Energy Efficiency Optimization Problem Research Articles

Energy Efficiency Optimization for Wireless Power Transfer Enabled Massive MIMO Systems With Hardware Impairments

This paper investigates the energy efficiency (EE) optimization for massive multiple-input multiple-output (MIMO) systems powered by wireless power transfer (WPT) with hardware impairments at sensor nodes (SNs). In the considered system, the SNs are first powered by the WPT from power beacon (PB). Then, the SNs use the harvested energy to transmit data to the base station (BS) with large scale of multiple antennas. Finally, the BS employs maximal-ratio combining (MRC) to detect the data symbols transmitted by the SNs. As the EE optimization problem is a non-convex problem which is difficult to solve directly. A lower bound approximation and variable substitution method are used to transform the EE maximization problem into a concave-linear fractional programming. Then, an energy efficient resource allocation algorithm that combines time and power allocation is proposed by fractional programming to maximize the EE of the system. Finally, simulation results are presented to show the effectiveness of the proposed algorithm and the impact of the hardware impairments on the system performance.

Energy Efficient Downlink Resource Allocation for D2D-Assisted Cellular Networks With Mobile Edge Caching

In this paper, a downlink device-to-device (D2D)-assisted cellular networks with mobile edge caching, where most popular video files are independently cached in D2D users and cellular base station (BS), are studied. In the considered system model, each user may obtain the requested video from the cache of BS or/and D2D users surrounding them. According to the different collaborative schemes of BS caching and D2D caching, it can be divided into two different resource allocation schemes. In the hybrid caching transmission scheme, users could adopt the BS caching mode or alternatively the D2D caching mode. In the joint caching transmission scheme, each user may obtain the requested files from the BS server and the adjacent D2D users, simultaneously. By taking the required data rate and the interference constraint into account, we formulate two joint resource allocation problems integrating link selection, channel allocation, and power control to maximize the system energy efficiency (EE). Leveraging on the Dinkelbach method, the EE optimization problems are transformed into mixed-integer nonlinear programming problems and can be decomposed into three subproblems: link selection, channel allocation, and power control. To solve these complicated problems, we propose two optimization algorithms that consist of a modified branch and bound method as well as Lagrange dual decomposition approach. The simulation results demonstrate the superiority of these two proposed algorithms in improving system throughput and EE compared with other algorithms.

Energy Efficiency Optimization for NOMA-Based Cognitive Radio With Energy Harvesting

Energy Efficiency (EE) is a significant problem for Cognitive Radio (CR) network. Recently, more interest has focused on EE optimization problem in wireless-powered communication. In conventional CR network, spectrum sensing and limited battery capacity may decrease system performance. In this article, a Non-Orthogonal Multiple Access (NOMA) system with Simultaneous Wireless Information and Power Transfer (SWIPT) for CR network is studied. The frame structure is designed with two subslots. In the downlink subslot, the Secondary Users (SUs) harvest wireless energy from Radio Frequency (RF) signals and sense the spectrum state simultaneously. In the uplink subslot, SUs transmit their independent information to Base Station (BS). Two modes are considered in this article: overlay network and underlay network. A CR-NOMA system model is presented and the approximate expressions of EE for two modes are obtained. Based on the subslot allocation, two optimization problems aiming to maximize EE are formulated. In the overlay network, the constraints are transmit power and total transmission slot. In the underlay network, instead of sensing the spectrum, SUs utilize the channel with primary user (PU) simultaneously. Thus, the constraints of interference threshold and channel gain of PU are also taken into considered. The proposed optimization problems can be regarded as nonlinear fractional programming. The Dinkelbach method is used to transform the nonlinear fractional programming problems into the parametric ones. Simulation results show that there indeed exists a best downlink subslot to maximize the EE of CR-NOMA networks.

Energy-Efficient Sensor Grouping for IEEE 802.11ah Networks With Max-Min Fairness Guarantees

For the large scale wireless networks, restricted access window (RAW) mechanism is a promising technique for realizing large-scale sensor access with the limited collision probability. In this paper, we are committed to designing the traffic distribution based sensor grouping scheme to balance the energy efficiency (EE) of different groups in the large scale access networks. Specifically, by adopting the Markov chain model, we formulate the optimization problem of max-min EE by taking into account traffic demands with even distribution of different all groups, but the formulated problem is an integer nonlinear programming (INLP) problem. In order to solve the INLP problem, we propose an optimal traffic grouping algorithm (OTGA) by utilizing the branch-and-bound method (BBM) to accommodate for the congestion level among groups. Though the traffic demands of each group can be obtained from the traffic grouping scheme, different combination of heterogeneous sensors can generate the same traffic demands, which make it difficult to find the optimal solution of sensor grouping from the proposed traffic grouping scheme. Furthermore, a heuristic traffic-sensor mapping algorithm (HTMA) is presented to make the traffic demands of each group appropriate. Thus, the proposed scheme can achieve a sub-optimal performance with the individual EE. The numerical results are provided to verify the effectiveness of the proposed schemes.

Outage-Constrained Secrecy Energy Efficiency Optimization for CRNs With Non-Linear Energy Harvesting

In this paper, we investigate a robust transmit design in underlay energy harvesting (EH) cognitive radio networks (CRNs), where the malicious energy receivers (ERs) can be treated as potential eavesdroppers (Eves) due to their ability of wiretapping the desired information. Considering a general case of statistical Eves' channel state information (CSI), to transmit signals in a secret and energy-efficient manner, an outage-constrained secrecy energy efficiency (OC-SEE) optimization problem is formulated to design transmit beamforming vectors. The original problem is non-convex and challenging to solve. To make it more tractable, we first rewrite this problem by semi-definite relaxation (SDR), and Bernstein-type inequality (BTI) approach is resorted to conservatively approximate the probabilistic constraints. Then, based on the fractional programming theory, an equivalent parametric reformulation is proposed to convert the original problem into a subtractive form. Furthermore, by introducing auxiliary variables, with the aid of Sphere Bounding method, the tractable solution of this OC-SEE maximization problem can be obtained through solving a two-stage optimization problem. Finally, numerical results are provided to validate the efficacy of the proposed design.

Energy Efficiency Optimization for Secure Transmission in MISO Cognitive Radio Network With Energy Harvesting

In this paper, we investigate different secrecy energy efficiency (SEE) optimization problems in a multiple-input single-output underlay cognitive radio (CR) network in the presence of an energy harvesting receiver. In particular, these energy efficient designs are developed with different assumptions of channels state information (CSI) at the transmitter, namely perfect CSI, statistical CSI and imperfect CSI with bounded channel uncertainties. In particular, the overarching objective here is to design a beamforming technique maximizing the SEE while satisfying all relevant constraints linked to interference and harvested energy between transmitters and receivers. We show that the original problems are non-convex and their solutions are intractable. By using a number of techniques, such as non-linear fractional programming and difference of concave (DC) functions, we reformulate the original problems so as to render them tractable. We then combine these techniques with the Dinkelbach’s algorithm to derive iterative algorithms to determine relevant beamforming vectors which lead to the SEE maximization. In doing this, we investigate the robust design with ellipsoidal bounded channel uncertainties, by mapping the original problem into a sequence of semidefinite programs by employing the semidefinite relaxation, non-linear fractional programming and S-procedure. Furthermore, we show that the maximum SEE can be achieved through a search algorithm in the single dimensional space. Numerical results, when compared with those obtained with existing techniques in the literature, show the effectiveness of the proposed designs for SEE maximization.

Energy Efficient Nano-Node Association and Resource Allocation for Hierarchical Nano-Communication Networks

Nano-communication networks operating in the Terahertz (THz) band are being developed quickly owing to the high bandwidth range. In this paper, we investigate energy efficient data transmission in hierarchical nano-communication networks with multiple inbody nano-nodes and micro-nodes. Considering the channel characteristic of the THz band, we formulate a weighted energy efficiency optimization problem for the joint design of nano-node association and resource allocation in such networks. By regarding nano-node association results as the network cluster configurations, the non-convex optimization problem is decomposed into a network clustering problem and a resource allocation problem. The network clustering problem is a combine optimization problem, whose optimal cluster configuration is derived using Markov approximation theory. Besides, the resource allocation problem, which is a mixed integer nonlinear programming problem, is solved by comprehensively using continuity relaxation theory and an improved dynamic programming method. An adaptive frequency selection and power control algorithm is given for nano-nodes to determine the optimal frequency and transmit power under a specific cluster configuration. To gain more insights into the obtained solutions, a joint network clustering and resource allocation algorithm is presented to accommodate hierarchical nano-communication networks to achieve the energy efficient data transmission. Simulation results validate the theoretical analysis of the proposed algorithms.

A UHF RFID-Based System for Children Tracking

Given the fact that roughly 800 000 children are reported missing in the United States every year, how to assist parents to track their children becomes an important problem. Even though many children tracking systems have been proposed, the high cost and energy limitation of locators are the stumbling blocks which limit the application of those systems. To address this challenge, we design a children tracking system based on RFID technology, where children carry RFID tags and the system is responsible for locating the children by aggregating the readings from the deployed readers. Noting the importance of localized processing for efficient children tracking, we further study how the locally available computing resource, such as the mobile devices carried by the park employees and visitors, can be utilized for service provisioning. Since mobile devices have limited energy, we study an energy efficiency optimization problem by jointly considering the resource allocation and user association. The formulated problem is solved by a dynamic updating matching approach. Through extensive simulations, we have demonstrated the effectiveness of our proposed solution.

Energy Modeling and Parameter Identification of Dual-Motor-Driven Belt Conveyors without Speed Sensors

The energy model of belt conveyors plays a key role in the energy efficiency optimization problem of belt conveyors. However, the existing energy models and parameter identification methods are mainly limited to single-motor-driven belt conveyors and require speed sensors. This paper will present an energy model and a parameter identification method for dual-motor-driven belt conveyors whose speed sensors are not available. Firstly, a new energy model of dual-motor-driven belt conveyors is established by combining the traditional energy model with the dynamic model of a dual-motor-driven system. Then, a parameter identification method based on an extended Kalman filtering algorithm and recursive least square approach is proposed. Finally, the feasibility and effectiveness of the method are demonstrated by simulation experiments.

Energy efficient power allocation approach to asymmetric analogue network coding with rate‐dependent circuit power

Energy efficiency (EE) plays an indispensable role in the battery constrained systems. In this study, the EE optimisation problem for asymmetric analogue network coding (ANC) protocol is analysed in a two-way relaying network, where the two sources have different transmission rate requirements. A generalised power consumption model is applied consisting of not only the transmit power and fixed circuit power, but also the most general rate-dependent dynamic circuit power, with which a power allocation problem is formulated to achieve the maximal EE constrained by the rate requirements as well as the total transmit power budget. By exploiting the two-layer optimisation method and non-linear fractional programming theorem, the original EE maximisation problem is solved in an efficient way. Specially, when the rate-dependent circuit power is modelled as the linear function of transmission rate, a closed-form solution is obtained. Through simulations, the proposed power allocation algorithm is verified and the impacts of some system parameters such as relay location, transmit power and transmission rate asymmetry are also provided to gain useful insights, which facilitates the energy-efficient design for the practical asymmetric ANC protocol.

MEETS: Maximal Energy Efficient Task Scheduling in Homogeneous Fog Networks

A homogeneous fog network is defined as a group of peer nodes with sharable computing and storage resources, as well as spare spectrum for node-to-node/device-to-device communications and task scheduling. It promotes more intelligent applications and services in different Internet of Things (IoT) scenarios, thanks to effective collaborations among neighboring fog nodes via cognitive spectrum access techniques. In this paper, a comprehensive analytical model that considers circuit, computation, offloading energy consumptions is developed for accurately evaluating the overall energy efficiency (EE) in homogeneous fog networks. With this model, the tradeoff relationship between performance gains and energy costs in collaborative task offloading is investigated, thus enabling us to formulate the EE optimization problem for future intelligent IoT applications with practical constraints in available computing resources at helper nodes and unused spectrum in neighboring environments. Based on rigorous mathematical analysis, a maximal energy-efficient task scheduling (MEETS) algorithm is proposed to derive the optimal scheduling decision for a task node and multiple neighboring helper nodes under feasible modulation schemes and time allocations. Extensive simulation results demonstrate the tradeoff relationship between EE and task scheduling performance in homogeneous fog networks. Compared with traditional task scheduling strategies, the proposed MEETS algorithm can achieve much better EE performance under different network parameters and service conditions.

Energy-spectral efficient resource allocation and power control in heterogeneous networks with D2D communication

Heterogeneous networks (HetNets) provide the demand for high data rates. In this study, we analyze the coexistence of femtocells and device-to-device (D2D) communication with macrocells. Interference management and decreasing energy consumption are two important issues in HetNets. To this end, we propose an efficient fractional frequency reuse (FFR)-based spectrum partitioning scheme to reduce the cross-tier interference. We also propose to use different optimization problems for resource allocation in different tiers. For this purpose, an energy efficient optimization problem is applied to D2D user equipment. Further, an optimization problem based on the spectral efficiency, i.e., throughput, is considered for macrocell and femtocell tiers. These problems are modeled as a non-cooperative game that results in low computational complexity. Iterative algorithms with fast convergence are used to solve the optimization problems. It is shown that applying different optimizations on different tiers leads to better performance than considering the same optimization for all tiers. The results indicate that the proposed FFR structure and optimization problems improve system performance. We also analyze the tradeoff between energy efficiency and spectral efficiency of the introduced structure.

Energy Efficiency Optimization With SWIPT in MIMO Broadcast Channels for Internet of Things

Simultaneous wireless information and power transfer (SWIPT) is anticipated to have great applications in 5G communication systems and the Internet of Things. In this paper, we address the energy efficiency (EE) optimization problem for SWIPT multiple-input multiple-output broadcast channel (BC) with time-switching (TS) receiver design. Our aim is to maximize the EE of the system whilst satisfying certain constraints in terms of maximum transmit power and minimum harvested energy per user. The coupling of the optimization variables, namely transmit covariance matrices and TS ratios, leads to an EE problem which is nonconvex, and hence very difficult to solve directly. Hence, we transform the original maximization problem with multiple constraints into a suboptimal min–max problem with a single constraint and multiple auxiliary variables. We propose a dual inner/outer layer resource allocation framework to tackle the problem. For the inner-layer, we invoke an extended SWIPT-based BC-multiple access channel (MAC) duality approach and provide two iterative resource allocation schemes under fixed auxiliary variables for solving the dual MAC problem. A subgradient searching scheme is then proposed for the outer-layer in order to obtain the optimal auxiliary variables. Numerical results confirm the effectiveness of the proposed algorithms and illustrate that significant performance gain in terms of EE can be achieved by adopting the proposed extended BC-MAC duality-based algorithm.

Spectral and Energy Efficiency Tradeoff for Massive MIMO

Achieving a good tradeoff between spectral efficiency (SE) and energy efficiency (EE) is important for the emerging wireless communication systems. Motivated by this, we study multiuser downlink beamforming in massive multiple-input multiple-output systems for maximizing a new metric of resource efficiency (RE), which is defined as a weighted combination of EE and SE. The new measure has more flexibility in striking the balance between SE and EE, but brings enormous difficulties in solving the resulting problem. To this end, we first investigate an uplink–downlink duality for the RE maximization. The conventional uplink–downlink duality only applies in the SE or the capacity region, and has yet not been explicitly established for the EE or the RE. In this paper, we prove with rigorous derivation that the duality has a more general form, which can be directly used to tackle the EE or RE beamforming optimization problem. Based on the duality, we then develop an optimization algorithm to realize spectral and energy efficient multiuser beamforming with either instantaneous or statistical channel state information (CSI). Numerical results finally verify that the design based on statistical CSI is able to asymptotically achieve the performance obtained with instantaneous CSI, but with much lower complexity.

Non-renewable energy efficiency optimization in energy harvesting relay-assisted system

In this paper, we consider a scenario where the source node powered by the conventional power grid transmits data with the assistance of some energy harvesting (EH) decode-and-forward (DF) relay nodes. Unlike EH providing renewable energy, most power grids are supported by the non-renewable fossil fuels. So our goal is to maximize the non-renewable energy efficiency (EE) of the non-EH source throughout multiple timeslots. To deal with the randomness of energy arrivals and non-ideal circuit power, we formulate the EE optimization problem of the relaying system by combining the power allocation and the sleep mode in both the source and the relay. We firstly deal with this problem in a special case with just one timeslot and then solve it in a multi-timeslot scenario with dynamic programming (DP) approach. We also propose a heuristic algorithm with a complexity lower than the DP algorithm. We compare the performance of the two algorithms with a baseline scheme. Simulation results show that the heuristic algorithm provides close EE with the DP algorithm, and a significant EE gain over the baseline scheme.

Energy Efficient Design of Massive MIMO by Considering the Effects of Nonlinear Amplifiers

Massive Multiple-Input Multiple-Output (MIMO) alludes to the theory of having a large number of transmitter chains at the base station, which in turn provides the higher spectral and energy efficiency with reduced radiated power and greater simplicity in the signal processing. In this paper, we have improved the energy efficiency of Massive MIMO by considering the effects of nonlinear amplifiers in each transmitter branch. We have designed the system by calculating the optimal number of transmitters and receivers with the optimal transmitted power and their corresponding spectral efficiency in terms of energy efficient prospective of Massive MIMO under both the perfect and imperfect channel conditions at different power consumption and area of coverage. We have evaluated the impacts of nonlinear amplifiers by calculating the energy efficiency at different efficiencies and distortion losses of nonlinear power amplifiers. In order to solve the optimization problem of energy efficiency, we have proposed an alternative optimization method which converges quickly and provides the optimal parameters under both the perfect and imperfect channel conditions.

Energy Efficiency Optimization in MIMO Interference Channels: A Successive Pseudoconvex Approximation Approach

In this paper, we consider the (global and sum) energy efficiency optimization problem in downlink multi-input multi-output multi-cell systems, where all users suffer from multi-user interference. This is a challenging problem due to several reasons: 1) it is a nonconvex fractional programming problem, 2) the transmission rate functions are characterized by (complex-valued) transmit covariance matrices, and 3) the processing-related power consumption may depend on the transmission rate. We tackle this problem by the successive pseudoconvex approximation approach, and we argue that pseudoconvex optimization plays a fundamental role in designing novel iterative algorithms, not only because every locally optimal point of a pseudoconvex optimization problem is also globally optimal, but also because a descent direction is easily obtained from every optimal point of a pseudoconvex optimization problem. The proposed algorithms have the following advantages: 1) fast convergence as the structure of the original optimization problem is preserved as much as possible in the approximate problem solved in each iteration, 2) easy implementation as each approximate problem is suitable for parallel computation and its solution has a closed-form expression, and 3) guaranteed convergence to a stationary point or a Karush-Kuhn-Tucker point. The advantages of the proposed algorithm are also illustrated numerically.

Energy efficiency optimization for SWIPT in K-user MIMO interference channels

In this paper, we investigate the energy efficiency (EE) for K-user multiple-input multiple-output (MIMO) interference channels with simultaneous wireless information and power transfer (SWIPT) and interference alignment (IA). Specifically, power splitting architecture is leveraged at each receiver for information decoding (ID) and energy harvesting simultaneously. Taking the quality of service (Qos) requirement and harvested power constraints into consideration, we maximize the system EE by jointly designing transmit beamformers, transmit power, power splitters and receive filters. To solve the nonconvex EE optimization problem, we first eliminate the interference among users for ID via the maximal signal to interference plus noise ratio (Max-SINR) IA method, which yields a two-dimensional EE optimization problem. And then, we propose an EE-Max algorithm coupled with the Max-SINR, nonlinear fraction programming and one dimension searching to solve the reformulated problem. Simulation results show the effectiveness of the proposed EE optimization algorithm and reveal the influence of the maximum transmit power constraint and the QoS requirement on the system EE.

Social-aware energy efficiency optimization for device-to-device communications in 5G networks

Mobile social networks and device-to-device (D2D) communications have emerged as promising techniques to support better local advanced services in 5G networks. Nevertheless, the integration of mobile social networks and D2D communications into 5G networks poses pivotal challenges such as how to exploit the social relationships of mobile users (MUs) and manage the interference and resources (i.e., spectrum and energy) in order to improve the performance of D2D communications. To this end, we propose a social-aware energy efficiency optimization solution for D2D communications in 5G networks. In particular, we first analyze and evaluate the influence of social relationships on the performance of D2D communications, which enable us to formulate the energy efficiency optimization (EEO) problem while carefully considering both the social relationships and physical interference between all the MUs. The EEO problem is then solved for optimal channel mode selection and optimal transmission powers allocated to each MU to maximize the energy efficiency, by utilizing adaptive genetic algorithm. Numerical results show that compared with social-unaware methods, our proposed solution can achieve significant improvement in terms of energy efficiency and system throughput while preserving the quality of service (QoS) for all users by taking into account the spectrum efficiency and transmission power constraints.

Energy Efficiency Optimization for OFDM Based 5G Wireless Networks With Simultaneous Wireless Information and Power Transfer

In 5G wireless networks, the system capacity and the number of users will be significantly increased. However, the energy consumed for the communication will also be increased, which results in low system energy efficiency. Simultaneous wireless information and power transfer (SWIPT) can enable the user to perform energy harvesting from the radio-frequency signals parallel with the information decoding, which can effectively improve the system energy efficiency. In this paper, we study the energy efficiency optimization problem for the orthogonal frequency-division multiplexing-based 5G wireless networks with SWIPT, in which the subcarrier and power allocation are jointly optimized to maximize the system energy efficiency for single user and multiple user cases. For those two cases, through mathematical transforming, we use the Dinkelbach iterative method and the Lagrange dual method to solve the non-convex energy efficiency optimization problem. Simulation results show that compared with other algorithms, our proposed algorithm can achieve higher energy efficiency.