Multi-Objective Energy-Efficient Resource Allocation for Multi-RAT Heterogeneous Networks (original) (raw)
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Energy Efficient Optimal Resource Allocation in Multi-RAT Heterogeneous Network
In this paper, we address the frequent problem associated with user association and resource allocation along with optimal deployment of base station (BS) in multiple radio access technology (Multi-RAT)-assisted heterogeneous network (Het-Net). Considering real time user scenarios, optimal resource allocation in Het-Net while ensuring each user's minimum required data rate is a challenging task to be performed. Here, we propose a novel algorithm with a well-known and efficient metaheuristic optimization technique to resolve the aforementioned problem. We use hybrid memory-based dragonfly algorithm with differential evolution (DADE) for its excellent convergence characteristics. Extensive simulations are performed to determine the optimal network utility under the consideration of nonuniform user distribution and fine-tuning their respective service class and contract of association parameters. Simulation results depict that the proposed algorithm improves the overall network utility in terms of radio resource utilization and energy consumption while satisfying the user demands. Comparative analysis of the proposed technique with the other state-of-theart algorithm depicts the superiority of the proposed algorithm in terms of accuracy and consistency. We also perform optimal multi-RAT cell planning under the above constraints including a network blackout scenario. The algorithm ensures each user coverage by optimally allocating the available resources.
QoS-aware energy-efficient resource allocation in OFDM-based heterogenous cellular networks
International Journal of Communication Systems, 2015
Recently, in order to satisfy the heavy demands of network capacity brought about by the proliferation of wireless devices, service providers are increasingly deploying heterogeneous cellular networks (HetNets) for boosting the network coverage and capacity. In this paper, we present an iterative energy-efficient scheduling scheme (IEESS) for downlink OFDM-based HetNets with quality-of-service (QoS) consideration. We formulate the problem as a nonlinear fractional programming problem aiming to maximize the QoS-aware energy efficiency (QEE) in HetNets. In order to solve this problem, we first transform it into a parametric programming problem, which takes QEE as an evolved parameter in the iterative procedure of IEESS. In each iteration, for the given value of QEE, subchannel and power assignment sub-problem is a nonlinear NP-hard problem. And hence we adopt dual decomposition method for obtaining the optimal assignment of subchannels and power of the sub-problem for the given value of QEE. Simulation results depict that both outer QEE parameter search and inner subgradient search can converge in a few iterations and the resultant solutions outperform the equal power allocation scheme (EPAS) [1] and capacity maximization scheme (CMS) [2] in terms of QEE.
A framework for globally optimal energy-efficient resource allocation in wireless networks
2016
State-of-the-art algorithms for energy-efficient resource allocation in wireless networks are based on fractional programming theory, and are able to find the global maximum of the system energy efficiency only in noise-limited scenarios. In interference-limited scenarios, several sub-optimal solutions have been proposed, but an efficient framework to globally maximize energy-efficient metrics is still lacking. The goal of this work is to fill this gap, which will be achieved by merging fractional programming theory with monotonic optimization theory. The resulting optimization framework is useful for at least two main reasons. First, it sheds light on the ultimate energy-efficient performance of wireless networks. Second, it provides the means to benchmark the energy efficiency of practical, but sub-optimal, solutions.
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.
Energy Efficient Resource Allocation for 5G Heterogeneous Networks using Genetic Algorithm
IEEE Access
The energy efficient resource allocation scheme based on genetic algorithm (GA) for the downlink orthogonal frequency division multiple access (OFDMA) heterogeneous networks (HetNets) is developed in this paper. To maximize the spectrum efficiency for the fifth generation (5G) mobile networks, frequency reuse-1 is employed. Thus, advanced inter-cell interference coordination techniques are required to mitigate the inter-cell interference for 5G HetNets. In this paper, the energy efficient optimization problem based on coordinated scheduling is formulated, which is a mixed-integer nonlinear fractional programming problem and is intractable to solve directly. To tackle this, a two-step GA based scheme is proposed to solve the optimization problem. In the first step, the resource blocks matrix is solved by normal GA in the spectral efficiency aspect with fixed power distribution matrix, and then the power distribution matrix is obtained in the second step by non-dominated sorting genetic algorithm II (NSGA-II) with obtained resource blocks allocation matrix. Finally, the system level numerical evaluation process is provided to illustrate the effectiveness of the developed scheme. INDEX TERMS Energy efficiency, resource allocation, heterogeneous networks, genetic algorithm.
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.
Energy-Efficient Bandwidth and Power Allocation for Multi-homing Networks
IEEE Transactions on Signal Processing, 2015
This paper investigates resource allocation for multi-homing networks where users can simultaneously transmit data to multiple radio access networks (RANs) using multiple air interfaces. We aim at optimally assigning the bandwidth and power to each user-RAN connection so as to maximize energy-efficiency of the entire network subject to user specific QoS requirements as well as the available resource budgets.
IEEE Transactions on Wireless Communications, 2012
Energy consumption in wireless communication system is rapidly increasing due to growing wireless multimedia access. Combating adverse effects of excessive energy consumption demands for energy-aware system design, leading to a new research paradigm called green communication. In this paper, we propose user selection and power allocation schemes for a multiuser, multi-relay cooperative cellular system in order to minimize the cost of transmission. In the proposed schemes, the cost function is first formulated to optimize the weighted sum powers of base and relay stations. It is then extended to a more general multi-objective scheme which jointly optimizes the sum power and throughput keeping a balance between them. In both of the schemes, quality-of-service is guaranteed in terms of end-to-end signal-to-noise ratio. To make the proposed schemes realistic, we assume the presence of estimation errors in channel state information. An algorithm to enhance fairness among users in these schemes is also presented. Simulation results are presented to confirm the performance of proposed schemes in terms of energy efficiency, system throughput, outage probability, and fairness to end users.
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.
International Journal of Communication Systems, 2015
Energy efficiency (EE) has currently turn into one of the major issues in heterogeneous networks (HetNet) paradigm of today's wireless communication industry. In this paper, we optimize EE for downlink OFDMA system in HetNet, taking into account realistic network power consumption model, that is, considering circuit power. This paper investigates the EE maximization using convex optimization theory where primary optimization criterion is data rate in a downlink multiuser HetNet. Given QoS (data rate) requirement, for maximizing EE, a constrained based optimization problem is devised. Because the optimization problem is non-convex in nature, we reconstruct the optimization problem as a convex one and devise a pragmatically efficient novel resource assignment algorithm for maximizing achievable EE, with quick convergence. The considered optimization problem is transformed into a convex optimization problem by redefining the constraint using cubic inequality, which results in an efficient iterative resource allocation algorithm. In each iteration, the transformed problem is solved by using dual decomposition with a projected gradient method. Analytical insights and numerical results exhibit the potency of the devised scheme for the targeted complex wireless systems.