Optimal Power Control Research Articles

Multi-Pair D2D Communications Aided by an Active RIS Over Spatially Correlated Channels With Phase Noise

This paper investigates a multi-pair device-to-device (D2D) communication system aided by an active reconfigurable intelligent surface (RIS) with phase noise and direct link. The approximate closed-form expression of the ergodic sum rate is derived over spatially correlated Rician fading channels with statistical channel state information (CSI). When the Rician factors go to infinity, the asymptotic expressions of the ergodic sum rates are presented to give insights in poor scattering environment. The power scaling law for the special case of a single D2D pair is presented without phase noise under uncorrelated Rician fading condition. Then, to solve the ergodic sum rate maximization problem, a method based on genetic algorithm (GA) is proposed for joint power control and discrete phase shifts optimization. Simulation results verify the accuracy of our derivations, and also show that the active RIS outperforms the passive RIS.

Optimal wireless rate and power control in the presence of jammers using reinforcement learning

Future wireless networks require high throughput and energy efficiency. This paper studies using Reinforcement Learning (RL) to do transmission rate and power control for maximizing a joint reward function consisting of both throughput and energy consumption. We design the system state to include factors that reflect packet queue length, interference from other nodes, quality of the wireless channel, battery status, etc. The reward function is normalized and does not involve unit conversion. It can be used to train three different types of agents: throughput-critical, energy-critical, and throughput and energy balanced. Using the NS-3 network simulation software, we implement and train these agents in an 802.11ac network with the presence of a jammer. We then test the agents with two jamming nodes interfering with the packets received at the receiver. We compare the performance of our RL optimal policies with the popular Minstrel rate adaptation algorithm: our approach can achieve (i) higher throughput when using the throughput-critical reward function; (ii) lower energy consumption when using the energy-critical reward function; and (iii) higher throughput and slightly higher energy when using the throughput and energy balanced reward function. Although our discussion is focused on 802.11ac networks, our method is readily applicable to other types of wireless networks.

Research on energy optimal method of more electrical aircraft base on energy storage

This Article analyzes the power topology, power transmission and use characteristics of future multi-electric aircraft. Power optimization algorithms and optimal control strategy are provided, and the power system optimization algorithm model is constructed combined with the application scenario. The simulation results show that the energy optimization and comprehensive management of the airborne power system based on the energy storage and generator energy conplementary operation mode has a good effect on improving the efficiency of airborne energy utilization, and has application value in engineering applications.

A Review of Energy Efficiency and Power Control Schemes in Ultra-Dense Cell-Free Massive MIMO Systems for Sustainable 6G Wireless Communication

The traditional multiple input multiple output (MIMO) systems cannot provide very high Spectral Efficiency (SE), Energy Efficiency (EE), and link reliability, which are critical to guaranteeing the desired Quality of Experience (QoE) in 5G and beyond 5G wireless networks. To bridge this gap, ultra-dense cell-free massive MIMO (UD CF-mMIMO) systems are exploited to boost cell-edge performance and provide ultra-low latency in emerging wireless communication systems. This paper attempts to provide critical insights on high EE operation and power control schemes for maximizing the performance of UD CF-mMIMO systems. First, the recent advances in UD CF-mMIMO systems and the associated models are elaborated. The power consumption model, power consumption parts, and energy maximization techniques are discussed extensively. Further, the various power control optimization techniques are discussed comprehensively. Key findings from this study indicate an unprecedented growth in high-rate demands, leading to a significant increase in energy consumption. Additionally, substantial gains in EE require efficient utilization of optimal energy maximization techniques, green design, and dense deployment of massive antenna arrays. Overall, this review provides an elaborate discussion of the research gaps and proposes several research directions, critical challenges, and useful recommendations for future works in wireless communication systems.

On the Capacity-Achieving Input of Channels With Phase Quantization

Several information-theoretic studies on channels with output quantization have identified the capacity-achieving input distributions for different fading channels with 1-bit in-phase and quadrature (I/Q) output quantization. However, an exact characterization of the capacity-achieving input distribution for channels with multi-bit phase quantization has not been provided. In this paper, we consider four different channel models with multi-bit phase quantization at the output and identify the optimal input distribution for each channel model. We first consider a complex Gaussian channel with $b$-bit phase-quantized output and prove that the capacity-achieving distribution is a rotated $2^b$-phase shift keying (PSK). The analysis is then extended to multiple fading scenarios. We show that the optimality of rotated $2^b$-PSK continues to hold under noncoherent fast fading Rician channels with $b$-bit phase quantization when line-of-sight (LoS) is present. When channel state information (CSI) is available at the receiver, we identify $\frac{2\pi}{2^b}$-symmetry and constant amplitude as the necessary and sufficient conditions for the ergodic capacity-achieving input distribution; which a $2^b$-PSK satisfies. Finally, an optimum power control scheme is presented which achieves ergodic capacity when CSI is also available at the transmitter.

Energy-Efficient Covert Communication With the Aid of Aerial Reconfigurable Intelligent Surface

In this letter, a novel aerial reconfigurable intelligent surface (ARIS) assisted covert communication framework is proposed, where RIS is mounted on unmanned aerial vehicle (UAV) to enhance the covert performance of ground transceivers in the presence of a warden. The detection performance of warden is first analyzed, and then a covert energy efficiency (CEE) maiximization problem is formulated by jointly optimizing power control, passive beamforming, and UAV trajectory. To solve the non-convex problem, an algorithm based on alternating optimization is devised to achieve the suboptimal solution. Numerical evaluations show that the proposed scheme has a larger CEE gain than the scheme without RIS under the condition of more reflecting elements. Moreover, the proposed scheme can achieve near-same covertness performance at a lower energy consumption cost compared with covert transmission rate maximization scheme.

Cost optimization of UAV-MEC network calculation offloading: A multi-agent reinforcement learning method

In the Unmanned Aerial Vehicle (UAV)-assisted mobile edge computing (MEC) system, most existing studies do not effectively consider the task offloading of user equipment (UEs) and the economic issues related to MEC service providers. Firstly, this paper considers the UE offloading cost and the pricing of MEC server, and establishes the UE, UAV cost and UAV revenue model. For the established non-convex optimization problem, it is further described as a Markov decision process (MDP), and a multi-agent reinforcement deep learning algorithm (MADRL) is proposed to minimize the system energy consumption by jointly optimizing power control, resource allocation and UE association, so as to effectively improve the overall revenue of UAV under the premise of ensuring the system performance. The simulation results show that our scheme can effectively improve the UAV revenue, and is significantly better than the random scheme without energy consumption optimization. In addition, the revenue obtained by our design scheme is 10.7% higher than that of the comparison scheme.

Active power control optimization strategy of photovoltaic assisted frequency modulation considering LVRT

Voltage and frequency problems may occur at the same time under large disturbance faults, with the large-scale new energy connected to the power grid. The fault ride through control strategy of photovoltaic inverter gives priority to reactive power during low voltage period, so there is a lack of active power control. An optimal control strategy of photovoltaic reactive power priority and auxiliary frequency modulation is proposed for LVRT control, aiming at the frequency stability of weak current network caused by excessive active power imbalance during LVRT. By sampling the frequency deviation signal of the system as the input signal, it realizes the adjustment of active power control parameters during LVRT, and carries out system auxiliary frequency modulation, including frequency difference discrimination link, parameter selector and parameter switching control. Finally, this paper concludes that the active power optimization control strategy of LVRT control can improve the frequency stability of the system significantly during low-voltage period by simulation, which is on the premise of ensuring the low-voltage reactive power support.

Joint Video Packet Assignment, Power Control and User Scheduling Over Cognitive Multi-Homing Heterogeneous NOMA Networks

Non-orthogonal multiple access (NOMA)-based cognitive heterogeneous multi-homing networks is a very important scenario in the future wireless networks. In this work, we formulate a joint video packet assignment, power control and user scheduling problem as a mixed integer non-linear programming (MINLP) to maximize the total video transmission quality for cognitive multi-homing heterogeneous NOMA networks, which is subject to the maximum accessed number of secondary users at each subchannel, video encoding characteristics, maximum interference power constraint and total available power constraint. For the joint video packet assignment, power control and user scheduling problem, we divide it into a video packet assignment subproblem, a power control subproblem and a secondary user scheduling subproblem for cognitive multi-homing heterogeneous NOMA networks. Firstly, the secondary user scheduling algorithm is proposed using the greedy method. Then, we utilize successive convex approximation (SCA) method to transform the power control subproblem into a convex programming problem, and an approximated optimal power control algorithm is proposed with the dual decomposition method. Finally, a heuristic video packet assignment algorithm is designed, which utilizes the auction theory. Numerical simulation results demonstrate that the proposed algorithms not only improve the video transmission quality, but also enhance the total throughput of cognitive multi-homing heterogeneous NOMA networks.

Performance Analysis and Optimization of NOMA-Based Cell-Free Massive MIMO for IoT

This article investigates the performance of nonorthogonal multiple access (NOMA)-based cell-free massive multiple-input–multiple-output (mMIMO) for the Internet of Things (IoT) considering spatially correlated Rician fading channels. The exact closed form of downlink spectral efficiency (SE) and energy efficiency expressions is derived with three estimators and the maximum ratio transmission by taking the impacts of imperfect successive interference cancellation and pilot contamination into account. Subsequently, the performance of a local-MMSE precoder with the three aforementioned estimators is analyzed. Then, a large-scale fading-based user pairing scheme is proposed to further analyze the system SE. Besides, we formulate the optimum power control design as a max–min problem and a computational efficient suboptimal algorithm is proposed based on the successive convex approximation. Furthermore, our results reveal that the magnitude of the spatial correlation negligibly effects the SE in spatially correlated Rician fading channels. Then, numerical results confirm the positive effect of the proposed power control scheme. Also, our results further illustrate that NOMA-based cell-free mMIMO for IoT provides significant performance gain compared with its counterpart deploying conventional orthogonal multiple-access schemes.

Joint power control and multiple antennas optimization for reducing receiver blocking in dense VANETs

The dynamic topology of vehicular ad hoc networks (VANETs) has a significant impact on the transmission and reception of data. It will cause receiver blocking due to the increase in the number of vehicles on the road and the rapid change of network topology. How to reduce the blocking and improve the reliability of data transmission is still a challenging problem. In this paper, we first propose a joint distributed power control and multiple antennas (DPC-MA) scheme, which can minimize blocking and simultaneously improve throughput. Moreover, it works well with an increased number of vehicles. Then we build an optimization model to select the optimal transmission power and number of antennas for vehicles based on their neighborhood relationships. The formulated problem is a mixed-integer nonlinear programming problem (MINLP), which is NP-hard and usually has no feasible solution. To solve this problem, we propose a low-complexity efficient algorithm using Lagrangian duality, which decomposes the original problem into a series of sub-problems and solves them iteratively using the fixed-point iterative algorithm and sub-gradient method. Simulation results show that our proposed scheme has better performance in resolving blocking and achieving high throughput, especially in high-density vehicle scenarios.

On Optimal Power Control for Energy Harvesting Communications With Lookahead

Consider the problem of power control for an energy harvesting communication system, where the transmitter is equipped with a finite-sized rechargeable battery and is able to look ahead to observe a fixed number of future energy arrivals. An implicit characterization of the maximum average throughput over an additive white Gaussian noise channel and the associated optimal power control policy is provided via the Bellman equation under the assumption that the energy arrival process is stationary and memoryless. A more explicit characterization is obtained for the case of Bernoulli energy arrivals by means of asymptotically tight upper and lower bounds on both the maximum average throughput and the optimal power control policy. Apart from their pivotal role in deriving the desired analytical results, such bounds are highly valuable from a numerical perspective as they can be efficiently computed using convex optimization solvers.

Impacts of User Association and Power Control on AoI in Multi-Tier Cellular-Based IoT Networks

In this letter, we study the age of information (AoI) of a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> -tier cellular Internet-of-Things (IoTs) network by taking into account both weighted path loss association policy and fractional power control strategy. By adopting tools from stochastic geometry, we first obtain the conditional per-tier AoI and conditional success probability, and then derive the tractable expression for average AoI. The proposed analytical framework can be used to evaluate the impacts of weight association ratio of different base station (BS) tiers and power control factor on average AoI. Numerical results verify the correctness of our analysis and reveal the following two conclusions: i) the nearest association policy (i.e., with weight association ratio being 0dB) is optimal to both average AoI and overall success probability; ii) the power control factor has significant impact on average AoI, where the optimal power control factor for average AoI is larger than that for overall success probability.

Mayfly Optimization Algorithm–Based PV Cell Triple-Diode Model Parameter Identification

The maximum power output and control optimization analysis of photovoltaic (PV) systems are based on accurate and reliable PV cell parameter identification. However, its difficult problems such as high nonlinearity and multimodality have become obstacles to the traditional optimization methods to obtain accurate and efficient results. This study uses a new intelligent optimization algorithm called the mayfly algorithm (MA) to efficiently identify the triple-diode model (TDM) of PV cells and uses the minimum root mean square error (RMSE) as the evaluation index to verify the effectiveness of the algorithm. Moreover, by continuously adjusting the parameters, population number, and iteration times of the MA to better balance the relationship between global development and local optimization, we can obtain more efficient and better optimization results. The research case shows that the MA is superior to other meta-heuristic algorithms in the accuracy and stability of PV cell parameter identification. For example, the minimum standard deviation (SD) of the RMSE obtained by the MA is 1,305 times smaller than another algorithm.

Control and Dynamic Characteristics Analysis for the Double-Compound Axial Piston Pump Based on Working Conditions

The periodic flow and pressure pulsation of the axial piston pump can lead to periodic vibration, noise, and even damage to various components in the hydraulic system. Therefore, the dynamic characteristics analysis of the axial piston pump in the hydraulic system is of great significance for reducing vibration and noise in the hydraulic system and improving efficiency. The double-compound axial piston pump is the key component of a high power and large flow hydraulic power system, which has a special power control mode. In this paper, the working and control principles of the double-compound axial piston pump are analyzed, the numerical model of the double-compound axial piston pump is established, and the effectiveness of the model is verified through experimental tests. The constant power control characteristics of the double-compound axial piston pump under different power settings and the variable power control characteristics under the rated load pressure are analyzed. By analyzing the dynamic characteristics of the double-compound axial piston pump, the influence of different working conditions on the dynamic characteristics is investigated. The results show that the output efficiency of the double-compound axial piston pump is higher when the output flow is larger. When the piston chamber works in the closed pre-compression pressure zone and the closed pre-release pressure zone, oil backflow occurs. The oil backflow in the piston chamber seriously intensifies the outlet flow pulsation of the double-compound axial piston pump. The flow pulsation rate is positively correlated with load pressure and power control pilot pressure, while negatively correlated with spindle speed. The paper provides a basis for the analysis and optimization of power control, flow pulsation, fluid vibration, and noise of the double-compound axial piston pump.

Reactive Power and Voltage Optimization of New-Energy Grid Based on the Improved Flower Pollination Algorithm

In order to solve the reactive power and voltage control problem caused by the high proportion of new energy connected to the power grid, this paper takes the minimum voltage deviation, minimum network loss and maximum dynamic reactive power margin of the whole system as the comprehensive optimization objectives and establishes a reactive power and voltage optimization model by considering the reactive power regulation ability of SVC (Static Var Compensator) and new energy units. In view of the continuous and discrete variables in the model, the traditional continuous FPA (Flower Pollination Algorithm) is discretized to form an improved continuous-discrete hybrid FPA, and the tournament selection mechanism is adopted to speed up the convergence. Through the example analysis of the IEEE-39 bus system, the feasibility of the proposed reactive power and voltage optimal control method in the new energy grid is verified. Compared with GA (Genetic Algorithm), the results show that the improved FPA has high optimization accuracy, which is suitable for solving the reactive power and voltage optimization problem of the new energy grid.

Joint User Association, Power Optimization and Trajectory Control in an Integrated Satellite-Aerial-Terrestrial Network

Internet-of-Things (IoT) is being widely embraced with the number of connected devices growing rapidly. Moreover, IoT applications are emerging in diverse verticals such as connected cars, connected factories, and smart agriculture. For new business models, in order to meet key network performance indicators, connectivity must be flexible and agile. An integrated satellite-aerial-terrestrial network (I-SAT) has recently stimulated interest in providing wireless communication due to its high maneuverability, versatile deployment, and pervasive connectivity. The resource planning, task distribution, and action management of an I-SAT can be accomplished through effective acquisition, coordination, transmission, and aggregation of diverse information. This paper considers an I-SAT network, in which multiple unmanned aerial vehicles (UAVs) with aerial stations and a terrestrial base station (BS), in a cognitive setting, in the presence of satellite-receiver communication, are deployed to support smart vehicles on the ground. By taking into account different limitations and Quality of Service (QoS) constraints, the goal is to maximize the average throughput among users by jointly optimizing user association, BS/UAV transmission power, and UAV trajectory. The formulated problem is a non-convex optimization problem with a complicated expression that is hard to solve. To tackle this problem, an alternating iterative algorithm based on the block descent method is proposed. Precisely, the problem is subdivided into three subproblems, transmitter-vehicle association optimization, BS/UAV power allocation optimization, and UAV trajectory control. Then, in an iterative process, these subproblems are solved sequentially. The proposed solution uses a segment-by-segment technique, which breaks the complete UAV flight trajectory into smaller time segments to reduce computation time when the network service period is considerable. As a result, each time segment’s optimization can be solved more quickly. Furthermore, the paper presents the results of network simulations carried out to assess the efficiency of the proposed solution. The findings show that the presented scheme outperforms different benchmark schemes in terms of the average user throughput when observing multiple different scenarios.

Priority-Driven Self-Optimizing Power Control Scheme for Interlinking Converters of Hybrid AC/DC Microgrid Clusters in Decentralized Manner

Hybrid ac&#x002F;dc microgrid clusters are key building blocks of smart grid to support sustainable and resilient urban power systems. Practically, in networked microgrid clusters, the subgrid load-priorities and power quality requirements for different areas vary significantly. In order to realize optimal power exchanges among microgrid clusters, this article proposes a decentralized self-optimizing power control scheme for interlinking converters (ILCs). First, a priority-driven optimal power exchange model of ILCs is built that fully considers the priorities and capacities in subgrids. The whole optimization objective is to minimize the total dc-voltage&#x002F;ac-frequency state deviations of subgrids. Second, to realize the decentralized power flow control, an optimal-oriented quasi-droop control strategy of ILCs is introduced. Consequently, as each of ILCs only monitors the local ac-side frequency and dc-side voltage signals, the whole optimal power control of the wide-area microgrid clusters is achieved in a decentralized manner without any communication link. Thus, the proposed control algorithm has the features of decreased cost, increased scalability, reduced geographic restrictions and high resilience and robustness in terms of communication faults. Finally, the proposed method is validated by three cases in hardware-in-loop environment.

Power Optimization and Network Congestion Controlling Technique for an IoT Enabled Smartbin for Smart Cities

The rapid increase in population leads to a corresponding increase in waste. As an integral application of a smart city, smart waste management framework provides a viable solution of implementing smartbins to monitor the waste and collect it. However, being a battery-operated node in continuous communicating with the cloud, power efficiency and network congestion are two of the major issues faced by the smartbins. Therefore, an algorithm for power optimization and network congestion control has successfully been presented in this article. The results obtained from testing the algorithm under different filling conditions and zero packet loss provide evidence of its practicality to optimize power usage and circumvent network congestion. The proposed algorithm can easily be implemented as an integral part of a smartbin to allow the cities to support smart waste management frameworks with a minimal failure rate and minimal cost of maintenance. Researchers can integrate the algorithm into their frameworks to propose solutions to the ever-increasing problem of waste management.

A New Self-Adaptive Teaching–Learning-Based Optimization with Different Distributions for Optimal Reactive Power Control in Power Networks

Teaching–learning-based optimization has the disadvantages of weak population diversity and the tendency to fall into local optima, especially for multimodal and high-dimensional problems such as the optimal reactive power dispatch problem. To overcome these shortcomings, first, in this study, a new enhanced TLBO is proposed through novel and effective θ-self-adaptive teaching and learning to optimize voltage and active loss management in power networks, which is called the optimal reactive power control problem with continuous and discontinuous control variables. Voltage and active loss management in any energy network can be optimized by finding the optimal control parameters, including generator voltage, shunt power compensators, and the tap positions of tap changers, among others. As a result, an efficient and powerful optimization algorithm is required to handle this challenging situation. The proposed algorithms utilized in this research were improved by introducing new mutation operators for multi-objective optimal reactive power control in popular standard IEEE 30-bus and IEEE 57-bus networks. The numerical simulation data reveal potential high-quality solutions with better performance and accuracy using the proposed optimization algorithms in comparison with the basic teaching–learning-based optimization algorithm and previously reported results.