Energy Efficiency Maximization Problem Research Articles

Energy-Efficient Optimal Power Allocation for SWIPT Based IoT-Enabled Smart Meter.

This paper presents an internet of things (IoTs) enabled smart meter with energy-efficient simultaneous wireless information and power transfer (SWIPT) for the wireless powered smart grid communication network. The SWIPT technique with energy harvesting (EH) is an attractive solution for prolonging the battery life of ultra-low power devices. The motivation for energy efficiency (EE) maximization is to increase the efficient use of energy and improve the battery life of the IoT devices embedded in smart meter. In the system model, the smart meter is equipped with an IoT device, which implements the SWIPT technique in power splitting (PS) mode. This paper aims at the EE maximization and considers the orthogonal frequency division multiplexing distributed antenna system (OFDM-DAS) for the smart meters in the downlink with IoT enabled PS-SWIPT system. The EE maximization is a nonlinear and non-convex optimization problem. We propose an optimal power allocation algorithm for the non-convex EE maximization problem by the Lagrange method and proportional fairness to optimal power allocation among smart meters. The proposed algorithm shows a clear advantage, where total power consumption is considered in the EE maximization with energy constraints. Furthermore, EE vs. spectral efficiency (SE) tradeoff is investigated. The results of our algorithm reveal that EE improves with EH requirements.

Energy-efficient cooperative spectrum sensing over imperfect reporting channels with collision constraint

Recently, energy-efficient cooperative spectrum sensing (CSS) has been studied extensively in cognitive radio networks (CRNs). Most studies assume that the occupancy state of the primary user (PU) remains constant during the transmission of secondary users (SUs). However, the PU could change its activity multiple times during the transmission period, resulting in collisions with each other. In this paper, the energy efficiency (EE) maximization problem for CRNs with CSS is examined by explicitly considering the collision probability of the PU state to transition while the SU transmits data. Specifically, two types of interference based on the sensing errors and PU re-occupancy as well as imperfect reporting channels are considered simultaneously. The goal is to achieve the maximum average EE of SUs while ensuring detection accuracy by jointly optimizing the number of collaborative SUs, frame duration and transmission power, subject to the constraints of collision probability, average transmission power, average interference power and data rate. To address the non-convex optimization problem, the optimal frame duration is obtained first based on the properties of collision probability. Then, based on Dinkelbach’s method, an energy-efficient CSS iterative power control algorithm is developed. Numerical results verify the effectiveness of the proposed algorithm. It is shown that the proposed algorithm is better to the existing schemes regarding to EE. The impacts of the PU traffic parameters, reporting errors, transmission power and rate constraints on the collision probability and EE are evaluated.

Power and Rate Adaptive Pushing Over Fading Channels

Proactive caching is capable of reducing access latency and improving network throughput, thereby attracting attention from both industry and academia. However, the energy efficiency (EE) of proactive caching over fading channels has not been well studied yet. In this paper, we aim at presenting an energy-efficient content pushing policy by carefully adapting the transmit rate and power in pushing and on-demand delivery phases, while assuring delivery delay constraints. To this end, the average delay, delay-outage probability, and EE are analyzed for a general adaptive pushing policy based on the saddle point approximation. According to these results, we formulate EE maximization problems under average delay and delay-outage constraints, respectively, for two special types of adaptive pushing policies, namely, opportunistic pushing and water-filling-based pushing. Due to the high complexity of the grid search for solving the formulated problems, suboptimal algorithms are presented based on gradient descent and golden section search methods. Moreover, we mathematically derive the scaling property and numerically obtain request probability and delivery delay thresholds for content pushing. Simulations show that the presented pushing policies achieve higher EE than on-demand transmissions, especially when the content item is popular and the tolerable delivery delay is small.

Multi-UAV Trajectory Planning for Energy-Efficient Content Coverage: A Decentralized Learning-Based Approach

In next-generation wireless networks, high-mobility unmanned aerial vehicles (UAVs) are promising to provide content coverage, where users can receive sufficient requested content within a given time. However, trajectory planning for multiple UAVs to provide content coverage is challenging since 1) UAVs cannot provide content coverage for all users due to the limited energy and caching storage, and 2) the trajectory planning of UAV is coupled with each other. Moreover, the complete information based trajectory planning methods are unusable since UAVs cannot obtain prior information on the rapidly changing environment. In this paper, we investigate the multi-UAV trajectory planning for energy-efficient content coverage. We first formulate an energy efficiency maximization problem considering recharging scheduling, which aims to reduce the total length of trajectories of UAVs under the quality of service (QoS) constraints. To settle environment uncertainty, the trajectory planning problem is modeled as two coupled multi-agent stochastic games, whose equilibrium constitute the optimal trajectory. To obtain the equilibrium, we propose a decentralized reinforcement learning algorithm, which can decouple the two games. We prove that the proposed algorithm can converge to the optimal solution of the Bellman equation with a higher rate compared to the centralized one. Moreover, simulation results show that the energy efficiency of the proposed algorithm is smaller than 5% compared the optimal, which is obtained with the prior information of environments.

Energy-Efficient Joint Base Station Switching and Power Allocation for Smart Grid Based Hybrid-Powered CoMP-Enabled HetNet

With the rapid proliferation of wireless traffic and the surge of various data-intensive applications, the energy consumption of wireless networks has tremendously increased in the last decade, which not only leads to more CO2 emission, but also results in higher operating expenditure. Consequently, energy efficiency (EE) has been regarded as an essential design criterion for future wireless networks. This paper investigates the problem of EE maximisation for a cooperative heterogeneous network (HetNet) powered by hybrid energy sources via joint base station (BS) switching (BS-Sw) and power allocation using combinatorial optimisation. The cooperation among the BSs is achieved through a coordinated multi-point (CoMP) technique. Next, to overcome the complexity of combinatorial optimisation, Lagrange dual decomposition is applied to solve the power allocation problem and a sub-optimal distance-based BS-Sw scheme is proposed. The main advantage of the distance-based BS-Sw is that the algorithm is tuning-free as it exploits two dynamic thresholds, which can automatically adapt to various user distributions and network deployment scenarios. The optimal binomial and random BS-Sw schemes are also studied to serve as benchmarks. Further, to solve the non-fractional programming component of the EE maximisation problem, a low-complexity and fast converging Dinkelbach’s method is proposed. Extensive simulations under various scenarios reveal that in terms of EE, the proposed joint distance-based BS-Sw and power allocation technique applied to the cooperative and harvesting BSs performs around 15–20% better than the non-cooperative and non-harvesting BSs and can achieve near-optimal performance compared to the optimal binomial method.

Energy efficient resource allocation in cache‐enabled fog networks

AbstractTo gain high energy efficiency and low latency, fog computing can be considered as a promising enabling technology for supporting future IoT networks. Exponentially increasing data rate demand of smart IoT devices in the fifth‐Generation (5G) networks require new resource management and power allocation schemes. Fog computing involves the deployment of data storage devices (fog nodes) near the proximity of IoT nodes. The fog nodes collaborate to process requests generated from users. These nodes can store data in their storage capacity and supports in achieving quality‐of‐service (QoS) requirements. A joint node association and energy efficiency maximization problem is formulated in this article. The proposed problem is a non‐linear concave programming problem under cache size, association, and optimal power allocation constraints. A mesh adaptive direct search algorithm (MADS) is used to solve the formulated problem to find the sub‐optimal results. The efficient performance of the algorithm can be seen in comparison with more complex algorithms namely, outer approximation algorithm and exhaustive search algorithm. Extensive simulations are done to observe the detailed performance analysis of the IoT‐Fog network. As a result, energy‐efficient resource allocation is done under cache and QoS constraints with very little complexity and low computational power and delays.

Reconfigurable Intelligent Surfaces for Energy Efficiency in Multicast Transmissions

Reconfigurable Intelligent surface (RIS), which can induce phase shift of the received signals and then forward them by its reflecting elements, possesses high energy efficiency (EE). This letter studies the EE for the RIS-assisted multicast communication. A multiple-antenna base station (BS) delivers common messages to a group of mobile users (MUs), The RIS is placed to maintain the transmission. First, this letter analyzes the equivalent multicast channel and total power consumption to formulate an EE maximization problem. Then, this letter proposes the functional algorithms by fractional programming and alternating optimization to obtain a locally optimal solution for this problem. Lastly, we show asymptotic behaviors of the EE when the numbers of reflecting elements, antennas, and MUs go to infinity.

Energy-Efficient Resource Allocation in Massive MIMO-NOMA Networks With Wireless Power Transfer: A Distributed ADMM Approach

In multicell massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) networks, base stations (BSs) with multiple antennas deliver their radio frequency energy in the downlink, and Internet-of-Things (IoT) devices use their harvested energy to support uplink data transmission. This paper investigates the energy efficiency (EE) problem for multicell massive MIMO NOMA networks with wireless power transfer (WPT). To maximize the EE of the network, we propose a novel joint power, time, antenna selection, and subcarrier resource allocation scheme, which can properly allocate the time for energy harvesting and data transmission. Both perfect and imperfect channel state information (CSI) are considered, and their corresponding EE performance is analyzed. Under quality-of-service (QoS) requirements, an EE maximization problem is formulated, which is non-trivial due to non-convexity. We first adopt nonlinear fraction programming methods to convert the problem to be convex, and then, develop a distributed alternating direction method of multipliers (ADMM)- based approach to solve the problem. Simulation results demonstrate that compared to alternative methods, the proposed algorithm can converge quickly within fewer iterations, and can achieve better EE performance.

Intelligent Reflecting Surface Enabled Random Rotations Scheme for the MISO Broadcast Channel

The current literature on intelligent reflecting surface (IRS) focuses on optimizing the IRS phase shifts to yield coherent beamforming gains, under the assumption of perfect channel state information (CSI) of individual IRS-assisted links, which is highly impractical. This work, instead, considers the random rotations scheme at the IRS in which the reflecting elements only employ random phase rotations without requiring any CSI. The only CSI then needed is at the base station (BS) of the overall channel to implement the beamforming transmission scheme. Under this framework, we derive the sum-rate scaling laws in the large number of users regime for the IRS-assisted multiple-input single-output (MISO) broadcast channel, with optimal dirty paper coding (DPC) scheme and the lower-complexity random beamforming (RBF) and deterministic beamforming (DBF) schemes at the BS. The random rotations scheme increases the sum-rate by exploiting multi-user diversity, but also compromises the gain to some extent due to correlation. Finally, energy efficiency maximization problems in terms of the number of BS antennas, IRS elements and transmit power are solved using the derived scaling laws. Simulation results show the proposed scheme to improve the sum-rate, with performance becoming close to that under coherent beamforming for a large number of users.

UAV-Aided Vehicular Communication Design With Vehicle Trajectory’s Prediction

This letter studies the optimization of a UAV-aided vehicular communication system where a rotary-wing UAV serves a moving vehicle. Considering the propulsion energy of the UAV, our goal is to maximize the energy efficiency (EE) of the system by optimizing UAV trajectory. Due to the causality of the vehicle location information (VLI), the formulated optimization problem is challenging to solve. To address the VLI causality issue, a vehicle's trajectory prediction framework is proposed, in which a short-term accurate trajectory prediction model and a long-term coarse trajectory prediction model are integrated. Based on the proposed vehicle's trajectory prediction framework, a novel receding horizon optimization method is proposed to transform the EE maximization problem into a sequence of optimization problems, in which a time window of the short-term vehicle's trajectory prediction is successively moved along the vehicle's trip. By solving the sequence of optimization problems, an energy-efficient UAV's trajectory optimization algorithm is obtained. Simulation results verify that our proposed scheme can achieve about 20%-40% EE performance gains over existing baseline schemes.

An Efficient Channel and Power Allocation Scheme for D2D Enabled Cellular Communication System: An IoT Application

The Device-to-device (D2D) networks bear a close resemblance to future Internet of Things (IoT) networks. Resource management is an important aspect for realization of D2D communication in upcoming IoT networks. In this article, we select underlay in-band D2D communication as it is more beneficial in terms of spectral efficiency even though it comes at the cost of interference with cellular communication. To deal with this difficulty, we propose a novel downlink resource sharing strategy, where multiple D2D users (D2Ds) share the resource of a single cellular user (CU), and each D2D pair reuses multiple channels from different cellular users (CUs). The proposed scheme adopts a channel selection technique, wherein multiple channels of CU can be shared by each D2D user. Furthermore, optimal power for each D2D user is determined through the Lagrangian dual optimization method. The formulated power control maximization scheme nicely balances the total transmission power of D2D and D2D sum-rate. The proposed channel and power allocation problem aims at maximizing the D2D sum-rate by increasing the number of active D2D links while preserving the quality of service (QoS) of CU. Finally, a relationship between energy efficiency (EE) and transmit power of D2D is investigated through an EE maximization problem. The overall system performance is evaluated in terms of the D2D shared ratio, throughput gain of the network, and computational complexity of the proposed optimal strategy. Further, the merits of using the proposed resource sharing scheme over the existing schemes are also verified through numerical results.

Beyond Cell-Free MIMO: Energy Efficient Reconfigurable Intelligent Surface Aided Cell-Free MIMO Communications

Cell-free systems can effectively eliminate the inter-cell interference by enabling multiple base stations (BSs) to cooperatively serve users without cell boundaries at the expense of high costs of hardware and power sources due to the large-scale deployment of BSs. To tackle this issue, the low-cost reconfigurable intelligent surface (RIS) can serve as a promising technique to improve the energy efficiency of cell-free systems. In this article, we consider an RIS aided cell-free MIMO system where multiple RISs are deployed around BSs and users to create favorable propagation conditions via reconfigurable reflections in a low-cost way, thereby enhancing cell-free MIMO communications. To maximize the energy efficiency, a hybrid beamforming (HBF) scheme consisting of the digital beamforming at BSs and the RIS-based analog beamforming is proposed. The energy efficiency maximization problem is formulated and an iterative algorithm is designed to solve this problem. The impact of the transmit power, the number of RIS, and the RIS size on energy efficiency are investigated. Both theoretical analysis and simulation results reveal that the optimal energy efficiency depends on the numbers of RISs and the RIS size. Numerical evaluations also show that the proposed system can achieve a higher energy efficiency than conventional ones.

Energy-Efficient Resource Allocation in C-RANs with Capacity-Limited Fronthaul

Cloud Radio Access Network (C-RAN) is a key architecture for 5G cellular wireless network that aims at improving spectral and energy efficiency of the network by uniting traditional RAN with cloud computing. In this paper, a novel resource allocation scheme that optimizes the network energy efficiency of a C-RAN is designed. First, an energy consumption model that characterizes the computation energy of the BaseBand Unit (BBU) is introduced based on empirical results collected from a programmable C-RAN testbed. Then, an optimization problem is formulated to maximize the energy efficiency of the network, subject to practical constraints including Quality of Service (QoS) requirement, radio remote head transmit power, and fronthaul capacity limits. The formulated Network Energy Efficiency Maximization (NEEM) problem jointly considers the tradeoff among the network accumulated data rate, BBU power consumption, fronthaul cost, and beamforming design. To deal with the non-convexity and mixed-integer nature of the problem, we utilize successive convex approximation methods to transform the original problem into the equivalent Weighted Sum-Rate (WSR) maximization problem. We then propose a provably-convergent iterative method to solve the resulting WSR problem. Extensive simulation results coupled with real-time experiments on a small-scale C-RAN testbed show the effectiveness of our proposed resource allocation scheme and its advantages over existing approaches.

Green computing in IoT: Time slotted simultaneous wireless information and power transfer

Simultaneous Wireless Information and Power Transfer (SWIPT) is an emerging field to transmit information and power in IoT network through the same RF signal. Time switching (TS) protocol is more favorable in free space communication than Power Splitting (PS) protocol when the transmitted RF signal is already weak. This is because, transmitted signal loses its power due to attenuation in free space, and using PS design receiver circuit (complex), the received weak signal is further split into two fraction for energy harvesting (EH) and information decoding (ID) simultaneously, that causes inadequacy in SWIPT system. Whereas, using TS design receiver circuit (simple) insert extra delay in the network as EH and ID operations are done in two different time domain one by one. Literature on SWIPT lacks towards cooperation between more energy harvesting in case of free space communication (TS) and critical information transmission in case of delay constraint communication (PS). In this context, this paper presents a time-slotted SWIPT (T-SWIPT) focusing on maximization of energy efficiency in the relay based sensors-enabled IoT network. It enables simultaneous energy harvesting at receiver and neighboring sensors without adding extra delay in the network. The PS ratio, transmission power allotment and energy broadcast time are jointly formulated as non-convex energy efficiency maximization problem. A solution to the problem is presented using Lagrangian dual decomposition and fractional programming. The performance evaluation shows that T-SWIPT attains optimum energy efficiency by trading off transmission power allotment, power-splitting ratio and sink broadcast time slot.

Energy Efficiency Maximization for Full Duplex MIMO Cloud Radio Access Networks

This paper studies a joint precoder and fronthaul compression design for full-duplex (FD) miltiple-input-multiple-output (MIMO) cloud radio access networks (CRANs). A cloud control unit (CU) communicates with multiple downlink and uplink users through FD radio units (RUs) connected to the CU through fronthaul links which are limited capacity. We address the energy efficiency (EE) maximization problem subject to the transmit power constraints at each RU, each user and the limited capacity of fronthaul links. Since the formulated design problem is a highly non-convex problem in design variables, we exploit a successive convex approximation (SCA) method to obtain the concave lower bound of the achievable sum rate and a convex upper bound of limited capacity fronthaul link functions. Then, we apply the Dinkelbach method to develop an efficient iterative algorithm guaranteeing convergence in which the convex optimization problems are solved. Numerical results are provided to investigate the EE of the proposed algorithm.

Green communication for OFDMA cellular networks with multiple antennas

The green communication which requires the maximum reduction of energy consumption has become a strong need for the new wireless system designs because of the massive increasing number of mobile subscriptions. Thus, in this paper, energy efficiency (EE) maximization problem is considered for multiple-input single-output orthogonal frequency division multiple access (MISO-OFDMA) cellular networks. The EE maximization problem is defined for a downlink communication model by considering the total transmit power and circuit power constraints. Quality of service (QoS) constraints which guarantee the minimum data rate requirement of the users are also accounted. The considered problem belongs to the category of mixed-integer non-linear programming problem (MINLP), and thus is NP-hard. To cope up with this complexity, a two-step resource allocation algorithm, which contains an heuristic subchannel allocation algorithm and the iterative power allocation algorithm with fast convergence based on the Dinkelbach method, is proposed. Numerical results are provided to demonstrate the effectiveness of the proposed algorithm and the convergence and energy efficiency performance of the proposed scheme is validated.

Promoting Energy Efficiency and Proportional Fairness in Densely Deployed Backscatter-Aided Networks

Recently, energy-efficient ambient backscatter communication has emerged as a promising technology to build up self-sustainable wireless networks. However, the limited transmission range and rate restrict the communication capability of a single backscatter node. To ensure network coverage and connectivity, the nodes in ambient backscatter-aided wireless networks (AmBWNs) have to be densely deployed. Unfortunately, the fickle and sporadic nature of the energy source and carrier in AmBWNs would lead to unreliable and unstable transmissions. We argue that the network throughput can be significantly improved if we can take full advantages of both stable active transmission and energy-saving backscattering. However, it is challenging to jointly determine nodes' transmission mode to enhance the throughput and energy efficiency. Moreover, the fairness in terms of traffic load at each node of AmBWNs is not thoroughly considered in the existing research. The improper load allocation would hinder the network throughput and even cause network partition if some bottleneck nodes deplete the energy. In this article, a novel metric is defined to measure the aggregate energy efficiency of neighboring nodes, capturing the beneficial interaction of active links and backscatter links. Subsequently, we address the aggregate energy efficiency maximization problem constrained by Gini threshold. The Gini coefficient is used to adjust the load in proportion to the delivery capacity of each node, achieving proportional fairness in the dense AmBWN. The simulation result shows that the well-designed metric can improve the energy efficiency by 11 times and the throughput by 86%.

Energy Efficient Power Allocation with Antenna Selection for Interference Alignment based MIMO-OFDM Cognitive Radio Networks

Energy efficient communications in cognitive radio (CR) networks has been of great interest. In order to address the power consumption problem and to maximise the energy efficiency (EE) in MIMO-OFDM underlay CR systems, power allocation (PA) and antenna selection (AS) algorithms are proposed. Interference alignment, which helps in mitigating the interference at primary and secondary receivers, is considered for setting-up the underlay CR scenario. The AS transmit is employed for the secondary users (SUs) on a per-subcarrier basis to improve their QoS. Further, to improve the QoS of both primary user (PU) and SUs, the AS is followed by energy efficient PA. Due to the fractional nature of EE, the EE maximisation problem is transformed into an equivalent parametric optimisation problem based on Dinkelbach method. Simulation results confirm that the proposed EE aware algorithms can improve the EE performance of both PU and SUs than the SE aware algorithms.

Energy efficient power allocation with antenna selection for interference alignment-based MIMO-OFDM cognitive radio networks

Energy efficient communications in cognitive radio (CR) networks has been of great interest. In order to address the power consumption problem and to maximise the energy efficiency (EE) in MIMO-OFDM underlay CR systems, power allocation (PA) and antenna selection (AS) algorithms are proposed. Interference alignment, which helps in mitigating the interference at primary and secondary receivers, is considered for setting-up the underlay CR scenario. The AS transmit is employed for the secondary users (SUs) on a per-subcarrier basis to improve their QoS. Further, to improve the QoS of both primary user (PU) and SUs, the AS is followed by energy efficient PA. Due to the fractional nature of EE, the EE maximisation problem is transformed into an equivalent parametric optimisation problem based on Dinkelbach method. Simulation results confirm that the proposed EE aware algorithms can improve the EE performance of both PU and SUs than the SE aware algorithms.

Learn From Optimal Energy-Efficiency Beamforming for SWIPT-Enabled Sensor Cloud System Based on DNN

With the explosive increase of the types and quantity of devices in wireless networks, it is necessary to design a fast and energy-efficient decision-making strategy for the resource allocation to maintain the efficient operation of the wireless system. However, traditional resource allocation strategies designed based on optimization methods are of high complexity and poor real-time performance, which are not conducive to online decision-making. In this paper, deep learning approaches are introduced to design the optimal beamforming for Sink nodes in the SWIPT-enabled Sensor-Cloud system. First, the energy-efficiency maximization problem is first formulated with a “return/pay” form. In order to realize the application of deep learning for problem solving, a high-dimensional solvable mathematical expression is transformed from the maximization problem, and then a SWIFT-WMMSE algorithm iteratively is designed to obtain the optimal beamforming vector. At the same time, the convergence of the SWIPT-WMMSE algorithm is proved. Second, the approachability of the neural network to approximate the SWIPT-WMMSE algorithm is discussed. Furtherly, based on the error propagation in DNN approaching process, the criteria for the DNN scale design is deduced, and the approximation to the SWIPT-WMMSE algorithm is realized through the training of DNN, which is operated as an alternative algorithm to solve the optimal beamforming vectors for Sink nodes. Finally, the simulation results verify the effectiveness of SWIPT-WMMSE algorithm and DNN model, as well as the approximation effect of DNN model and its advantages in improving the system performance, especially in the aspect of complexity reducing and time saving.