Energy Efficiency Optimization Algorithm Research Articles

Comprehensive energy efficiency optimization algorithm for steel load considering network reconstruction and demand response

Industrial loads are usually energy intensive and inefficient. The optimization of energy efficiency management in steel plants is still in the early stage of development. Considering the topology of power grid, it is an urgent problem to improve the operation economy and load side energy efficiency of steel plants. In this paper, a two-level collaborative optimization method is proposed, which takes into account the dynamic reconstruction cost, transmission loss cost, energy cost and demand response benefit. The upper level objective is the optimization of topology in the grid structure to optimize the power loss and dynamic reconstruction costs of the grid. The lower level is the energy cost considering demand response, real time price and dynamic demand response price. Firstly, the mathematical models of stable load, impact load and the steel production line load are built. The key parameters are identified by the Back Propagation neural network algorithm according to the actual production data. Secondly, considering the constraints of grid structure and load operation capacity, the impact of dynamic grid loss and real-time dynamic electricity price on the energy efficiency of the whole grid are analyzed in depth. The optimal operation model considering the dynamic reconfiguration and grid tramission loss of distribution network is built. Taking a steel plant park in Northeast China as an example, it is proved that the optimization model can improve energy efficiency on the load side by optimizing energy consumption and demand response participation time on load side. The energy cost is reduced by 17.77% on the load side, the network loss is reduced by 1.8%, and the operating cost of the power grid is reduced by 26.2%, which has a positive effect on improving energy utilization efficiency, reducing distribution network loss, and improving overall economic efficiency.

Energy efficiency optimization algorithm based on RIS and D2D for multi-user MIMO system

Reconfigurable Intelligent Surface (RIS) is composed of reconfigurable passive components, which can realize on-demand customization of wireless communication environment. Device-to-Device (D2D) communication is one of the essential technologies to improve the capacity and spectrum efficiency of network system. In this paper, we propose an energy efficiency optimization algorithm based on RIS and D2D assisted multi-user MIMO system. The purpose is to maximize the energy efficiency of the system by jointly optimizing subcarrier allocation, power allocation and passive beamforming at RIS. We first formulate the energy efficiency maximization problem with the constraints of different user rate, power, subcarrier allocation and phase. Since it is a non-convex problem, we use an alternating optimization (AO) algorithm to decouple it into three sub-problems. The solutions of these three sub-problems are solved by subcarrier allocation based on channel strength, SCA algorithm, Dinkelbach algorithm and Riemannian gradient algorithm respectively. Simulation results show that the proposed scheme can significantly improve the energy efficiency.

An Energy-Efficient Optimization Method for High-Speed Rail Communication Systems Assisted by Intelligent Reflecting Surfaces (IRS)

This paper proposes an intelligent reflecting surface (IRS)-assisted energy efficiency optimization algorithm to address the problem of energy efficiency (EE) degradation in high-speed rail communication systems caused by line-of-sight link blockages between base stations and trains. The joint optimization of base station beamforming and IRS phase shifts is formulated as a variable-coupled energy efficiency maximization problem, subject to the base station’s transmission power and the IRS unit’s modulus constraints. This is known to be an NP-hard problem, making it challenging to obtain the global optimal solution. To tackle the issue of optimization variable coupling, an alternating optimization is employed to decompose the original problem into two sub-problems: base station beamforming and IRS phase-shift optimization. The Dinkelbach method is utilized to convert the fractional objective function into a difference form; then, the successive convex approximation (SCA) algorithm is applied to transform non-convex constraints into convex ones, which are solved using CVX. The Riemann conjugate gradient (RCG) algorithm can effectively solve the difficult unit module constraint. Finally, an alternating iterative strategy is employed to converge to a suboptimal solution. Our simulation results demonstrate that the proposed algorithm significantly enhances system efficiency with low computational complexity. Specifically, when the number of IRS reflecting elements is 64, the system’s EE is improved by approximately 12.41%, 35.26%, and 37.96% compared to the semi-definite relaxation algorithm, the random phase shift approach, and no IRS scheme, respectively.

Energy Efficiency and Delay Optimization of Virtual Slicing of Fog Radio Access Network

To develop the energy efficient of 6G networks, the fog radio access network (F-RAN) is expected to meet various use cases of high-performance mobile services. Although network virtualization greatly enhances the flexibility to accommodate various services, the interaction and optimization between delay and energy efficiency (EE) in virtual slicing (VS)-based F-RAN have not been well studied. To accomplish the EE and delay optimization of VS of F-RAN. The key technical challenges lie in the construction of new network architecture, the integration and optimization of radio, caching, and computing 3-D resources, reducing the algorithm’s complexity, and simulation verification. We first design a novel network architecture based on VS and fog computing. The VS method in F-RAN to embed VS assembles virtual radio, caching, and computing resources into physical substrates, transforming nonconvex problems into convex optimization problems by transforming constraints and using Lyapunov optimization methods. We further propose a virtual resource allocation optimization algorithm based on EE. To reduce the complexity of the algorithm, a low-complexity EE optimization algorithm is further proposed for virtual resource allocation. The simulation results show that the low-complexity virtual resource allocation EE optimization algorithm proposed in this article has better performance than the existing fog access network resource allocation methods. The EE is further improved by about 30% under the condition that the guaranteed delay threshold is two slots (i.e., 1 ms).

The Energy Efficiency of Heterogeneous Cellular Networks Based on the Poisson Hole Process

In order to decrease energy consumption caused by the dense deployment of pico base stations (PBSs) in heterogeneous cellular networks (HetNets), this paper first analyzes the energy efficiency (EE) of two-tier HetNets and then proposes a method to maximize the network EE by adjusting the PBS transmit power. The two-tier HetNets are modeled by the Poisson point process (PPP) and the Poisson hole process (PHP), and then the coverage probability of the macro base station (MBS) and the PBS in the two-tier HetNets is derived based on the mean interference to signal ratio (MISR). According to the user association probability, the coverage probability of the PPP-PHP HetNets is obtained. Then, the tractable expression of the average achievable rate is deduced on the basis of the relationship between the coverage probability and the average achievable rate. Finally, the expression of EE is derived and the EE optimization algorithm is proposed based on the PBS transmit power. The simulation results show that the PPP-PHP network is superior to the PPP-PPP network in terms of coverage probability and EE, and the network EE can be effectively improved by setting an appropriate PBS transmit power.

Optimal topology control of monitoring sensor network based on physical layer security for smart photovoltaic power system

In order to realize the intelligent management of photovoltaic power generation system, wireless sensor network (WSN) is considered as a promised solution. However, the energy of sensor nodes in WSN is limited and the security is not guaranteed. In order to prolong the service life of WSN and to improve the security of monitoring, this paper comprehensively considers the factors those affect the security and efficiency of WSN, such as transmission power, sensor association and the choice of Cluster Head (CH). Therefore, this paper proposes a joint sensor secure rate and energy efficiency optimization algorithm to obtain the optimal topology and transmission power design. Then, an effective iterative algorithm based on Block Coordinate Descent (BCD) technology is proposed to obtain the optimal solution to minimize the energy consumption of WSN and maximize the secure rate of sensor networks, thus ensuring the security and reliability of monitoring. Sensor association is modeled as a 0–1 multi-knapsack optimization problem. Different complexity methods are implemented to solve this NP-hard problem, and their performance are compared with simulations.

Decision Analysis on IoV Routing Transmission and Energy Efficiency Optimization Algorithm with AmBC

Decision Analysis on IoV Routing Transmission and Energy Efficiency Optimization Algorithm with AmBC

Energy-Efficient Optimization Algorithm in NOMA-Based UAV-Assisted Data Collection Systems

In order to relieve the traffic burden of the ground base station (BS) and improve system operation efficiency, we study the energy-efficient optimization problem in a non-orthogonal multiple access (NOMA)-based unmanned aerial vehicle (UAV)-assisted data collection system, where a UAV assists a ground BS for traffic offloading. Particularly, the joint optimization problem of the placement of the UAV and transmit power of the sensor nodes is formulated to maximize the energy efficiency of all sensor nodes under the quality-of-service constraints and the probabilistic ground-to-air channel model. To balance the optimized performance and online operation time, we propose an alternating-based offline optimization algorithm for obtaining the optimal online UAV trajectory policy. Under the given UAV placement, the formulated problem is first transformed into a convex power allocation subproblem. Based on the power-allocation solutions, the deep deterministic policy gradient algorithm is then leveraged to approach the optimal UAV placement. Simulations show that the proposed algorithm can obtain an efficient performance while consuming an average online operation time of fewer than 0.2 seconds.

UAV-assisted cooperative offloading energy efficiency system for mobile edge computing

Reliable communication and intensive computing power cannot be provided effectively by temporary hot spots in disaster areas and complex terrain ground infrastructure. Mitigating this has greatly developed the application and integration of UAV and Mobile Edge Computing (MEC) to the Internet of Things (IoT). However, problems such as multi-user and huge data flow in large areas, which contradict the reality that a single UAV is constrained by limited computing power, still exist. Due to allowing UAV collaboration to accomplish complex tasks, cooperative task offloading between multiple UAVs must meet the interdependence of tasks and realize parallel processing, which reduces the computing power consumption and endurance pressure of terminals. Considering the computing requirements of the user terminal, delay constraint of a computing task, energy constraint, and safe distance of UAV, we constructed a UAV-Assisted cooperative offloading energy efficiency system for mobile edge computing to minimize user terminal energy consumption. However, the resulting optimization problem is originally nonconvex and thus, difficult to solve optimally. To tackle this problem, we developed an energy efficiency optimization algorithm using Block Coordinate Descent (BCD) that decomposes the problem into three convex subproblems. Furthermore, we jointly optimized the number of local computing tasks, number of computing offloaded tasks, trajectories of UAV, and offloading matching relationship between multi-UAVs and multiuser terminals. Simulation results show that the proposed approach is suitable for different channel conditions and significantly saves the user terminal energy consumption compared with other benchmark schemes.

Energy Efficiency Analysis of e-Commerce Customer Management System Based on Mobile Edge Computing

Energy efficiency optimization of mobile edge computing e-commerce clients and reasonable management of server computing resources are worth further study. The participant of the algorithm game model proposed in this paper is mobile e-commerce customer management. The decision space is a two-dimensional space composed of unloading decision and power control, and the benefit function is the energy efficiency function and delay function. The existence and uniqueness of the multidimensional game model are proved theoretically. The simulation results show that the proposed multidimensional game based energy efficiency optimization algorithm of mobile edge computing can reduce the energy consumption and delay of mobile terminals and improve the energy efficiency of unloading calculation under the same task compared with the game scheme without considering power consumption control when the number of e-commerce customer management is larger. This paper deduces the optimal load migration decision of mobile e-commerce customer management and the optimal pricing strategy of mobile edge cloud service providers and proves that the optimal decision and optimal pricing constitute the Starkberg equilibrium. The semidistributed and decentralized task transfer decision-making mechanisms are designed, respectively, and the management decision-making behaviors of mobile e-commerce customers in the mobile edge cloud energy trading market are studied by numerical analysis, as well as the time efficiency of the two mechanisms.

REO: A Reliable and Energy Efficient Optimization Algorithm for Beacon-Enabled 802.15.4–Based Wireless Body Area Networks

Nowadays, due to the advancements in microelectronics and wireless communications, Wireless Body Area Networks (WBANs) have gained significant attention. They have various application fields including telemedicine, military, sports, and entertainment, which require contradictory Quality of Service (QoS) in terms of reliability, delay, energy, and simplicity. Medium Access Control (MAC) protocols play an essential role in finding a solution to these tradeoffs. Many ongoing research activities, primarily based on the low power IEEE 802.15.4 standard, tried to improve the WBAN performance by MAC parameter adjustment but, to the authors' knowledge, none were able to properly satisfy delay and reliability requirements. In this paper, a numerical MAC optimization algorithm is proposed for the Beacon-enabled IEEE 802.15.4-based WBANs, optimizing the delay, reliability, and energy tradeoffs based on the existing traffic and QoS requirements. The simulation results show that in comparison with similar works, the energy efficiency and reliability are improved by at least 14% and 25%, respectively while reducing the delay substantially.

Fuzzy Decoupling Energy Efficiency Optimization Algorithm in Cloud Computing Environment

With the vigorous development of information technology, cloud computing, as a distributed computing technology, has become a research hotspot in the industry. The cloud computing system has a huge resource pool. In order to meet user-specific quality of service requests, it needs to perform reasonable scheduling of various tasks. Under the premise of ensuring high computing performance and better service quality in the cloud computing environment, system energy efficiency optimization has become a key issue to be promoted in the promotion of cloud computing. The research purpose of this paper is to study the fuzzy decoupling energy efficiency optimization algorithm in cloud computing environment. This paper designs a fuzzy decoupling energy efficiency optimization scheme.

Energy Efficient Resource Allocation in Cache Based Terahertz Vehicular Networks: A Mean-Field Game Approach

Terahertz (THz) frequency band has become one of the popular researches for the short-range broadband, high-rate data communication, reliability, and strong anti-interference ability, which can realize better performance for dense vehicular network scenario. In this paper, we introduce a cache based dense vehicular networks with THz frequency link and focus on the resource allocation optimization problem of subband and power allocation by maximizing energy efficiency (EE). In order to reduce the computational complexity, the original optimization problem is formulated as a mean-field game (MFG) problem, where each cache access point (cAP) can solve its local EE optimization problem by Dinkelbach-style method and Lagrangian dual method. Different access cells interact with MFG through mean subband power. Then a global MFG based EE optimization algorithm is proposed to associate the local strategy of each cell, which guarantee the globality of the optimized solutions. The numerical results suggest the effectiveness of the proposed scheme.

Energy Efficient Resource Allocation in Millimeter-Wave D2D Enabled 5G Cellular Networks

The current trend has seen the data capacity and traffic density increase due to the increased demand for multimedia services. Since this cannot be handled successfully by the current 4G networks, there is a need to integrate the mmWave and the Device-to-Device (D2D) communication 5G technologies to meet this increased demand and traffic density. However, there is the challenge of increased interference between dense D2D users and cellular users if D2D users are allowed to reuse the resources allocated to cellular users. This degrades the performance of the D2D users in terms of achievable data rate and Energy Efficiency (EE). The paper formulates a match theoretic resource allocation scheme to maximize the achievable D2D sum rate. In addition, an EE optimization problem is formulated for D2D users by considering the rate and power constraints. The EE optimization problem is solved by the Lagrangian dual decomposition method. The algorithms were simulated in MATLAB and the results were compared to Hungarian and heuristic optimization algorithms. The results showed that the match theoretic resource allocation is on average 1.82 times better than the Hungarian algorithm. At the same time, the match theoretic resource allocation algorithm increases fairness in resource allocation as it maintains a higher sum rate for low and high-density number of users. The proposed EE optimization algorithm improved the D2D performance by 8.2% compared to the heuristic algorithm.

Energy efficiency optimization algorithm for heterogeneous NOMA network based on imperfect CSI

Energy efficiency optimization algorithm for heterogeneous NOMA network based on imperfect CSI

Energy Efficiency Optimization Algorithm of CR-NOMA System Based on SWIPT

This paper proposes a system EE (energy efficiency) optimization algorithm based on OPS (On-off Power Splitting) strategy for SWIPT (Simultaneous Wireless Information and Power Transfer) two-way relay assisted CR-NOMA (Cognitive Radio Non-Orthogonal Multiple Access) network. The system capacity expression of the secondary users with the OPS strategy is derived. Under the constraints of harvested energy and quality of service (QoS) of the users, the optimization problem with the goal of maximizing energy efficiency is constructed. In this paper, the NP-Hard problem is transformed into three subproblems about relay power, NOMA coefficient, and segmentation coefficient, which are solved by golden section algorithm, monotonicity decision function, and genetic algorithm. The simulation results show that compared with the PS (Power Splitting) and TS (Time Switching) strategies, the OPS strategy can significantly improve the transmission energy efficiency of the system.

Robust Energy Efficiency Optimization Algorithm for Health Monitoring System With Wireless Body Area Networks

The electronic health monitoring system, with wireless body area networks (WBANs), is a new technological paradigm that helps early detection of any abnormal physiological signs in a patient’s body. In this letter, an optimization problem is formulated to optimize the energy efficiency (EE) of WBAN without requiring the channel state information (CSI) from the transmitting sensor nodes to the aggregator. We utilize a generalized gamma distribution that supports various patient conditions during daily life activities and can efficiently model both everyday and dynamic activities. The optimization problem aims to optimize each sensor transmit power and encoding rate to minimize the EE (measured in J/bits) by considering outage probability and packet retransmission. It is shown that the formulated optimization problem is semi-strictly quasi-convex in each decision variable, and an alternative optimization approach is proposed to determine its solution. The simulation results show that the proposed algorithm is 30% more energy efficient when compared to sub-optimal solution with a constant encoding rate and transmit power of 5 dB, and 0.6 mW, respectively.

A Novel Access Control and Energy-Saving Resource Allocation Scheme for D2D Communication in 5G Networks

This paper investigates access link control and resource allocation for the device-to-device (D2D) communication in the fifth generation (5G) cellular networks. The optimization objective of this problem is to maximize the number of admitted D2D links and minimize the total power consumption of D2D links under the condition of meeting the minimum transmission rate requirements of D2D links and common cellular links. This problem is a two-stage nondeterministic polynomial (NP) problem, the solving process of which is very complex. So, we transform it into a one-stage optimization problem. According to the monotonicity of objective function and constraint conditions, a monotone optimization problem is established, which is solved by reverse polyblock approximation algorithm. In order to reduce the complexity of this algorithm, a solution algorithm based on iterative convex optimization is proposed. Simulation results show that both algorithms can maximize the number of admitted D2D links and minimize the total power consumption of D2D links. The proposed two algorithms are better than the energy efficiency optimization algorithm.

Distributed Energy Efficiency Optimization for Multi-User Cognitive Radio Networks Over MIMO Interference Channels: A Non-Cooperative Game Approach

Energy efficiency (EE) optimization is investigated for a multi-user cognitive radio network (CRN) over multiple-input-multiple-output (MIMO) interference channels (ICs). To reduce the system overhead due to information exchange among the secondary CR uses (SUs), the EE optimization problem is formulated as a non-cooperative game, where each SU transmitter competes against the other SU pairs by optimizing its transmit covariance matrix. Specifically, each multi-antenna SU maximizes locally its energy efficiency in terms of the number of bits transmitted per unit energy consumption, subject to the per-SU transmit power constraint and the primary user (PU) perceived total interference constraint. It is proved that the formulated non-cooperative game admits at least one Nash equilibrium (NE), and the sufficient condition for a unique NE is derived subsequently. Primal decomposition is employed in the local EE optimization problem to relax the coupling PU perceived interference constraint such that fully distributed operation is allowed. A distributed iterative EE optimization algorithm (DIEEOA) is then proposed to obtain the unique NE, which is shown to converge to the global optimum. Linear precoding techniques are employed to mitigate the impacts of multi-user interference and imperfect channel state information (CSI). Through numerical simulations, effectiveness of the proposed scheme is validated and the system setting parameters’ impacts on the performance are studied.

Energy efficient for UAV-enabled mobile edge computing networks: Intelligent task prediction and offloading

Mobile edge computing (MEC) network provides near-users computing and communication functions and has become a potential 5G evolutionary architecture. In order to overcome the shortcomings of the existing MEC network in fixed base stations and limited computing resources, unmanned arial vehicle (UAV) is introduced as a relay edge computing node and UAV-enabled MEC networks are proposed. However, UAVs have limited energy. Thus, energy consumption would be an optimal target during the information interaction. Therefore, an energy efficiency optimization algorithm based on a three-layer computation offloading strategy is proposed in this paper by combining the UAV position optimization algorithm and the LSTM-based task prediction algorithm. The experiments show that the computation offloading strategy of the UAV-enabled MEC network can be dynamically programmed with the proposed algorithm and architecture, according to the required delay, UAV height, and data size in order to effectively reduce the energy consumption of the UAV.