Energy-efficient Resource Allocation Problem Research Articles

Energy-Efficient Resource Allocation for D2D-Assisted Fog Computing

In this paper, we address the problem of energy-efficient resource allocation in a multi-device D2D-assisted fog computing scenario, where the goal is to minimize the total energy consumption subject to constraints on the transmit powers, computation resources and task processing times. The considered problem is non-convex and finding its global optimum is generally intractable; hence we propose two sub-optimal approaches to solve it. First, by investigating the relationship between the task processing time and the total energy consumption, we show how the original problem can be relaxed into a sequence of convex subproblems whose solutions can be efficiently obtained via standard algorithms. Second, to further reduce computational complexity, we propose a low-complexity heuristic resource allocation strategy which does not require calculating gradients and the Hessian matrices in the solution process. We also develop a lower bound on the total energy consumption for the considered task offloading scenario as a benchmark for comparison purpose. Computer simulations under a wide range of conditions and parameter settings show that both methods achieve a near-optimal solution in comparison to the lower bound.

Energy-efficient resource allocation in NOMA-integrated V2X networks

In Vehicle-to-Everything (V2X) communication networks, cellular Device-to-Device (D2D) communication can improve the spectrum efficiency, and non-orthogonal multiple access (NOMA) can further enhance the connection density. However, for the cellular D2D communications, the involved execution condition for NOMA may be affected by the additional interference introduced by cellular links in V2X. In this paper, we study the energy-efficient resource allocation problem in cellular D2D-aided V2X networks with NOMA. To efficiently meet the quality of service (QoS) requirements while taking into account the maximum performance from the user’s perspective, we are committed to maximizing the minimum energy efficiency (EE) of each matching link, by jointly optimizing power allocation and spectrum reusing at Vehicle-to-Infrastructure (V2I) links and cellular D2D-based Vehicle-to-Vehicle (V2V) links. Since this problem is mathematically mixed-integer and non-convex, we first develop a two-layer block coordinate descent (BCD) scheme to solve power allocation subproblem. Specifically, the optimal intra-group power allocation strategy is derived in the inner-layer. Then, Dinkelbach and concave–convex procedure (CCCP) are employed to tighten the lower bound of original problem in the outer-layer. Followed by this, we address the remaining spectrum sharing subproblem based on the principle of minimum EE maximization. Simulation results show that the proposed algorithm can achieve excellent performance and outperform the other benchmark schemes significantly.

Energy-Efficient Resource Allocation for Secure D2D Communications Underlaying UAV-Enabled Networks

In this paper, we investigate the energy-efficient resource allocation problem in device-to-device (D2D) communications underlaying unmanned aerial vehicle (UAV)-enabled networks. The UAV is deployed as a flying base station to communicate with wireless users in the presence of an eavesdropper in the cell. We consider two types of users: the ground users (GUs) served by the UAV and the D2D users that communicate directly with one another. Our aim is to maximize the total energy efficiency (TEE) of all D2D pairs while guaranteeing the quality of service (QoS) requirements and secrecy rates of all GUs and D2D users via joint power control and channel allocation. The considered TEE maximization problem is a mixed-integer nonlinear programming (MINLP) problem, which is difficult to solve. Therefore, we propose a method that consists of outer and inner loops. In the outer loop, Dinkelbach's algorithm is utilized to transform the original fractional programming problem into a subtractive form. In the inner loop, we employ the alternating optimization method and divide the equivalent optimization problem into two sub-problems: power allocation and channel allocation. Solving the two sub-problems directly using standard convex optimization software may have high complexity. Therefore, we also propose a low-complexity algorithm using the Lagrangian dual and Kuhn—Munkres algorithm to obtain the optimal power allocation in closed-form and the optimal channel allocation, respectively. Simulation results show that the proposed algorithm converges in a small number of iterations. Furthermore, the proposed approach shows its superior performance compared with other benchmark methods.

Ruin Theory for Energy-Efficient Resource Allocation in UAV-Assisted Cellular Networks

Unmanned aerial vehicles (UAVs) can provide an effective solution for improving the coverage, capacity, and the overall performance of terrestrial wireless cellular networks. In particular, UAV-assisted cellular networks can meet the stringent performance requirements of the fifth generation new radio (5G NR) applications. In this article, the problem of energy-efficient resource allocation in UAV-assisted cellular networks is studied under the reliability and latency constraints of 5G NR applications. The framework of ruin theory is employed to allow solar-powered UAVs to capture the dynamics of harvested and consumed energies. First, the surplus power of every UAV is modeled, and then it is used to compute the probability of ruin of the UAVs. The probability of ruin denotes the vulnerability of draining out the power of a UAV. Next, the probability of ruin is used for efficient user association with each UAV. Then, power allocation for 5G NR applications is performed to maximize the achievable network rate using the water-filling approach. Simulation results demonstrate that the proposed ruin-based scheme can enhance the flight duration up to 61% and the number of served users in a UAV flight by up to 58%, compared to a baseline SINR-based scheme.

Energy Efficiency Maximization in NOMA Enabled Backscatter Communications With QoS Guarantee

Energy efficiency (EE) is an important performance metric in communication systems. However, to the best of our knowledge, the energy-efficient resource allocation (RA) problem in non-orthogonal multiple access enabled backscatter communication networks (NOMA-BackComNet) comprehensively considering the user's quality of service (QoS) has not been investigated. In this letter, we present the first attempt to solve the EE-based RA problem for NOMA-BackComNet with QoS guarantee. The objective is to maximize the EE of users subject to the QoS requirements of users, the decoding order of successive interference cancellation and the reflection coefficient (RC) constraint, where the transmit power of the base station and the RC of the backscatter device are jointly optimized. To solve this non-convex problem, we develop a novel iteration algorithm by using Dinkelbach's method and the quadratic transformation approach. Simulation results verify the effectiveness of the proposed scheme in improving the EE by comparing it with the other schemes.

Energy-Efficient Resource Allocation in Fog Computing Networks With the Candidate Mechanism

Recently, a fog computing network that widely deploys fog nodes (FNs) at the edge of the network has been able to provide better communication performance and powerful computation support to the resource-limited Internet-of-Things (IoT) devices. In this article, we analyze the energy-efficient (EE) resource allocation problem in fog computing networks with the candidate FNs mechanism to ensure the network loading balance under the transmission performance constraints. In the scenario, the associated computation capability allocated to IoT devices from FNs is related to the historical energy consumption and the current energy consumption. The FN that reports nonzero computation capability is considered as the candidate FN and included in the candidate set. Moreover, a candidate FN-based EE resource allocation (CF-EE) algorithm is proposed to maximize network EE, which is converted into the Lyapunov optimization for each time slot. The optimal resource allocation can be obtained by minimizing the upper bound of the Lyapunov drift function and the penalty term to guarantee the loading balance and network stability. Finally, the optimization problem is decomposed into two suboptimization problems: 1) transmission resource allocation optimization and 2) power allocation optimization, and solved separately. The simulation results demonstrate that the proposed CF-EE algorithm can achieve a considerable performance improvement compared with algorithms in the literature.

Codebook-Based Max–Min Energy-Efficient Resource Allocation for Uplink mmWave MIMO-NOMA Systems

In this paper, we investigate the energy-efficient resource allocation problem in an uplink non-orthogonal multiple access (NOMA) millimeter wave system, where the fully-connected-based sparse radio frequency chain antenna structure is applied at the base station (BS). To relieve the pilot overhead for channel estimation, we propose a codebook-based analog beam design scheme, which only requires to obtain the equivalent channel gain. On this basis, users belonging to the same analog beam are served via NOMA. Meanwhile, an advanced NOMA decoding scheme is proposed by exploiting the global information available at the BS. Under predefined minimum rate and maximum transmit power constraints for each user, we formulate a max-min user energy efficiency (EE) optimization problem by jointly optimizing the detection matrix at the BS and transmit power at the users. We first transform the original fractional objective function into a subtractive one. Then, we propose a two-loop iterative algorithm to solve the reformulated problem. Specifically, the inner loop updates the detection matrix and transmit power iteratively, while the outer loop adopts the bi-section method. Meanwhile, to decrease the complexity of the inner loop, we propose a zero-forcing (ZF)-based iterative algorithm, where the detection matrix is designed via the ZF technique. Finally, simulation results show that the proposed schemes obtain a better performance in terms of spectral efficiency and EE than the conventional schemes.

Stackelberg game-based energy-efficient resource allocation for 5G cellular networks

To obtain better bandwidth and performance, the fifth generation (5G) cellular network is proposed to implement new-generation cellular mobile communications for new applications such as the internet of things, big data, smart city and so on. However, due to multiple/dense cellular network structures and high data rate, the 5G cellular network holds high inter-cell interference (ICI) and lower energy efficiency. The soft frequency reuse (SFR) is introduced to reduce the inter-cell interference in multiple cellular networks (such as 5G cellular networks) with the orthogonal frequency division multiplexing in base stations. Then, we investigate the energy-efficient resource allocation problem in the 5G cellular network with SFR. To coordinate the ICI among adjacent cells, we introduce the interference pricing factor into the utility function. The energy-efficient resource allocation problem is described as a Stackelberg game model. Because the sub-carrier assignment in the optimization process is an integer program which is very hard to be solved, we make a relaxation for the integer variable in the model and propose an iteration algorithm to obtain the Stackelberg game equilibrium solution. Simulation results show that the proposed method is feasible and promising.

Joint Energy Efficient Subchannel and Power Optimization for a Downlink NOMA Heterogeneous Network

Non-orthogonal multiple access (NOMA) has been considered as a key technology in the fifth-generation mobile communication networks due to its superior spectrum efficiency. Since the heterogeneous network has been emerged to satisfy users' explosive data rate requirements and large connectivity of mobile Internet, implementing NOMA policy in heterogeneous networks (HetNets) has become an inevitable trend to enhance the 5G system throughput and spectrum efficiency. In this paper, we aim to maximize the entire system energy efficiency, including the macrocell and small cells, in a NOMA HetNet via subchannel allocation and power allocation. By considering the co-channel interference and cross-tier interference, the energy efficient resource allocation problem is formulated as a mixed integer nonconvex optimization problem. It is challenging to obtain the optimal solution; therefore, a suboptimal algorithm is proposed to alternatively optimize the macrocell and the small cells resource allocation. Specifically, convex relaxation and dual-decomposition techniques are exploited to optimize the subchannel allocation and power allocation. Moreover, optimal closed-form power allocation expressions are derived for small cell and macrocell user equipments by the Lagrangian approach. Simulations results show that the proposed algorithms can converge within ten iterations and can also attain higher system energy efficiency than the reference schemes.

Distributed Resource Allocation for Energy Efficiency in OFDMA Multicell Networks With Wireless Power Transfer

In this paper, an energy-efficient resource allocation problem is investigated for the wireless power transfer (WPT)-enabled OFDMA multicell networks. In the considered system, multiple base stations (BSs) with a large number of antennas are responsible to provide WPT in the downlink, and the users can recycle and utilize the received energy for uplink data transmission. The role of BS is to execute WPT; thus, there are no data transmissions in the downlink. A time-division protocol is considered to divide the time of downlink WPT and uplink wireless information transfer into separate time slots. With the objective to improve the energy efficiency, we propose the time, subcarrier, and power allocation schemes and antenna selection algorithms. As the perfect channel state information (CSI) is hard to obtain in the practical systems, we also take the case where only estimated CSI is available into consideration when executing resources allocation decisions and analyze the corresponding performance. Due to the non-convexity of the formulated optimization problem, we first apply the nonlinear programming scheme to convert it to a convex optimization problem. Then, an efficient alternating direction method of multipliers-based distributed resource allocation algorithm is applied to address the transformed problem. Performance evaluations are conducted to demonstrate the advantages of the proposed schemes.

Energy Efficient Resource Allocation for UAV-Assisted Space-Air-Ground Internet of Remote Things Networks

Internet of remote things (IoRT) networks are regarded as an effective approach for providing services to smart devices, which are often remote and dispersed over in a wide area. Due to the fact that the ground base station deployment is difficult and the power consumption of smart devices is limited in IoRT networks, the hierarchical Space-Air-Ground architecture is very essential for these scenarios. This paper aims to investigate energy efficient resource allocation problem in a two-hop uplink communication for Space-Air-Ground Internet of remote things (SAG-IoRT) networks assisted with unmanned aerial vehicle (UAV) relays. In particular, the optimization goal of this paper is to maximize the system energy efficiency by jointly optimizing sub-channel selection, uplink transmission power control and UAV relays deployment. The optimization problem is a mix-integer non-linear non-convex programming, which is hard to tackle. Therefore, an iterative algorithm that combines two sub-problems is proposed to solve it. First, given UAV relays deployment position, the optimal sub-channel selection and power control policy are obtained by the Lagrangian dual decomposition method. Next, based on the obtained sub-channel allocation and power control policy, UAV relays deployment is obtained by successive convex approximation (SCA). These two sub-problems are alternatively optimized to obtain the maximum system energy efficiency. Numerical results verify that the proposed algorithm has at least 21.9% gain in system energy efficiency compared to the other benchmark scheme.

Energy efficient and fair resource allocation for LTE‐unlicensed uplink networks: A two‐sided matching approach with partial information

AbstractLong‐Term Evolution–unlicensed (LTE‐U) has recently attracted worldwide interest to meet the explosion in cellular traffic data. By using carrier aggregation, licensed and unlicensed bands are integrated to enhance transmission capacity while maintaining reliable and predictable performance. As there may exist other conventional unlicensed band users, such as WiFi users, LTE‐U users have to share the same unlicensed bands with them. Thus, an optimized resource allocation scheme to ensure the fairness between LTE‐U users and conventional unlicensed band users is critical for the deployment of LTE‐U networks. In this paper, we investigate an energy efficient resource allocation problem in LTE‐U coexisting with other wireless networks, which aims at guaranteeing fairness among the users of different radio access networks. We formulate the problem as a multiobjective optimization problem and propose a semidistributed matching framework with a partial information‐based algorithm to solve it. We demonstrate our contributions with simulations in which various network densities and traffic load levels are considered.

Super-Modular Game-Based User Scheduling and Power Allocation for Energy-Efficient NOMA Network

In this paper, we consider a single cell downlink non-orthogonal multiple access (NOMA) network and aim at maximizing the energy efficiency. The energy-efficient resource allocation problem is formulated as a non-convex and NP-hard problem. To decrease the computation complexity, we decouple the optimization problem as a subchannel matching scheme and power allocation subproblems. In the subchannel matching scheme, a non-cooperative game is applied to model this problem. To discuss the existence of Nash equilibrium (NE), we introduce a super-modular game and then design an algorithm to converge to the NE point. Moreover, a greed subchannel matching algorithm with low complexity is given through a two-way choice between users and subchannels. However, for given subchannel matching scheme, power allocation is still a non-convex problem, which is difficult to get the optimal solution. We then transform the non-convex problem to a convex problem by applying a successive convex approximation method. Afterward, we provide an algorithm to converge to suboptimal solution by solving a convex problem iteratively. Finally, simulation result demonstrates that the energy efficiency performance of the NOMA system is better than the orthogonal frequency division multiple access system.

Energy-Efficient Fast Configuration of Flexible Transponders and Grooming Switches in OFDM-Based Elastic Optical Networks

We investigate the problem of energy-efficient resource allocation constrained to quality of service and physical requirements in orthogonal frequency division multiplexing (OFDM)-based elastic optical networks and propose a fast two-stage algorithm to solve it. The first stage of the proposed algorithm deals with routing, traffic grooming, and traffic ordering and mainly aims at minimization of the number of deployed optical amplifiers and transponders. We provide an integer linear program for routing and traffic grooming and propose a heuristic procedure, which yields its near-optimum solution in a shorter runtime. In the second stage, we optimize transponder parameters to minimize total transponder power consumption. We show how signal-to-noise ratio and transponder power consumption are represented by convex expressions and use the results to provide a convex formulation for optimizing transponder parameters. Unlike the conventional formulations, we consider transmitted optical power as an optimization variable tuned for each lightpath and show how this improves power consumption of different network elements. Simulation results demonstrate that the proposed algorithm for routing and traffic grooming is around 2 orders of magnitude faster than its equivalent integer linear formulation and reduces network power consumption more than 9% compared with the scenario in which no traffic grooming is applied. It is also shown that our convex formulation for transponder parameter assignment is 1 order of magnitude faster than its mixed-integer nonlinear counterpart and reduces total transponder power consumption more than 13% compared with a fixed transponder configuration scheme. Furthermore, we investigate the effect of adaptive modulation assignment on power consumption and show it is not necessary to have complex transponders supporting a high number of modulation formats. We also analyze the impact of transponder capacity on traffic grooming and investigate the inherent trade-off between capital expenditures and operational expenditures in terms of the capacity and design complexity of the transponders.

Energy-Efficient Resource Allocation for Heterogeneous Wireless Network With Multi-Homed User Equipments

In this paper, we investigate the energy-efficient resource allocation problem for heterogeneous wireless network with multihomed user equipments. First, the energy-efficient resource allocation is formulated as an energy efficiency (EE) maximization problem, which is a mixed-integer nonlinear optimization (MINO) problem. We first introduce a continuity relaxation and Lagrange dual method to solve the MINO problem, which has relatively high computational complexity. For reducing the computational complexity of the resource allocation problem, we further propose a two-phase optimization method. Specifically, it includes the minimum rate guaranteed resource allocation and the energy-efficient resource allocation. Finally, we show that the two-phase optimization method achieves the suboptimal EE with significantly lower computational complexity compared with the optimal one. Simulation results show that the suboptimal algorithm achieves the EE performance higher than the conventional approach and comparable with the optimal one, but with much less complexity.

Energy efficient constrained shortest path first-based joint resource allocation and route selection for multi-hop CRNs

Cognitive radio networks (CRNs) are expected to improve spectrum utilization efficiently by allowing secondary users (SUs) to opportunistically access the licensed spectrum of primary users (PUs). In CRNs, source and destination SUs may achieve information interaction in an ad hoc manner. In the case that no direct transmission link between the SU transmission pairs is available, multi-hop relay SUs can be applied to forward information for the source and destination SUs, resulting in multi-hop CRNs. In this paper, we consider a multi-hop CRN consisting of multiple PUs, SU transmission pairs and relay SUs. Stressing the importance of transmission hops and the tradeoff between data rate and power consumption, we propose an energy efficient constrained shortest path first (CSPF)-based joint resource allocation and route selection algorithm, which consists of two sub-algorithms, i.e., CSPF-based route selection sub-algorithm and energy efficient resource allocation sub-algorithm. More specifically, we first apply CSPF-based route selection sub-algorithm to obtain the shortest candidate routes (SCRs) between the SU pair under the transmission constraints. Then, an energy efficient resource allocation problem of the SCRs is formulated and solved by applying iterative algorithm and Lagrange dual method. Simulation results demonstrate the effectiveness of the proposed algorithm.

QoS-Driven Resource Allocation and EE-Balancing for Multiuser Two-Way Amplify-and-Forward Relay Networks

In this paper, we study the problem of energy-efficient resource allocation in multiuser two-way amplify-and-forward (AF) relay networks with the aim of maximizing the energy efficiency (EE), while ensuring the quality-of-service (QoS) requirements and balancing the EE of the user links. We formulate an EE-balancing optimization problem that maximizes the ratio of the spectral efficiency (SE) over the total power dissipation subject to QoS and a limited transmit power constraints. The problem which maximizes the EE by jointly optimizing the subcarrier pairing, power allocation, and subcarrier allocation, turns out to be a non-convex fractional mixed-integer nonlinear programming problem, which has an intractable complexity in general. We apply a concave lower bound on the achievable sum rate and a series of convex transformations to make the problem convex one and propose an iterative algorithm for iteratively tightening the lower bound and finding the optimal solution through dual decomposition approach. In addition, a low-complexity suboptimal algorithm is investigated. We then characterize the impact of various network parameters on the attainable EE and SE of the network employing both EE maximization and SE maximization algorithms when the network is designed from the EE perspective. Simulation results demonstrate the effectiveness of the proposed algorithms.

Energy Efficiency of Confidential Multi-Antenna Systems With Artificial Noise and Statistical CSI

The problem of energy-efficient resource allocation in multiple-antenna wiretap channels is investigated, wherein a malicious user tries to eavesdrop the communication between two legitimate users. The use of artificial noise (AN) in combination with different statistical channel state information scenarios at the legitimate transmitter is considered. Unlike most previous related papers, the goal of the resource allocation is to maximize the amount of bits which can be reliably and confidentially transmitted per Joule of consumed energy. This leads to the maximization of the ratio between the system secrecy capacity and consumed power, a metric which we label secrecy energy efficiency (SEE) . The resulting nonconvex maximization problems are tackled by means of the fractional programing and sequential convex optimization tools. The resulting algorithm monotonically increases the objective value, and upon convergence, yields a first-order optimal solution of the problem with a polynomial complexity. Moreover, the impact of using the AN technique on the energy consumption due to digital signal processing operations is explicitly accounted for, providing insight as to when AN is beneficial from an energy-efficient perspective, too. Numerical results show the merits of the proposed algorithms, also showing that AN does not always improve the system SEE, depending on the digital signal processor used to compute the resource allocation.

Proportional-fair energy-efficient radio resource allocation for OFDMA smallcell networks

This paper investigates the energy-efficient radio resource allocation problem of the uplink smallcell networks. Different from the existing literatures which focus on improving the energy efficiency (EE) or providing fairness measured by data rates, this paper aims to provide fairness guarantee in terms of EE and achieve EE-based proportional fairness among all users in smallcell networks. Specifically, EE-based global proportional fairness utility optimization problem is formulated, taking into account each user's quality of service, and the cross-tier interference limitation to ensure the macrocell transmission. Instead of dealing with the problem in forms of sum of logarithms directly, the problem is transformed into a form of sum of ratios firstly. Then, a two-step scheme which solves the subchannel and power allocation separately is adopted, and the corresponding subchannel allocation algorithm and power allocation algorithm are devised, respectively. The subchannel allocation algorithm is heuristic, but can achieve close-to-optimal performance with much lower complexity. The power allocation scheme is optimal, and is derived based on a novel method which can solve the sum of ratios problems efficiently. Numerical results verify the effectiveness of the proposed algorithms, especially the capability of EE fairness provisioning. Specifically, it is suggested that the proposed algorithms can improve the fairness level among smallcell users by 150---400 % compared to the existing algorithms.

Energy-Efficient Resource Allocation for Downlink Non-Orthogonal Multiple Access Network

Non-orthogonal multiple access (NOMA) is a promising technique for the fifth generation mobile communication due to its high spectral efficiency. By applying superposition coding and successive interference cancellation techniques at the receiver, multiple users can be multiplexed on the same subchannel in NOMA systems. Previous works focus on subchannel assignment and power allocation to achieve the maximization of sum rate; however, the energy-efficient resource allocation problem has not been well studied for NOMA systems. In this paper, we aim to optimize subchannel assignment and power allocation to maximize the energy efficiency for the downlink NOMA network. Assuming perfect knowledge of the channel state information at base station, we propose a low-complexity suboptimal algorithm, which includes energy-efficient subchannel assignment and power proportional factors determination for subchannel multiplexed users. We also propose a novel power allocation across subchannels to further maximize energy efficiency. Since both optimization problems are non-convex, difference of convex programming is used to transform and approximate the original non-convex problems to convex optimization problems. Solutions to the resulting optimization problems can be obtained by solving the convex sub-problems iteratively. Simulation results show that the NOMA system equipped with the proposed algorithms yields much better sum rate and energy efficiency performance than the conventional orthogonal frequency division multiple access scheme.