Optimal Power Control Strategy Research Articles

Asynchronous distributed optimal power control for fatigue load minimization in wind farms

An asynchronous distributed optimal power control (OPC) scheme is proposed to minimize fatigue load in the wind farms (WFs) in this paper. To better satisfy the grid code, each wind turbine (WT) in the scheme is equipped with an energy storage system (ESS) at the DC link of the WT converter. Based on the WT topology, a WT-ESS control model is established, thus developing an OPC scheme to control the generator torque, pitch angle, and ESS output simultaneously for reducing fatigue load. By formulating the model predicted control (MPC)-based optimal problems, the virtual mechanical power control is proposed to regulate the ESSs output for clarifying the power interactions among WTs and ESSs. In order to achieve more effective control of the above three parameters among multiple WTs-ESSs, an asynchronous distributed alternating direction method of multipliers (AD-ADMM) is developed to simultaneously minimize the variations of thrust force, shaft torque, and flapping moment load of the WTs in a wider range. Considering the impact of communication delays, the proposed AD-ADMM algorithm has the higher time efficiency and system robustness. The convergence of the proposed AD-ADMM algorithm is analyzed, and several cases are used to validate the performance of the OPC scheme.© 2017 Elsevier Inc. All rights reserved.

Distributed Optimal Power Control Scheme for Structural Loads Minimization in Wind Farms via a Consensus Approach

Distributed Optimal Power Control Scheme for Structural Loads Minimization in Wind Farms via a Consensus Approach

Federated Learning Over-the-Air by Retransmissions

Motivated by the increasing computational capabilities of wireless devices, as well as unprecedented levels of user- and device-generated data, new distributed machine learning (ML) methods have emerged. In the wireless community, Federated Learning (FL) is of particular interest due to its communication efficiency and its ability to deal with the problem of non-IID data. FL training can be accelerated by a wireless communication method called Over-the-Air Computation (AirComp) which harnesses the interference of simultaneous uplink transmissions to efficiently aggregate model updates. However, since AirComp utilizes analog communication, it introduces inevitable estimation errors. In this paper, we study the impact of such estimation errors on the convergence of FL and propose retransmissions as a method to improve FL accuracy over resource-constrained wireless networks. First, we derive the optimal AirComp power control scheme with retransmissions over static channels. Then, we investigate the performance of Over-the-Air FL with retransmissions and find two upper bounds on the FL loss function. Numerical results demonstrate that the power control scheme offers significant reductions in mean squared error. Additionally, we provide simulation results on MNIST classification with a deep neural network that reveals significant improvements in classification accuracy for low-SNR scenarios.

Optimal power control for sensors and DoS attackers over a fading channel network

In this paper, we focus on the optimal power control for the sensors and attackers in a remote state estimation process, which guarantees the achievement of the estimation task under the denial-of-service (DoS) attack. In the remote estimation process under the DoS attack, each sensor observes the target state and transmits its local estimates to the remote estimator via a wireless network, communications in which are disrupted by jamming signals. For the wireless network, while most of the existing works on the resilient remote state estimation consider time-invariant channel state information (CSI) model, we pay attention to the fading channel network, in which the channel state is fixed and the channel model can be characterized by a generalized finite-state Markov chain. We model the conflicting nature among the sensor and the attacker as a general-sum stochastic game, proposing a Nash Q-learning algorithm to find the optimal power control strategies for the two players. Moreover, the monotone structure of these strategies is constructed with a sufficient condition. Taking into account the practical applications, we also consider the case where the sensor and the attacker only know partial information of each other and employ the Bayesian game to model it. Based on the channel information of each player and its believes of the channel distribution of the opponent, the type-contingent energy strategies are obtained. Finally, simulation results are given to verify our results.

Power Control and Interference Pricing Strategies for Heterogeneous Networks With Renewable Energy Sources

In this paper, the power control and interference pricing problems between the macro base station (MBS) and small cell base stations (SBSs) in Heterogeneous networks (HetNets) are investigated. To optimally allocate power resources and effectively manage the power resources to reduce interference in HetNets, an innovative Stackelberg differential game approach is proposed. In the proposed Stackelberg game, the SBSs control their transmit power to achieve cost minimization based on the interference price offered by the MBS. Given the optimal power control strategies of SBSs, the MBS can adjust the interference price to realize the objective optimization. Renewable energy sources are introduced into the proposed HetNets, and differential equations formulate the SBSs' dynamic energy. Meanwhile, the MBS's state is given by the dynamic spectrum resources. Then the differential game is imported to achieve optimal power control and interference pricing. The open-loop Nash equilibrium solutions are first analyzed for the MBS and SBSs. Then the feedback Nash equilibrium solutions are given. Under the feedback pattern, the proposed model's performances in the non-cooperative and cooperative situations are discussed. Numerical simulations are given to show the convergence and effectiveness of the proposed game approach.

Performance Analysis of Radio Resource Allocation to Maximize Power Efficiency in Device-To-Device Communication


 
 
 Technological developments in the field of telecommunications have progressed. However, the more cellular network users, the more traffic on the Base Station (BS) will be. One way to overcome this is to implement a Device-To-device (D2D) communication system. However, when D2D User Equipment (DUE) reuses Cellular User channel resources Equipment (CUE), then interference will occur. This research is conducted to address interference problems and maximize energy efficiency for CUEs and DUEs by implementing the Greedy algorithm with additional power control. The aim is to block unnecessary resources and optimize resource allocation. The Optimal Power Control (OPC) scheme will utilize the transmit power threshold. The simulation scheme for the D2D communication system used is the uplink and underlay mode single-cell system model. Where the DUE pair will use the same resources as the CUE that has been given by the BS. Resources in CUE can only be reused by one pair of DUEs, and vice versa. The greedy algorithm using power control gets superior performance at a sum rate of 1.79 × 107 bps with an increase of 36.03%. Spectral efficiency of 2.49 bps/Hz with an increase of 36.03%. The power efficiency of 2.08 × 103 bps/mw with an increase of 118.47%. Based on the result the greedy algorithm without power control gets superior performance at CU Fairness of 1 with an increase of 50.14%.
 
 

Coordinated active and reactive power management for enhancing PV hosting capacity in distribution networks

AbstractThis paper proposes an operational planning model based on optimal active and reactive power control strategies to enhance solar photovoltaics (PV)’ hosting capacity in distribution networks. Reactive power control is carried out through optimum static VAr compensators (SVCs) placement, while active power control is performed through flexible loads, particularly shiftable and interruptible loads. The first stage of the proposed two‐stage stochastic model assigns decision‐making regarding calculating PV hosting capacity at different nodes, in addition to the allocation and capacity of SVCs. In the second stage, the first stage decisions are assessed to ensure the power flow constraints under various uncertainties such as daily load and stochastic PV generation. The presented model is investigated through numerical analyses on modified IEEE 15‐bus and IEEE 33‐bus distribution systems considering different active‐reactive strategy cases. While most previous works only rely on one type of active or reactive power control strategy, this study investigates the challenges of the respective application of active and reactive power control in various modes of fundamental practices. The obtained results prove the superiority of the proposed hybrid active‐reactive control strategy for enhancing PV hosting capacity compared to respective active or reactive power controls.

Jamming and Eavesdropping Defense Scheme Based on Deep Reinforcement Learning in Autonomous Vehicle Networks

As a legacy from conventional wireless services, illegal eavesdropping is regarded as one of the critical security challenges in Connected and Autonomous Vehicles (CAVs) network. Our work considers the use of Distributed Kalman Filtering (DKF) and Deep Reinforcement Learning (DRL) techniques to improve anti-eavesdropping communication capacity and mitigate jamming interference. Aiming to improve the security performance against smart eavesdropper and jammer, we first develop a DKF algorithm that is capable of tracking the attacker more accurately by sharing state estimates among adjacent nodes. Then, a design problem for controlling transmission power and selecting communication channel is established while ensuring communication quality requirements of the authorized vehicular user. Since the eavesdropping and jamming model is uncertain and dynamic, a hierarchical Deep Q-Network (DQN)-based architecture is developed to design the anti-eavesdropping power control and possibly channel selection policy. Specifically, the optimal power control scheme without prior information of the eavesdropping behavior can be quickly achieved first. Based on the system secrecy rate assessment, the channel selection process is then performed when necessary. Simulation results confirm that our jamming and eavesdropping defense technique enhances the secrecy rate as well as achievable communication rate compared with currently available techniques.

Joint power and hopping rate adaption against follower jammer based on deep reinforcement learning

AbstractAs an effective anti‐jamming approach, frequency‐hopping (FH) technology has been widely applied to tactical communication system, providing reliable communication guarantee and improving resilience against conventional interference under strong confrontation environment. Key challenges for tactical wireless communication network face are the smart follower jammer with responsive spectrum reconnaissance and intelligent decision‐making capabilities. In response, this article investigates a deep reinforcement learning based anti‐jamming scheme, with the aim of maximizing the system throughput. The interactions between a radio transmitter and a smart follower jammer are formulated as a hierarchical anti‐jamming dynamic game model, in which the radio terminal decides transmission power and hopping rate according to the state feedback information, and the jammer chooses spectrum scanning rate accordingly to minimizing the rewards of the FH communication system. We prove that there is a Nash equilibrium (NE) strategy for static and dynamic environment in the game. A double deep Q‐network with prioritized experience reply (PDDQN) based anti‐jamming scheme is proposed to approximating the optimal power control and hopping strategy without being aware of the environment and jamming parameters. Finally, simulation results demonstrate that the proposed algorithm efficiently provide better throughput and jamming resistance.

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.

Beyond Supervised Power Control in Massive MIMO Network: Simple Deep Neural Network Solutions

Power control in massive multiple input multiple output (MIMO) systems is an appealing technique to improve network performance and reliability. The traditional methods to solve such problems are based on the convex optimization theory, which incurs high computational complexity. In contrast, this work leverages deep neural networks to maximize the minimum data rate of the downlink users in the massive MIMO network. Based on the different setups in the practical systems, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-distribution case</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">weakly out-of-distribution case</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">out-of-distribution case</i> , we propose three approaches to solve them. Specifically, first, we establish a densely connected neural network for solving the power control problem. Then, for the case where the test data and training data come from the same distributions (the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-distribution case</i> ), we use the established neural network to obtain an approximately optimal power control strategy, and propose a self-training-based algorithm to use the unlabeled samples collected in the actual system to further improve the system performance. For the case that the test data and training data come from different distributions and a small amount of labeled target domain data can be obtained (the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">weakly out-of-distribution case</i> ), we propose a pre-training and fine-tuning algorithm to solve the problem. For the issue that we can not obtain the labels of the target domain data (the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">out-of-distribution case</i> ), we transform the problem into a covariate shift problem and propose an algorithm by weighting the loss function. Numerical results demonstrate that the proposed schemes not only match the optimal solution well but also have low online inference complexity.

A Novel Channel Model and Optimal Power Control Schemes for Mobile mmWave Two-Tier Networks

We present a unified system model and framework for the analytical performance study of two heterogeneous and physically-distinct, but coexisting, networks that work harmoniously at the same time, space, and frequency domains. The two-tier network model considered in this paper is an overlaying of femtocells on a macrocell. Overlaying femtocells improves the performance by offloading traffic from macrocells and providing spatial diversity. The mmWave channel model employed considers the number of clusters and rays within each cluster to vary due to the end-user mobility. This is a new and different model compared to the widely used channel models for mmWave two-tier networks. Optimal power control is formulated as a sum-rate maximization problem for downlink and uplink transmissions at two-tier networks and a power allocation scheme is proposed by following Shannon-Hartley theorem. A comprehensive and interesting performance investigation is provided, where it is shown that the upper bound on the number of admitted secondary users has a linear relationship with the outage probability threshold, logarithmic relationship with SINR and exponential relationship with channel gain factors. Simulation results show that the proposed scheme with algorithm 2 is very effective at managing the cross-tier interference and can outperform a competitive scheme from literature that is based on cognitive radio technology. The computational complexity analysis of proposed algorithms are also given, since the complexity of algorithm 2 can be a performance-complexity trade-off issue for systems with limited computation power and time requirements.

Millimeter Wave MIMO-OFDM With Index Modulation: A Pareto Paradigm on Spectral- Energy Efficiency Trade-Off

Multiple-input multiple-output orthogonal frequency division multiplexing with index modulation (MIMO-OFDM-IM) has recently received increased attention, due to the potential advantage to balance the trade-off between spectral efficiency (SE) and energy efficiency (EE). In this paper, we investigate the application of MIMO-OFDM-IM to millimeter wave (mmWave) communication systems, where a hybrid analogy-digital (HAD) beamforming architecture is employed. Taking advantage of the Pareto-optimal beam design, we propose a feasible solution to approximately achieve a globally Pareto-optimal trade-off between SE and EE, and the collision constraints of the multi-objective optimization problem (MOP) can be solved efficiently. Correspondingly, the MOP of SE-EE trade-off can be converted into a feasible solution for energy-efficient resource usage, by finding the Pareto-optimal set (POS) towards the Pareto front. This combinatorial-oriented resource allocation approach on the SE-EE relation considers the optimal beam design and power control strategies for downlink multi-user mmWave transmission. To ease the system performance evaluation, we adopt the Poisson point process (PPP) to model the mobile data traffic, and the evolutionary algorithm is applied to speed up the search efficiency of the Pareto front. Compared with benchmarks, the experimental results collected from extensive simulations demonstrate that the proposed optimization approach is vastly superior to existing algorithms.

Power Control Algorithm Based on a Cooperative Game in User-Centric Unmanned Aerial Vehicle Group

The quality of service (QoS) of a user in user-centric unmanned aerial vehicle group (UUAVG) is degraded by complex cochannel interference; hence, a cooperative game power control (CGPC) algorithm in UUAVG is proposed. The algorithm helps to establish a downlink power control model of the UUAVG, construct a product of the signal to interference noise ratio function of each user as a utility function of the cooperative game, and deduce the optimal power control scheme using the Lagrange function. This scheme reduces the interference of the service unmanned aerial vehicle (UAV) to edge users and improves the communication quality of all the users as well as the throughput of the entire system. Simulation results show that the average throughput of the CGPC algorithm improved by 10.32% compared with the traditional Stackelberg Game based Nonunified Pricing Power Control (SGNPPC) algorithm. This shows that the CGPC algorithm can effectively reduce the transmission power of the cooperative UAV and enhance the capacity of the entire system, ensuring communication quality.

Voltage control approach based on PCPM distributed algorithm in the presence of high PV penetration: a stochastic modeling

The uncertainty of solar generation is a challenging issue for the operation and control of distribution systems. Also, because of the large number of control and operation variables in advanced distribution networks, the centralized optimization technique requires high computational capabilities. In this paper, a distributed voltage regulation approach in a distribution system based on the predictor corrector proximal multiplier (PCPM) algorithm has been presented. This paper focuses on the voltage rise in distribution systems with high penetration of photovoltaics. The proposed method uses a clustering approach to divide the network into partitions based on the coupling degrees among different nodes. The optimal reactive power control strategy is conducted in each partition and integrated using PCPM. The proposed method is tested on 69 and 123 nodes IEEE distribution test systems. The results confirmed that the proposed method achieves the optimal solution with the same accuracy as the centralized method but in a much shorter computation time.

Reinforcement learning based a non-zero-sum game for secure transmission against smart jamming

Smart jammer and smart anti-jammer have always been attacked and defensed in a contradictory way. In fact, there exists fundamental trade-off between all evolved parties. It is well known that only through interactive training with powerful opponents can the strategy optimization ability in actual combat be improved. In the process of electronic countermeasures between the communication system and the jammer, the traditional electronic warfare attacker uses an open-loop jamming method, that is, the opponent's information cannot be obtained after the jamming, which greatly reduces the combat effect. In order to improve the strategic optimization capabilities of the combatant, cognition and intelligence are introduced into electronic countermeasures. In this paper, we start from modeling a non-zero-sum game, and analyze the Nash equilibrium (NE) of the static secure game and the conditions for its existence. Then, we design a multi-agent reinforcement learning framework with a optimal power control strategy in the dynamic game between the smart jammer and the trained base station (BS). Finally, due to the non-cooperative hostile relationship between the two sides in the actual combat, we add the eavesdropping function and the jamming effect evaluation modular to build a cognitive closed-loop. The experiment shows that the intelligent jammer with the eavesdropping function can seriously reduce the performance of the interfered communication party. However, the intelligent BS after training can effectively combat smart jamming. It can be demonstrated that confrontation training can improve the intelligence level of agents.

Distributed optimal active and reactive power control for wind farms based on ADMM

In this paper, a distributed optimal active and reactive power control (DARPC) strategy based on the alternating direction method of multipliers (ADMM) is proposed for wind farms (WFs). The WFs operate in a distributed manner to minimize the network power loss, voltage deviations of buses from the rated voltage, and active power output deviations of WTs from their proportional distribution (PD)-based power reference. An optimization problem is formulated as a quadratic programming (QP) problem by using the linearized DistFlow model. The ADMM-based solution is used to decompose the centralized optimization problem to several subproblems which are solved in individual local controllers with exchanged information from their practical neighbor controllers. Compared with existing distributed/hierarchical optimal control, the impacts of the active power injection from the WTs are taken into consideration and optimized while meeting the dispatch command from the transmission system operator (TSO). The ADMM-based distributed solution eliminates the requirement of a central unit. Compared with conventional centralized optimal control, the scalability of the WFs is improved. A WF consisting of 20 WTs is simulated in MATLAB/Simulink to test the control effectiveness of the proposed DARPC strategy.

Downlink Transmit Power Control in Ultra-Dense UAV Network Based on Mean Field Game and Deep Reinforcement Learning

As an emerging technology in 5G, ultra-dense unmanned aerial vehicles (UAVs) network can significantly improve the system capacity and networks coverage. However, it is still a challenge to reduce interference and improve energy efficiency (EE) of UAVs. In this paper, we investigate a downlink power control problem to maximize the EE in an ultra-dense UAV network. Firstly, the power control problem is formulated as a discrete mean field game (MFG) to imitate the interactions among a large number of UAVs, and then the MFG framework is transformed into a Markov decision process (MDP) to obtain the equilibrium solution of the MFG due to the dense deployment of UAVs. Specifically, a deep reinforcement learning-based MFG (DRL-MFG) algorithm is proposed to suppress the interference and maximize the EE by using deep neural networks (DNN) to explore the optimal power strategy for UAVs. The numerical results show that the UAVs can effectively interact with the environment to obtain the optimal power control strategy. Compared with the benchmarks algorithms, the DRL-MFG algorithm converges faster to the solution of MFG and improves the EE of UAVs. Moreover, the impact of the transmit power on EE under the different heights of the UAVs is also analyzed.

An Optimal Power Control Strategy for Grid-Following Inverters in a Synchronous Frame

This work proposes a power control strategy based on the linear quadratic regulator with optimal reference tracking (LQR-ORT) for a three-phase inverter-based generator (IBG) using an LCL filter. The use of an LQR-ORT controller increases robustness margins and reduces the quadratic value of the power error and control inputs during transient response. A model in a synchronous reference frame that integrates power sharing and voltage–current (V–I) dynamics is also proposed. This model allows for analyzing closed-loop eigenvalue location and robustness margins. The proposed controller was compared against a classical droop approach using proportional-resonant controllers for the inner loops. Mathematical analysis and hardware-in-the-loop (HIL) experiments under variations in the LCL filter components demonstrate fulfillment of robustness and performance bounds of the LQR-ORT controller. Experimental results demonstrate accuracy of the proposed model and the effectiveness of the LQR-ORT controller in improving transient response, robustness, and power decoupling.

A Low-Complexity Framework for Joint User Pairing and Power Control for Cooperative NOMA in 5G and Beyond Cellular Networks

This paper investigates the performance of cooperative non-orthogonal multiple access (C-NOMA) in downlink communication systems. Using C-NOMA, users with more favorable channel conditions can assist communication between the base station (BS) and the users with less favorable channel conditions using either full-duplex (FD) or half-duplex (HD) device-to-device (D2D) relaying and successive interference cancellation (SIC). To maximize the benefits of C-NOMA, we formulate and solve a novel optimization problem that jointly determines the optimal D2D user pairing and the optimal power control scheme in a downlink cellular system consisting of a BS that communicates with a set of spatially dispersed users. The formulated problem is a non-convex mixed-integer non-linear program (MINLP) which is difficult to solve due to the dependency between power control and user pairing. Thus, we decompose the problem into an inner power control problem and an outer pairing problem. For the inner problem, we derive the optimal closed-form expressions for both HD and FD relaying modes, while the outer problem of user pairing can be solved using the well-known Hungarian method. The simulation results show that the proposed framework outperforms a variety of proposed schemes in the literature and that it can obtain the optimal pairing and power control policies for a network with 100 users in negligible computational time.