA) cellular network, where the objective function is formulated as the ratio of the spectral-efficiency (SE) over the total power dissipation. It is proven that the fractional programming problem considered is quasi-concave so that Dinkelbach's method may be employed for finding the optimal solution at a low complexity. This method solves the above-mentioned master problem by solving a series of parameterized concave secondary problems. These secondary problems are solved using a dual decomposition approach, where each secondary problem is further decomposed into a number of similar subproblems. The impact of various system parameters on the attainable EE and SE of the system employing both EE maximization (EEM) and SE maximization (SEM) algorithms is characterized. In particular, it is observed that increasing the number of relays for a range of cell sizes, although marginally increases the attainable SE, reduces the EE significantly. It is noted that the highest SE and EE are achieved, when the relays are placed closer to the BS to take advantage of the resultant line-of-sight link. Furthermore, increasing both the number of available subcarriers and the number of active user equipment (UE) increases both the EE and the total SE of the system as a benefit of the increased frequency and multiuser diversity, respectively. Finally, it is demonstrated that as expected, increasing the available power tends to improve the SE, when using the SEM algorithm. By contrast, given a sufficiently high available power, the EEM algorithm attains the maximum achievable EE and a suboptimal SE.
Engineering Science and Technology, an International Journal, 2020
In this article, we devise two efficient radio resource allocation algorithms in order to increase fair energy efficiency among users of OFDMA networks. The objective function is considered to be the well-known max-min function in order for allocating radio resources fairly in terms of energy efficiency, by taking the maximum transmit power, minimum rate requirements, and subchannel assignment constraints into account. The resulting problem is a nonconvex mixed-integer nonlinear problem for which two algorithms are proposed. In the first proposed algorithm, by reallocating the transmit power on subchannels, we obtain a suitable subchannel assignment with high fair energy efficiency. In the second proposed algorithm, by applying generalized fractional programming, mathematically modifying the objective function and constraints to a continuous and convex form and employing sequential convex programming, we allocate power and subchannel jointly. The proposed algorithms efficiently utilize radio resources and therefore, yield a far better performance than available solutions.