Optimal Power Control Research Articles

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.

Joint Mobility, Communication and Computation Optimization for UAVs in Air-Ground Cooperative Networks

Unmanned aerial vehicles (UAVs) play a significant role in various 5G or Beyond-5G (B5G)-enabled Internet-of-Things (IoT) applications. However, the UAV performance in an air-ground cooperative network is significantly affected by its mobility and air-to-ground (A2G) communication and computation behaviors. In this paper, a UAV-oriented computation offloading system is investigated, where the UAV desires to complete its onboard computation demands with the assistance of a ground edge-computing infrastructure, i.e., a road-side unit (RSU). The objective is to maximize the energy efficiency of the UAV. Specifically, a non-convex constrained optimal control problem is formulated to optimize the overall energy efficiency of UAV by jointly considering the coupled effects of UAV's longitudinal mobility, A2G communication, and computation dynamics. To address the coupled complexity and non-convexity of the original problem, a primal decomposition approach is developed to transform the problem into a convex subproblem and a primary problem, and then a closed-form optimal transmission power control is derived by solving the subproblem, which is dependent on mobility information. By embedding the closed-form optimal power control into the primary problem, a gradient projection-based iterative algorithm is proposed to obtain a joint optimal solution for both the longitudinal acceleration control and the power control, the feasibility and convergence of which is also theoretically proven. Extensive simulations have been conducted to validate the effectiveness of the proposed method in terms of constraint satisfaction and convergence speed, and comparative results also demonstrate that it can outperform other benchmark methods in terms of global energy efficiency.

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.

An optimal transmission line power control under non‐sinusoidal conditions using static synchronous series compensator

In view of the growing load demand in developing countries, overloading in existing transmission lines is a major concern. In addition to the voltage drop due to overloading, the proliferation of non-linear loads in recent years distort the shape of sinusoidal voltage. Stable transmission of power through a transmission line under such condition is a challenge. In the presence of harmonics, control of transmittable power using static synchronous series compensator needs additional efforts, where increased power demand can be fulfilled by taking into consideration various other parameters. If an attempt is made to increase transmittable power through a transmission line using SSSC, power loss, the magnitude of voltage and its shape deteriorate in the presence of non-linear loads. Thus, the trade-off between transmittable power, transmission loss and voltage quality is tackled by optimal control of the compensator. In this study, a novel optimal control of SSSC is proposed with the objective of minimising transmission loss by maintaining the voltage within the limits while enhancing the transmittable power under non-sinusoidal conditions. A two-machine model of power system with 400 kV transmission line with parameters as per standards issued by the Central Electricity Authority is considered for validation of the algorithm using MATLAB platform and results are presented.

State Estimation-Based Distributed Energy Resource Optimization for Distribution Voltage Regulation in Telemetry-Sparse Environments Using a Real-Time Digital Twin

Real-time state estimation using a digital twin can overcome the lack of in-field measurements inside an electric feeder to optimize grid services provided by distributed energy resources (DERs). Optimal reactive power control of DERs can be used to mitigate distribution system voltage violations caused by increased penetrations of photovoltaic (PV) systems. In this work, a new technology called the Programmable Distribution Resource Open Management Optimization System (ProDROMOS) issued optimized DER reactive power setpoints based-on results from a particle swarm optimization (PSO) algorithm wrapped around OpenDSS time-series feeder simulations. This paper demonstrates the use of the ProDROMOS in a RT simulated environment using a power hardware-in-the-loop PV inverter and in a field demonstration, using a 678 kW PV system in Grafton (MA, USA). The primary contribution of the work is demonstrating a RT digital twin effectively provides state estimation pseudo-measurements that can be used to optimize DER operations for distribution voltage regulation.

Joint Optimization of BS Clustering and Power Control for NOMA-Enabled CoMP Transmission in Dense Cellular Networks

Non-orthogonal multiple access (NOMA)-enabled coordinated multipoint (CoMP) transmission has great potential in balancing spectrum utilization and interference mitigation in dense cellular networks. However, NOMA reforms the spectrum sharing policy of CoMP transmission due to introducing additional interference among coordinated base stations (BSs), which deteriorates the CoMP transmission rate. In this paper, we investigate the joint optimization of BS clustering and power control for NOMA-enabled CoMP transmission in dense cellular networks to maximize system sum-rate. We first characterize the impact of interference among coordinated BSs on CoMP transmission rate and find that the BS clustering is dependent on NOMA user grouping and restricted by the NOMA decoding condition. We then derive a tight lower bound on the system sum-rate and exploit it to design a joint BS clustering and power control scheme. Specifically, a power control algorithm is designed by a penalty convex-concave procedure to satisfy the NOMA decoding condition and users’ rate requirements. A BS clustering algorithm based on successive convex approximation is designed to iteratively update the BS clustering and NOMA user grouping to increase system sum-rate. Finally, two algorithms are alternately performed until all users successfully access and the system sum-rate converges. Simulation results show that the proposed scheme can efficiently alleviate interference among coordinated BSs to improve system sum-rate and spectrum efficiency of NOMA-enabled CoMP transmission even under high user density.

Multi-Objective Optimization of Joint Power and Admission Control in Cognitive Radio Networks Using Enhanced Swarm Intelligence

The problem of joint power and admission control (JPAC) is a critical issue encountered in underlay cognitive radio networks (CRNs). Moving forward towards the realization of Fifth Generation (5G) and beyond, where optimization is envisioned to take place in multiple performance dimensions, it is crucially desirable to achieve high sum throughput with low power consumption. In this work, a multi-objective JPAC optimization problem that jointly maximizes the sum throughput and minimizes power consumption in underlay CRNs is formulated. An enhanced swarm intelligence algorithm has been developed by hybridizing two new enhanced Particle Swarm Optimization (PSO) variants, namely two-phase PSO (TPPSO) and diversity global position binary PSO (DGP-BPSO) variants employed to optimize the multi-objective JPAC problem. The performance of the enhanced swarm intelligence algorithm in terms of convergence speed and stability, while optimizing both the sum throughput and power consumption, is investigated under three different operational scenarios defined by their single objective priorities, which translate to sum throughput and power consumption preferences. Simulation results have proven the effectiveness of the enhanced swarm intelligence algorithm in achieving high sum throughput and low power consumption under the three operational scenarios when the network includes an arbitrary number of primary and secondary users. Comparing the hybrid SPSO approach and the proposed approach, the proposed scheme has shown its effectiveness in increasing the sum throughput to 7%, 16%, and 31% under the multimedia, balanced and power saving operational scenarios, respectively. In addition, the proposed approach is more power efficient as it can provide additional power savings of 3.58 W, 2.48 W, and 1.6741 W under the aforementioned operational scenarios, respectively.

Design of reactive power optimization control for electromechanical system based on fuzzy particle swarm optimization algorithm

Due to low precision and premature tendency of traditional particle swarm optimization, the reactive power optimization control of electromechanical system based on fuzzy particle swarm optimization algorithm was designed. The premise is to meet the constraints of operation conditions. The active network loss was reduced and the static reactive power optimization mathematical model of electromechanical system was constructed by changing the voltage and reactive power distribution of system. Meanwhile, the voltage did not exceed the limit, and the discrete control variables were limited by the maximum allowable action times, so that the dynamic reactive power optimization mathematical model of electromechanical system was built by minimizing the sum of network loss in twenty-four hours of a day. The particle swarm algorithm was optimized by adaptive adjustment strategy, and then the particle position of particle swarm optimization algorithm was updated. Moreover, the static and dynamic reactive power optimization mathematical model of electromechanical system was solved. Finally, the reactive power optimization control of the electromechanical system is realized. Experimental results show that the proposed method has high convergence performance, so it is able to realize the precise control of reactive power optimization for electromechanical system and eliminate the voltage exceeding specified limits of electromechanical system. In this way, the node voltage can always be within the specified range.

Exploring a New Adaptive Routing Based on the Dijkstra Algorithm in Optical Networks-on-Chip.

The photoelectric hybrid network has been proposed to achieve the ultrahigh bandwidth, lower delay, and less power consumption for chip multiprocessor (CMP) systems. However, a large number of optical elements used in optical networks-on-chip (ONoCs) generate high transmission loss which will influence network performance severely and increase power consumption. In this paper, the Dijkstra algorithm is adopted to realize adaptive routing with minimum transmission loss of link and reduce the output power of the link transmitter in mesh-based ONoCs. The numerical simulation results demonstrate that the transmission loss of a link in optimized power control based on the Dijkstra algorithm could be maximally reduced compared with traditional power control based on the dimensional routing algorithm. Additionally, it has a greater advantage in saving the average output power of optical transmitter compared to the adaptive power control in previous studies, while the network size expands. With the aid of simulation software OPNET, the network performance simulations in an optimized network revealed that the end-to-end (ETE) latency and throughput are not vastly reduced in regard to a traditional network. Hence, the optimized power control proposed in this paper can greatly reduce the power consumption of s network without having a big impact on network performance.

Generalized User Grouping in NOMA: An Overlapping Perspective

Non-orthogonal multiple access (NOMA) is regarded as a promising technology to provide high spectral efficiency and support massive connectivity in 5G systems. Traditionally, NOMA user grouping is non-overlapping, leading to a waste of power resources within each NOMA group. Motivated by this, in this paper we propose a novel generalized user grouping (GuG) concept for NOMA from an overlapping perspective, which allows each user to participate in multiple user groups but subject to individual maximum power constraint. In order to achieve effective GuG and maximize the system sum rate, we formulate a joint power control and GuG optimization problem. Then we further provide a machine learning-based GuG scheme to obtain the optimized feasible GuG and the optimal power control solutions efficiently, in which the established machine learning-based model is exploited to explore the relative relationships of channel gains of users and obtain several fixed grouping patterns via Merge operation. Simulation results verify the efficiency of GuG in NOMA systems and indicate that compared with traditional NOMA user grouping schemes, our proposed GuG scheme achieves significant performance gains in terms of system sum rate.

Optimized Power Control in Internet of Vehicles Based on Serial Interference Cancellation and User Matching

With the significant advantages of 5G technique such as higher transmission rate and lower transmission delay, it is more in line with the requirements of the connected vehicle scenario. In addition, the serial interference cancellation (SIC) technology in 5G technology can further improve the communication quality compared to a single one-to-one multiplexing method. Taking the dynamic communication characteristics of Internet of Vehicles into consideration, the robust optimization problem is formulated, which attempts to maximization the total throughput of vehicle users under the changing channel gains. The channel uncertainty is formulated as probability constraints, and they are transformed to the deterministic form to achieve a convex optimization problem. And the Hungarian algorithm is introduced to allocate the available channels to the V2V pairs. With the proposed joint resource allocation algorithm, the maximum throughput can be obtained with the lower algorithm complexity. Also, the robustness against the changing channel gains is guaranteed and the outage probability of users is kept below the pre-given outage threshold.

Deep Learning-Based Power Control Scheme With Partial Channel Information in Overlay Device-to-Device Communication Systems

In the overlay device-to-device (D2D) communication systems, transmit power control is critical to better manage interference, so that the sum rate is maximized. Such power control for sum-rate optimization is NP-hard, which is typically tackled by iterative algorithms such as weighted minimum mean square error (WMMSE) method. However, the iterative power control schemes inherently incur high complexity and excessive latency. To overcome the limitations, we propose a deep learning-based power control scheme with reduced complexity and latency, where partial and outdated channel state information (CSI) is considered. Using a deep neural network (DNN)-based approach, we formulate an optimization problem to maximize the spectral efficiency under the constraints of user fairness and energy efficiency, where the DNN-based method is based on unsupervised learning with no label data generation process. In addition, a CSI reporting method based on the channel-to-interference power ratio is proposed for partial CSI feedback, which considerably reduces the feedback overhead. Through simulations, we show the results of the spectral efficiency, energy efficiency, and fairness performance for various topographical sizes and channel correlation coefficients. Also, it is shown that the proposed scheme achieves better spectral efficiency and energy efficiency than the WMMSE scheme even when it uses a small amount of CSI feedback.

Spectral Efficiency Improvement and Power Control Optimization of Massive MIMO Networks

Higher requirements must be put forward about wireless signal transmission in harsh environments. Energy loss mitigation, channel estimation, noise interference reduction demands high quality of service in wireless communication. In the fifth generation wireless communication, massive multiple-input multiple-output (MIMO) networks have been used and play a significant role to fulfill the requirements. Cell-Free massive MIMO networks are recognized as possible solution in the future wireless communication. Spectral efficiency (SE) is a very important index in assessing massive MIMO networks. This article tries to optimize both SE and power control of cell-free massive MIMO networks. Both uplink and downlink transmission are included in the study. The optimization model contains both SE and power control of massive MIMO networks. To tackle the model, a novel ensemble method is developed inspired by ensemble learning. The ensemble method is built up on the neighborhood field optimization method. The goodness of the developed ensemble method is verified by comparing with genetic algorithm, gradient descent method and Newton method. Extensive simulations are performed to study the optimization of SE and power control. Different wireless sensors are taken into consideration to simulate different requirements of massive MIMO networks. The results demonstrate the effectiveness of the proposed method for applying in massive MIMO networks.

Optimal Smart Inverter Control for PV and BESS to Improve PV Hosting Capacity of Distribution Networks Using Slime Mould Algorithm

In this study, an optimal reactive power (Volt/VAr) control of smart inverters for photovoltaic (PV) and battery energy storage systems (BESSs) to improve the PV hosting capacity (PVHC) of distribution networks is proposed. The primary objective of the proposed method is to improve the PVHC of a distribution network by determining the optimal oversize, dispatch, and control setting of the Volt/VAr functions of the smart inverters for both PVs and BESSs. Concurrently, the optimal locations of the PVs and BESSs are determined. The problem is formulated as a multi-objective mixed-integer nonlinear optimization to maximize the PVHC and minimize the voltage deviation simultaneously. A bio-inspired metaheuristic optimization method, i.e., the slime mould algorithm (SMA), is employed to solve the optimization problem. To assess the efficacy of the proposed PVHC improvement method, extensive simulations are conducted on an IEEE 33-node system using MATLAB software. The simulation results verify that the proposed method improves the PVHC of the distribution network compared to different cases and the default Volt/VAr control settings of the smart inverters. Furthermore, the SMA optimization method provides superior performance in finding the optimal PVHC of a distribution network compared to the conventional metaheuristic optimization methods.

Research on Marine DAB bidirectional DC converter based on DPS

This paper takes the application of bidirectional DC-DC converter in Marine hybrid power system as the background, the problems of high reflow power and current stress in dual active bridge converter under traditional phase shift control are studied in this paper. Based on the dual phase shift control strategy, the power transmission optimization algorithm model is analyzed and derived, and a control method for optimizing mode switching is proposed. The optimized control algorithm is validated by Matlab/Simulink simulation experiment. The results show that the reflow power and current stress optimization control algorithm can effectively reduce the reflow power and the current stress of the converter, so as to improve the operation performance of DAB converter in high-power ship hybrid power system.

Robust Optimal Power Control and Subcarrier Allocation in Uplink OFDMA Network With Assistance of Mobile Relay

Compared with the traditional fixed relay assisted communication network, the mobile relay assisted wireless communication network has greater communication coverage and better flexibility. It enables to extend the lifetime of a communication system and improve the throughput of uses. Due to the time-varying nature of the actual communication environment and the existence of various interferences, there are uncertain factors in the communication channel that affect the quality of service of users. In this paper, we study the energy efficiency maximization problem based on power control, mobile relay selection, and subcarrier allocation under uncertain channel. The proposed mobile relay selection and subcarrier allocation algorithm improves the energy efficiency of the system while ensuring the normal communication of mobile users. Probabilistic constraints are introduced into the optimization problem to describe the uncertain channel, and after the mathematical transformation, it becomes a solvable form. The subcarrier allocation algorithm and distributed robust power control algorithm are also presented. Simulation results show that the improved system energy efficiency has been achieved and the proposed algorithm behaves better scalability and robustness under dynamic communication environment.

Deep Reinforcement Learning for Power Controlled Channel Allocation in Wireless Avionics Intra-Communications

Wireless avionics intra-communications (WAIC) can play an important role in alleviate the issue of fuel consumption, stability and maintenance costs over traditional wired avionics systems. However, in order for WAIC system to coexist with other systems in aircraft, the transmitted power level of WAIC is strictly limited to 50 mW. Meanwhile, WAIC require extremely low outage probability for the safety-critical avionics applications. Hence, it is urgently needed to effectively allocate channels and utilize the limited power while ensuring the reliability and real-time performance of the WAIC system. In this paper, a deep reinforcement learning (DRL)-based power controlled channel allocation (DRL-PCCA) scheme for WAIC network is proposed, with physical layer of frequency hopping orthogonal frequency division multiplexing (FH-OFDM). First, we formulate a sub-bands allocation and power control optimization problem whose aim is to minimize the overall transmit power provided that all end nodes achieve their requested data rates and desired bit error rate (BER). However, the problem formulated is non-convex and NP-hard. To tackle this problem, we propose a DRL-based scheme, which can effectively solve the optimization problem of sequence decision making in complex environment by using neural networks to extract spatial correlation features of WAIC. In particular, a reliability framework for WAIC is presented to analyse the performance of the proposed scheme against the system requirements of the flight certification. Simulation results demonstrate that the performance of proposed DRL-based scheme is superior to the traditional power control and channel allocation scheme.

Optimal Output Power Control of Switched Reluctance Generator at a Constant Speed

In order to control the output power of a switched reluctance generator(SRG) at a constant speed, the output power of SRG is theoretically analyzed by using freewheeling control. First, through a theoretical analysis, a finite element simulation and an experiment, it was verified that the output power of SRG cannot be improved by using freewheeling control with a single pulse control method(SPCM). Then, the maximum output power can be obtained by optimizing the turn off angles of SPCM at a constant speed, and at the same time, the formula of the optimal turn-off angle was presented, which meets the criterion for the output power maximization. Finally, numerical simulation and experimental results demonstrated the validity of the theoretical analysis.

Constrained Utility Maximization in Dual-Functional Radar-Communication Multi-UAV Networks

In this paper, we investigate the network utility maximization problem in a dual-functional radar-communication multi-unmanned aerial vehicle (multi-UAV) network where multiple UAVs serve a group of communication users and cooperatively sense the target simultaneously. To balance the communication and sensing performance, we formulate a joint UAV location, user association, and UAV transmission power control problem to maximize the total network utility under the constraint of localization accuracy. We then propose a computationally practical method to solve this NP-hard problem by decomposing it into three sub-problems, i.e., UAV location optimization, user association and transmission power control. Three mechanisms are then introduced to solve the three sub-problems based on spectral clustering, coalition game, and successive convex approximation, respectively. The spectral clustering result provides an initial solution for user association. Based on the three mechanisms, an overall algorithm is proposed to iteratively solve the whole problem. We demonstrate that the proposed algorithm improves the minimum user data rate significantly, as well as the fairness of the network. Moreover, the proposed algorithm increases the network utility with a lower power consumption and similar localization accuracy, compared to conventional techniques.

Optimal Power Control, Scheduling, and Energy Harvesting for Wireless Networked Control Systems

Communication system design for wireless networked control systems (WNCSs) requires strict timing, reliability and lifetime guarantees despite limited battery resources and the non-idealities introduced by wireless networking such as delays. In this paper, we introduce radio frequency (RF) energy harvesting paradigm into WNCS framework for the first time in the literature. We study the optimal power control, energy harvesting and scheduling problem with the objective of providing maximum level of adaptivity under periodicity, delay and reliability requirements. We show that the power allocation problem is separable from the scheduling problem at optimality and provide the exact expression for optimal power control. The scheduling problem is then formulated as a mixed integer linear programming (MILP) problem and proven to be NP-Hard. For the scheduling, we propose polynomial-time heuristic algorithms motivated by the analogy between scheduling sensor nodes with energy harvesting requirements over time units and jobs with sequence dependent setup times on identical machines. We prove the theoretical worst-case bound for the performance of these heuristics. We show via extensive simulations that the proposed algorithms perform close-to-optimal and significantly better than Earliest Deadline First (EDF) algorithm in terms of adaptivity, delay, reliability and average runtime.