Energy-efficient Resource Allocation Problem Research Articles

Energy-Efficient Optimization for Downlink Massive MIMO FDD Systems With Transmit-Side Channel Correlation

The energy-efficient resource allocation problem is investigated for the downlink massive multiple-input–multiple-output (MIMO) frequency-division duplexing (FDD) system under a correlated Rayleigh fading channel. Our objective is to maximize energy efficiency by adjusting the training duration, training power, and data power, under the constraint of the total transmit energy and spectral efficiency requirement for the user, which evaluates the impacts of the training and data transmission phases simultaneously. The optimization problem is established in a complicated form, where the difficulties lie in the nonanalytic expression of the involved objective function, as well as the nonconvex nature of the optimization problem. To solve it, the deterministic equivalent approximation methodology is introduced to obtain an accurate analytical expression of the cost function. Based on this, the optimization problem in a nonconvex fractional form is transformed into an equivalent optimization problem with a parametric subtractive form, which is still nonconvex. Thus, the objective function is lower bounded by a concave function, which leads to the availability of standard convex theory, and finally, an iterative resource allocation algorithm is proposed. Moreover, we obtain the closed-form solutions using the Lambert W function for some special channel cases. Numerical results validate the benefits of the proposed scheme and illustrate the tradeoff between training and data transmission.

Energy-Efficient Resource Allocation for D2D Communications in Cellular Networks

In this paper, we study the energy-efficient resource allocation problem for device-to-device (D2D) communication underlaying cellular networks, which aims to maximize the minimum weighted energy efficiency of D2D links while guaranteeing minimum data rates for cellular links. We first characterize the optimal power allocation of the cellular links to transform the original resource allocation problem into the joint subchannel and power allocation problem for D2D links. We then propose three resource allocation algorithms with different complexity levels, namely, dual-based, branch-and-bound (BnB), and relaxation-based rounding (RBR) algorithms. While the dual-based algorithm solves the problem by using the dual decomposition method, the BnB and RBR algorithms tackle the problem by employing the relaxation approach. We establish the strong performance guarantees for the proposed algorithms through theoretical analysis. Extensive numerical studies demonstrate that the proposed algorithms achieve superior performance and significantly outperform a conventional algorithm.

Energy-efficient resource allocation in multi-user AF two-way relay channels

In this paper, we investigate an energy-efficient resource allocation problem in a two-way relay (TWR) network consisting of multiple user pairs and an amplify-and-forward (AF) relay. As the users and relay have individual energy efficiencies (EE), we formulate a multi-objective optimization problem (MOOP). A single-objective optimization problem (SOOP) of the MOOP is introduced using a weighted-sum method, which achieves a single Pareto optimal point of the MOOP. To derive the algorithm for the SOOP, we propose a more tractable equivalent problem using the Karush-Kuhn-Tucker conditions of the SOOP, which guarantees convergence at the local optimal points. The proposed equivalent problem can be efficiently solved by the proposed iterative algorithm. Numerical results demonstrate the effectiveness of the proposed algorithm in achieving the optimal EE in multi-user AF TWR networks.

Energy-Efficient Chance-Constrained Resource Allocation for Multicast Cognitive OFDM Network

In this paper, an energy-efficient resource allocation problem is modeled as a chance-constrained programming for multicast cognitive orthogonal frequency division multiplexing (OFDM) network. The resource allocation is subject to constraints in service quality requirements, total power, and probabilistic interference constraint. The statistic channel state information (CSI) between cognitive-based station (CBS) and primary user (PU) is adopted to compute the interference power at the receiver of PU, and we develop an energy-efficient chance-constrained subcarrier and power allocation algorithm. Support vector machine (SVM) is employed to compute the probabilistic interference constraint. Then, the chance-constrained resource allocation problem is transformed into a deterministic resource allocation problem, and Zoutendijk’s method of feasible direction is utilized to solve it. Simulation results demonstrate that the proposed algorithm not only achieves a tradeoff between energy efficiency and satisfaction index, but also guarantees the probabilistic interference constraint very well.

Energy-Efficient Resource Allocation in Coordinated Downlink Multicell OFDMA Systems

In this paper, a resource allocation algorithm for maximizing the energy efficiency (EE) is studied in multicell orthogonal frequency-division multiple-access (OFDMA) wireless networks. The resource allocation is designed based on imperfect channel state information (CSI). Universal frequency reuse is considered, and cochannel interference is dealt with via the cooperation of multiple base stations (BSs) sharing CSI but not user data. We formulate the energy-efficient resource allocation problem as a probabilistic mixed nonconvex optimization problem. The user scheduling, data rate adaptation, and power allocation are jointly designed to maximize the system EE, under the maximum transmitted power constraint and the outage probability constraint. An iterative algorithm is proposed in which the EE keeps improving until algorithm convergence. In each iteration, the energy-efficient power allocation optimization problem is solved by a lower bound problem and a parameterized transformation. Numerical results illustrate the convergence and the effectiveness of the proposed algorithm. In a multicell environment with cochannel interference, the proposed algorithm has large EE gains relative to the spectral efficiency (SE) loss. Compared with noncoordinated methods, the proposed method can achieve higher EE when backhaul power consumption is low.

Energy-efficient resource allocation for multiuser OFDMA system based on hybrid genetic simulated annealing

Orthogonal frequency division multiple access (OFDMA) is adopted in 4G wireless communication standard, where bandwidth resources can be split into smaller granular units. Tailored for slow adaptive OFDMA system, we study the energy-efficient resource allocation problem based on chance-constrained programming. The optimization objective is to minimize the power consumption over subcarrier and power allocation. The constraints include the outage probability constraint and target bit error rate (BER) constraint. Because the optimization problem contains the probabilistic constraint, it is a chance-constrained programming problem. Hence, support vector machine (SVM) is adopted to compute the outage probability constraint. Then, we integrate SVM and genetic simulated annealing (GSA) to develop hybrid genetic simulated annealing (HGSA). Simulation tests verify that HGSA not only has the lower consumption power, but also satisfies the outage probability constraint.

Energy-efficient resource allocation for heterogeneous cognitive radio network based on two-tier crossover genetic algorithm

Cognitive radio (CR) is considered an attractive technology to deal with the spectrum scarcity problem. Multi-radio access technology (multi-RAT) can improve network capacity because data are transmitted by multiple RANs (radio access networks) concurrently. Thus, multi-RAT embedded in a cognitive radio network (CRN) is a promising paradigm for developing spectrum efficiency and network capacity in future wireless networks. In this study, we consider a new CRN model in which the primary user networks consist of heterogeneous primary users (PUs). Specifically, we focus on the energy-efficient resource allocation (EERA) problem for CR users with a special location coverage overlapping region in which heterogeneous PUs operate simultaneously via multi-RAT. We propose a two-tier crossover genetic algorithm-based search scheme to obtain an optimal solution in terms of the power and bandwidth. In addition, we introduce a radio environment map to manage the resource allocation and network synchronization. The simulation results show the proposed algorithm is stable and has faster convergence. Our proposal can significantly increase the energy efficiency.

Energy-Efficient Resource Allocation in Single-Cell OFDMA Systems: Multi-Objective Approach

In this paper, we investigate the energy-efficient resource allocation problem in a single-cell orthogonal frequency division multiple access (OFDMA) system to achieve the energy efficiency (EE) tradeoff among users. Rather than overall system EE, our objective is to maximize the EE for each individual user. Therefore, a multiple-objective optimization problem is formulated, which in general has many Pareto optimal solutions and is hard to solve. To find its solution, we first convert it into two different single-objective optimization problems using the weighted-sum approach and the max-min approach, respectively. The single-objective optimization problems are non-convex due to the combinatorial channel allocation variables. Therefore, for both problems, we first provide an upper bound algorithm by relaxing the combinatorial variables and then develop a suboptimal heuristic algorithm. The sum-of-ratios optimization and the generalized fractional programming are utilized for the weighted-sum problem and the max-min problem, respectively. Numerical results demonstrate that both the weighted-sum and the max-min approaches can effectively solve the EE maximization problem, and the suboptimal heuristic algorithms can achieve a close performance to the corresponding upper bound algorithm.

Resource allocation based on quantum particle swarm optimization and RBF neural network for overlay cognitive OFDM System

In this work, we study the energy-efficient resource allocation problem based on chance-constrained programming for overlay cognitive orthogonal frequency division multiplexing (OFDM) system. The objective function minimizes the total power consumption and the constraint conditions include the requirement of the system outage probability and the feasibility of the subcarrier allocation solution. In order to solve the above chance-constrained resource allocation problem, two steps are taken to develop hybrid quantum particle swarm optimization (HQPSO). In the first step, we define an uncertain function according to the outage probability constraint condition and utilize the radial basis function (BRF) neural network to computer it. In the second step, HQPSO which includes quantum particle swarm optimization (QPSO) and RBF neural network is proposed. Simulation results demonstrate that the total power consumption of HQPSO is smaller than that of other algorithms while the system outage probability could be satisfied very well.

Energy-Efficient Resource Allocation in Cellular Networks With Shared Full-Duplex Relaying

Recent advances in self-interference cancelation techniques enable full-duplex relaying (FDR) systems, which transmit and receive simultaneously in the same frequency band with high spectrum efficiency. Unlike most existing works, we study the problem of energy-efficient resource allocation in FDR networks. We consider a shared FDR deployment scenario, where an FDR relay is deployed at the intersection of several adjacent cell sectors. First, a simple but practical transmission strategy is proposed to deal with the involved interference, i.e., multiaccess interference, multiuser interference, and self-interference. Then, the problem of joint power and subcarrier allocation is formulated to maximize the network-level energy efficiency while taking the residual self-interference into account. Since the formulated problem is a mixed combinatorial and nonconvex optimization problem with high computation complexity, we use Dinkelbach and discrete stochastic optimization methods to solve the energy-efficient resource-allocation problem efficiently. Simulation results are presented to show the effectiveness of the proposed scheme.

Multiple resource allocation in device‐to‐device communication underlaying cellular networks from an end‐to‐end energy‐efficient perspective

In this study, a novel energy-efficient resource allocation (RA) scheme is proposed for device-to-device communication underlaying cellular networks from an end-to-end energy-efficient perspective. The time slot, sub-channel (frequency) and power resources are allocated together to optimise the energy-efficiency (EE) performance. Furthermore, to match the practical communication situations and achieve the best EE performance, the time–frequency resource units (RUs) are used in a complete-shared pattern. Then, the multiuser interference is very severe and complex. With all these considerations, the energy-efficient RA problem is formulated as a mixed integer and non-convex optimisation problem, which is an non-deterministic polynominal (NP)-hard problem and extremely difficult to solve. To obtain a desirable solution with a reasonable computation cost, the authors tackle this problem with two steps. Step 1, the RU allocation policy is obtained via a greedy search method, and the original optimisation problem is reduced to a non-convex fractional programming problem. Step 2, exploiting the properties of fractional programming and after some manipulations, they transform the reduced problem to a concave optimisation problem, and obtain the sub-optimal power allocation strategy through the Lagrange dual approach. Finally, simulation results are presented to validate the effectiveness of the proposed RA scheme.

End-to-end energy-efficient resource allocation in device-to-device communication underlaying cellular networks

A proposed resource allocation (RA) scheme is given to device-to-device (D2D) communication underlaying cellular networks from an end-to-end energy-efficient perspective, in which, the end-to-end energy consumptions were taken into account. Furthermore, to match the practical situations and maximize the energy-efficiency (EE), the resource units (RUs) were used in a complete-shared pattern. Then the energy-efficient RA problem was formulated as a mixed integer and non-convex optimization problem, extremely difficult to be solved. To obtain a desirable solution with a reasonable computation cost, this problem was dealt with two steps. Step 1, the RU allocation policy was obtained via a greedy search method. Step 2, after obtaining the RU allocation, the power allocation strategy was developed through quantum-behaved particle swarm optimization (QPSO). Finally, simulation was presented to validate the effectiveness of the proposed RA scheme.

Energy-Efficient Scheduling and Power Allocation in Downlink OFDMA Networks With Base Station Coordination

This paper addresses the problem of energy-efficient resource allocation in the downlink of a cellular OFDMA system. Three definitions of the energy efficiency are considered for system design, accounting for both the radiated and the circuit power. User scheduling and power allocation are optimized across a cluster of coordinated base stations with a constraint on the maximum transmit power (either per subcarrier or per base station). The asymptotic noise-limited regime is discussed as a special case. %The performance of both an isolated and a non-isolated cluster of coordinated base stations is examined in the numerical experiments. Results show that the maximization of the energy efficiency is approximately equivalent to the maximization of the spectral efficiency for small values of the maximum transmit power, while there is a wide range of values of the maximum transmit power for which a moderate reduction of the data rate provides a large saving in terms of dissipated energy. Also, the performance gap among the considered resource allocation strategies reduces as the out-of-cluster interference increases.

Energy-Efficient Resource Allocation in LTE-Based MIMO-OFDMA Systems With User Rate Constraints

In recent years, the enormous energy consumption of wireless communication systems has aroused universal concerns throughout the world. Hence, energy efficiency has become one of the central topics in today's wireless communication industry. In this paper, we study energy-efficient resource allocation for Long Term Evolution (LTE) cellular mobile systems. To be applicable in LTE systems, both multiple-input multiple-output (MIMO) orthogonal frequency-division multiple access (OFDMA) radio access network and resource blocks (RBs) for subchannel assignment are considered in this work, functioning as two distinguishing characteristics for LTE resource allocation. In particular, an optimization problem concerning joint RB assignment, and power allocation is formulated to maximize the energy efficiency measured by “bits-per-Joule” metric, under per-user quality-of-service (QoS) requirements in the form of user rate constraints. We first show that this energy-efficient resource allocation problem can be converted into a more tractable equivalent problem by which the fractional objective of energy efficiency is well settled. With the Lagrange dual method then, we decouple the RB assignment and power allocation on different subchannels using dual decomposition, which greatly simplifies the combinatorial RB assignment, and address the equivalent problem effectively via solving a series of dual optimization problems. Moreover, a computationally efficient but near-optimal algorithm is proposed to perform QoS-aware energy-efficient resource allocation in practice. Simulation results show that our proposed algorithm may not only improve energy efficiency significantly but fully satisfy users' rate requirements as well. Moreover, with MIMO-OFDMA and RB assignment being considered, the proposed algorithm may be more applicable in LTE systems than most existing schemes on energy-efficient resource allocation.

Energy-Efficient Resource Allocation for Heterogeneous Services in OFDMA Downlink Networks: Systematic Perspective

In the area of energy-efficient (EE) resource allocation, only limited work has been done on consideration of both transmitter and receiver energy consumption. In this paper, we propose a novel EE resource-allocation scheme for orthogonal frequency-division multiple-access (OFDMA) networks, where both transmitter energy consumption and receiver energy consumption are considered. In addition, different quality-of-service (QoS) requirements, including minimum-rate guarantee service and best effort service, are taken into account. The time slot, subcarrier (frequency), and power-allocation policies are jointly considered to optimize system EE. With all these considerations, the EE resource-allocation problem is formulated as a mixed combinatorial and nonconvex optimization problem, which is extremely difficult to solve. To reduce the computational complexity, we tackle this problem in three steps. First, for a given power allocation, we obtain the time-frequency resource unit (RU) allocation policy via binary quantum-behaved particle swarm optimization (BQPSO) algorithm. Second, under the assumption of known RU allocation, we transform the original optimization problem into an equivalent concave optimization problem and obtain the optimal power-allocation policy through the Lagrange dual approach. Third, an iteration algorithm is developed to obtain the joint time-frequency power-resource-allocation strategy. We validate the convergence and effectiveness of the proposed scheme by extensive simulations.

Relay Selection and Subcarrier-Pair Based Energy-Efficient Resource Allocation for Multirelay Cooperative OFDMA Networks

Energy-efficient resource allocation is investigated for a relay-based multiuser cooperation orthogonal frequency division multiple access (OFDMA) uplink system with amplify-and-forward (AF) protocol for all relays. The objective is to maximize the total energy efficiency (EE) of the uplink system with consideration of some practical limitations, such as the individual power constraint for the users and relays and the quality of service (QoS) for every user. We formulate an energy-efficient resource allocation problem that seeks joint optimization of subcarrier pairing, relay selection, subcarrier assignment, and power allocation. Unlike previous optimization throughput models, we transform the considered EE problem in fractional form into an equivalent optimal problem in subtractive form, which is solved by using dual decomposition and subgradient methods. To reduce computation costs, we propose two low-complexity suboptimal schemes. Numerical studies are conducted to evaluate the EE of the proposed algorithms.

Energy-Efficient Resource Allocation for Heterogeneous Cognitive Radio Networks with Femtocells

Both cognitive radio and femtocell have been considered as promising techniques in wireless networks. However, most of previous works are focused on spectrum sharing and interference avoidance, and the energy efficiency aspect is largely ignored. In this paper, we study the energy efficiency aspect of spectrum sharing and power allocation in heterogeneous cognitive radio networks with femtocells. To fully exploit the cognitive capability, we consider a wireless network architecture in which both the macrocell and the femtocell have the cognitive capability. We formulate the energy-efficient resource allocation problem in heterogeneous cognitive radio networks with femtocells as a Stackelberg game. A gradient based iteration algorithm is proposed to obtain the Stackelberg equilibrium solution to the energy-efficient resource allocation problem. Simulation results are presented to demonstrate the Stackelberg equilibrium is obtained by the proposed iteration algorithm and energy efficiency can be improved significantly in the proposed scheme.

Energy-efficient resource allocation for OFDMA-based two-way relay channel with physical-layer network coding

Physical-layer network coding (PNC) is a novel cooperative technique for two-way relay channel (TRC), where two users exchange information via intermediate relay node(s). On the other hand, the issue of green communications to reduce energy consumption has recently started to arouse much attention. This article studies the energy-efficient resource allocation problem for orthogonal frequency division multiple access (OFDMA)-based TRC with PNC. In particular, resource allocation for multi-user, multi-relay OFDMA systems is vital for the optimization of power allocation, relay selection and subcarrier assignment. By applying convex optimization techniques, an optimal resource allocation scheme is proposed to minimize total transmit power with required rates. Numerical simulations show that the proposed scheme provides diversity gain compared to the single relay network, and PNC gain relative to the TRC without PNC.

Energy-efficient power allocation in OFDM-based cognitive radio systems: A risk-return model

Efficient and reliable subcarrier power allocation in orthogonal frequency-division multiplexing (OFDM)-based cognitive radio networks is a challenging problem. Traditional waterfilling approach is inefficient for such networks due to the strict requirements on the interference generated to the primary users (PUs). In this paper, we present a solution to an energy-efficient resource allocation problem which maximizes the cognitive radio (i.e., secondary) link capacity taking into account the availability of the subcarriers (and hence the reliability of transmission by cognitive radios) and the limits on total interference generated to the PUs. We consider an energy-aware capacity expression by taking into account another factor called subcarrier availability. Optimizing such an expression saves valuable resources such as battery life by selectively allocating power to underutilized subcarriers. Based on a risk-return model, we formulate a convex optimization problem which incorporates a linear average rate loss function in the optimization objective to include the effect of subcarrier availability. Due to the complex structure of the optimal solution, we propose three suboptimal schemes, namely, the step-ladder, nulling, and scaling schemes. We compare the performances of optimal and suboptimal algorithms with the performance of a classical waterfilling scheme. We conclude that waterfilling, unable to satisfy the interference criterion, performs the worst amongst all the schemes considered in this paper.