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Nuclear power plants need a large number of control systems to achieve effective control and safe operation of the system, in which nuclear power plant core is the key component of radioactive nuclear fuel heat source, and reactor power control is related to the safety and economy of nuclear power plant operation. Therefore, it is of great significance to optimize the design of nuclear reactor power controller. In the controller design stage, the control parameters of PID controller will be fixed in advance, which makes the control effect of PID controller has a certain degree of optimization space. In order to solve the problem that traditional PID controller is difficult to accurately deal with the nonlinear power control in the high power range, this study derived and established a reactor core model for a pressurized water reactor nuclear power plant. The core model includes heat transfer equation, neutron dynamics equation and reactivity equation. In this study, an adaptive controller based on deep reinforcement learning based on policy gradient (deep deterministic policy gradient algorithm) combined with PID (proportional integral derivative) controller was used to simulate power control, and a reward function was constructed. The reward function can be used to represent the optimization of several control evaluation indexes such as response time, threat time, control accuracy, overshoot and oscillation. The depth deterministic policy gradient algorithm can realize real-time optimization policy learning of PID controller control parameters by interacting with core model in real time. After several groups of working conditions with different power levels and different power switching modes were tested. The simulation results show that: In the 100%FP-90%FP step power reduction process (training condition), compared with the traditional PID controller, the self-adaption power controller designed based on the depth deterministic policy gradient algorithm has faster response speed, higher control accuracy and stability. At the same time, under the conditions (test conditions) of 40%FP-30%FP step power reduction process, 90%FP-100%FP step power increase process, 30%FP-40%FP step power increase process, 100%FP-30%FP linear power reduction process and 30%FP-100%FP linear power increase process, The control effect of the self-adaption power controller designed based on the depth deterministic policy gradient algorithm is also significantly better than that of the traditional PID controller, which indicates that the controller designed by this method has high robustness and can accurately map the power variation information of the pile type to the optimal control parameters of the PID controller. The proposed method can accurately and quickly control the core power, and track load changes.

Abstract Facing the exponential demand for massive connectivity and the scarcity of available resources, next-generation wireless networks have to meet very challenging performance targets. Particularly, the operators have to cope with the continuous prosperity of the Internet of things (IoT) along with the ever-increasing deployment of machine-type devices (MTDs). In this regard, due to its compelling benefits, non-orthogonal multiple access (NOMA) has sparked a significant interest as a sophisticated technology to address the above-mentioned challenges. In this paper, we consider a hybrid NOMA scenario, wherein the MTDs are divided into different groups, each of which is allocated an orthogonal resource block (RB) so that the members of each group share a given RB to simultaneously transmit their signals. Firstly, we model the densely deployed network using a mean field game (MFG) framework while taking into consideration the effect of the collective behavior of devices. Then, in order to reduce the complexity of the proposed technique, we apply the multi-armed bandit (MAB) framework to jointly address the resource allocation and the power control problem. Thereafter, we derive two distributed decision-making algorithms that enable the users to autonomously regulate their transmit power levels and self-organize into coalitions based on brief feedback received from the base station (BS). Simulation results are given to underline the equilibrium properties of the proposed resource allocation algorithms and to reveal the robustness of the proposed learning process.

Abstract This paper proposes, a novel soft‐switching bidirectional direct current to direct current (DC–DC) converter based on switched‐capacitor and coupled‐inductor. The proposed converter has a simple structure and utilizes four switches and a three‐winding coupled inductor and can achieve a high voltage conversion ratio in both step‐up/step‐down operations. By employing the switched‐capacitor technique, voltage stress across the power switches is low and therefore low voltage rating switches can be adopted to reduce the losses of their conduction. Soft‐switching characteristic of the proposed converter reduces the switching loss of active power switches and raises the conversion efficiency. This paper presents theoretical analysis for all operating modes of the converter, voltage conversion ratio, voltage and current stresses of all power switches. Finally, to test the validity of theoretical results and demonstrate the performance of the converter, experimental results are provided.

The integration of multiterminal hybrid HVDC grids connecting LCC- and VSC-based networks faces several technical challenges such as DC fault isolation, ensuring multi-vendor interoperability, managing high DC voltage levels, and facilitating high-speed power reversal without interruptions. The two-stage DC-DC converter emerges as a key solution to address these challenges. By implementing the modular multilevel converter (MMC) structure, the converter's basic topology includes half-bridge sub-modules on the VSC side and full-bridge sub-modules on the LCC side. However, while this topology has been discussed in the literature, its connection to an LCC-based network with controlled current magnitude lacks detailed analysis regarding operational challenges, control strategies under various scenarios, and design considerations. This paper fills this gap by providing comprehensive mathematical analysis, design insights, and control strategies for the modular DC-DC converter to regulate DC voltage on the LCC-HVDC side. Additionally, the proposed control scheme minimizes the interfacing inductor between the two bridges, ensuring uninterrupted power flow during reversal and effective handling of DC faults. Validation through Control-Hardware-in-the-Loop testing across diverse operational and fault scenarios, along with a comparative analysis of different converters, further strengthens the findings.© 2017 Elsevier Inc. All rights reserved.

Abstract Wind‐Diesel Hybrid Systems (WDHSs) integrate wind turbines into diesel power systems, reducing costs and emissions in isolated grids. Due to the no‐load consumption of the Diesel Generators (DGs), fuel savings are only possible when the DGs are shut down. This requires a proper implementation of the frequency control to avoid perturbations because of the wind speed variations. During wind‐only (WO) operation, the Synchronous Machine (SM) generates the isolated grid voltage and the frequency controller varies the energy stored/supplied by an Energy Storage System and consumed by the controllable loads to balance the power. In this paper, a Battery‐based Energy Storage System (BESS) uses Li‐Ion batteries with a Dual Active Bridge (DAB) and a grid‐tie inverter connected to the isolated network. The controllable load is an Irrigation Water Supply System (IWSS), consisting of a pump supplying water to a reservoir tank. The pump is driven by a variable speed drive that uses a Permanent Magnet Synchronous Motor (PMSM). The coordinated control of BESS and IWSS gives full priority to the BESS for harnessing the wind potential whereas the IWSS consumes the excess of wind power. The full Wind Diesel Power System (WDPS) is modelled and simulated to validate the proposed system for different case scenarios.

Abstract The AIoT, with its artificial intelligence capabilities, can further enhance Device‐to‐Device (D2D) communication. Based on Non‐Orthogonal Multiple Access (NOMA), D2D technology can effectively alleviate wireless spectrum resource pressure and improve the capacity of heterogeneous cellular networks. However, it also introduces significant system interference issues. In this paper, a resource allocation algorithm is proposed for the NOMA‐D2D heterogeneous cellular network, based on a multi‐agent deep reinforcement learning framework. Firstly, the algorithm allocates appropriate channels to D2D clusters. Then, the power allocation factors and D2D transmit power are jointly optimized to suppress the interference and improve the system performance. Simulation results show that both the channel allocation efficiency and the power control performance of the system can be significantly improved.

In a vehicular status update network based on broadcast communication, a D/Geo/1/1 queuing mechanism with minimum timestamp priority was adopted to transmit data packets.The queuing theory was used to analyze the age-ofinformation (AoI) outage probability of the receivers and the average transmit power of the transmitter.Based on the monotonicity of the AoI outage probability with respect to packet sampling rate and transmit power, an iterative algorithm for jointly optimizing sampling rate and transmit power was proposed to minimize the maximum AoI outage probability of all receivers under power constraint, which fairly improved information freshness of all receivers under sampling and power control.Simulation results verify the accuracy of theoretical analysis and the efficiency of the algorithm.

The resource allocation and optimization scheme was studied in a coexistence scenario of unmanned aerial vehicle (UAV) and cellular communication network.To improve spectrum efficiency of the system, UAV users could reuse the cellular spectrum resources to access the network through full duplex or half duplex device-to-device technique.Additionally, a joint access control, mode selection, power control and resource allocation optimization problem was formulated to maximize the overall throughput of the network while ensuring quality of service requirements for both UAV users and ground cellular users.Specifically, the phase 1 method in the convex optimization was adopted for access control and feasibility check, and then the convex and concave procedure (CCCP) iterative algorithm was used to solve the power control problem for feasible UAV user pairs.By using this local optimum value, the original optimization problem can be simplified into a weighted maximization problem.Finally, the Kuhn-Munkres (KM) algorithm was used to match the optimal channel resources and obtain the global optimal throughput value of the system.Numerical results show that the proposed scheme can significantly improve the performance of system.

Facing the exponential demand for massive connectivity and the scarcity of available resources, next-generation wireless networks have to meet very challenging performance targets. Particularly, the operators have to cope with the continuous prosperity of the Internet of things (IoT) along with the ever-increasing deployment of machine-type devices (MTDs). In this regard, due to its compelling benefits, non-orthogonal multiple access (NOMA) has sparked a significant interest as a sophisticated technology to address the above-mentioned challenges. In this paper, we consider a hybrid NOMA scenario, wherein the MTDs are divided into different groups, each of which is allocated an orthogonal resource block (RB) so that the members of each group share a given RB to simultaneously transmit their signals. Firstly, we model the densely deployed network using a mean field game (MFG) framework while taking into consideration the effect of the collective behavior of devices. Then, in order to reduce the complexity of the proposed technique, we apply the multi-armed bandit (MAB) framework to jointly address the resource allocation and the power control problem. Thereafter, we derive two distributed decision-making algorithms that enable the users to autonomously regulate their transmit power levels and self-organize into coalitions based on brief feedback received from the base station (BS). Simulation results are given to underline the equilibrium properties of the proposed resource allocation algorithms and to reveal the robustness of the proposed learning process. [ABSTRACT FROM AUTHOR]

This paper proposes a Power Harmonic Suppression (Damping) (PHS) based reference current generation scheme, which focuses on simultaneous mitigation of harmonic pulsations in electromagnetic torque, stator real and reactive powers, and DC‐link voltage while retaining sinusoidal stator and grid currents and unity power factor at grid side. The crux of this PHS method lies in its ability to eliminate the harmonic powers developed in the DFIG stator and grid at 100Hz, 200Hz, 300Hz, 400Hz, and 600 Hz owing to second, fifth, and seventh harmonic grid voltages. This is achieved as a result of the elimination of natural fluxes developed in the DFIG air gap due to second, fifth, and seventh harmonic distortions in grid voltage. For this, a single concurrent reference current generation technique is developed for RSC and GSC to mitigate the oscillations of electromagnetic torque, stator real and reactive powers, and ripples in DC‐link voltage. Further, the reference rotor currents (positive, negative, fifth, and seventh sequence) are compared with the actual rotor currents using a PI controller to retain sinusoidal stator and grid currents. The proposed scheme's efficacy is validated with PSCAD/EMTDC simulations and using OPAL‐RT OP4510 real‐time simulator, on a 2.3 kVA DFIG. [ABSTRACT FROM AUTHOR]

This paper proposes, a novel soft‐switching bidirectional direct current to direct current (DC–DC) converter based on switched‐capacitor and coupled‐inductor. The proposed converter has a simple structure and utilizes four switches and a three‐winding coupled inductor and can achieve a high voltage conversion ratio in both step‐up/step‐down operations. By employing the switched‐capacitor technique, voltage stress across the power switches is low and therefore low voltage rating switches can be adopted to reduce the losses of their conduction. Soft‐switching characteristic of the proposed converter reduces the switching loss of active power switches and raises the conversion efficiency. This paper presents theoretical analysis for all operating modes of the converter, voltage conversion ratio, voltage and current stresses of all power switches. Finally, to test the validity of theoretical results and demonstrate the performance of the converter, experimental results are provided. [ABSTRACT FROM AUTHOR]

The integration of multiterminal hybrid HVDC grids connecting LCC- and VSC-based networks faces several technical challenges such as DC fault isolation, ensuring multi-vendor interoperability, managing high DC voltage levels, and facilitating high-speed power reversal without interruptions. The two-stage DC-DC converter emerges as a key solution to address these challenges. By implementing the modular multilevel converter (MMC) structure, the converter's basic topology includes half-bridge sub-modules on the VSC side and full-bridge sub-modules on the LCC side. However, while this topology has been discussed in the literature, its connection to an LCC-based network with controlled current magnitude lacks detailed analysis regarding operational challenges, control strategies under various scenarios, and design considerations. This paper fills this gap by providing comprehensive mathematical analysis, design insights, and control strategies for the modular DC-DC converter to regulate DC voltage on the LCC-HVDC side. Additionally, the proposed control scheme minimizes the interfacing inductor between the two bridges, ensuring uninterrupted power flow during reversal and effective handling of DC faults. Validation through Control-Hardware-in-the-Loop testing across diverse operational and fault scenarios, along with a comparative analysis of different converters, further strengthens the findings. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

Wind power systems (WPs) are complex non‐linear systems with varying parameters affected by environmental changes, including wind speed fluctuations. Extracting maximum power from WPs poses a significant challenge due to these factors. Direct power control (DPC) is a highly effective technique known for its simplicity and ease of implementation. However, it suffers from power ripples caused by the use of hysteresis comparators and switching tables that operate at variable frequencies. To address this issue, this paper presents the robust neural controller (NC) based on DPC, which replaces the switching tables. The Double‐Fed Induction Generator (DFIG) is the chosen generator for the studied WP system due to its advantageous features. The NC‐DPC effectively regulates the exchange of active and reactive powers between the DFIG and the system, maximizing power extraction from the WP system while reducing Total Harmonic Distortion and enhancing overall system quality. The effectiveness of the NC‐DPC is evaluated through MATLAB simulations and further supported by experimental data obtained using the Real‐Time Interface of the dSPACE‐DS1104 Controller card. [ABSTRACT FROM AUTHOR]

Device-to-device (D2D) communication is a new technology in cellular networks which enables direct communications between nearby devices. It could reuse the uplink spectrum of the cellular users (CUs) thus will introduce interference. Efficient resource allocation methods can coordinate interference and further improve network capacity. The difficulty of obtaining perfect channel state information (CSI) makes the resource allocation more complex. By using the statistical information of the unknown channel fading factors, we propose an optimal power control algorithm for any CU and any D2D pair sharing the same channel. Based on that, we formulate the resource allocation problem for multiple CUs and multiple D2D pairs with incomplete CSI as a maximum weight matching problem. We then solve the problem by combining the optimal power control algorithm and the maximum weight matching algorithm. Simulation results demonstrate the efficiency of our algorithms in achieving high D2D rates while guaranteeing the minimum rates required by CUs. [ABSTRACT FROM AUTHOR]

Copyright of Atomic Energy Science & Technology is the property of Editorial Board of Atomic Energy Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Southeast University / Dongnan Daxue Xuebao is the property of Journal of Southeast University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The device-to-device communication (D2D) concept allows direct communication between nearby devices without a base station. At the same time, cellular resources are reused. It reduces the end-to-end delay of D2D active users significantly. Most of the traditional methods consider allocating resources by downlink or uplink alone. The present study considers a novel hybrid approach for joint downlink and uplink to allocate resources, maximizing the network throughput. Further, it minimally restricts cellular and D2D pairs' interference and ensures smooth D2D communication. The challenge is that power control and Quality of service constraints are seriously degraded by strong intra-cell and inter-cell interference due to spectrum reusability and deployment. A hybrid structure that exploits efficient resource allocation is needed to tackle this situation. The optimization problem is formulated as a mixed-integer non-linear problem that is usually NP-hard. Such a problem is divided into two stages, namely channel assignment and power allocation. The factors considered for the objective problem of resource allocation are the transmission power of the cellular user, D2D active user, base station, connection distance, and Quality of Service constraints. The proposed novel hybrid scheme can improve network throughput and improves spectrum efficiency. The numerical results imply that the hybrid method in the proposal functions efficiently and is verified by comparing it with the present joint resource allocation methods. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As a contender in the competitive landscape of next-generation wireless communication technologies, visible light communication (VLC) stands out due to its potential for enhancing transmission rates and spectrum resource utilization. VLC offers various advantages, including license-free operation, high confidentiality, and cost-effectiveness. However, practical implementation faces challenges stemming from the limited modulation bandwidth of light-emitting diodes (LEDs), constraining system capacity and VLC communication rates. To address this limitation, non-orthogonal multiple access (NOMA) emerges as a novel multiple access strategy, particularly suitable for enhancing the capacity and communication rates of downlink VLC systems through power multiplexing. This paper delves into the energy-efficient design of joint LED association and power allocation (LA–PA) for downlink NOMA-based VLC systems. Through an analysis of channel capacity, we transform the non-convex energy-efficient optimization model, accounting for signal non-negativity, per-LED optical power constraints, and user rate constraints, into a convex form. Subsequently, we propose an iterative power allocation algorithm to attain solutions for the optimization problem with pre-established LED associations. Furthermore, we derive a feasibility condition for an LED association, considering signal non-negativity, per-LED optical power constraints, power constraints for successive interference cancellation (SIC), and channel gain between transceiver signals. This condition identifies feasible LEDs capable of maximizing energy efficiency (EE) when combined with the aforementioned power allocation algorithm. Finally, we illustrate the superiority of the joint LA–PA scheme in terms of the EE, transmission reliability, and transmission capacity performance gain over NOMA in the context of VLC. [ABSTRACT FROM AUTHOR]

Wind‐Diesel Hybrid Systems (WDHSs) integrate wind turbines into diesel power systems, reducing costs and emissions in isolated grids. Due to the no‐load consumption of the Diesel Generators (DGs), fuel savings are only possible when the DGs are shut down. This requires a proper implementation of the frequency control to avoid perturbations because of the wind speed variations. During wind‐only (WO) operation, the Synchronous Machine (SM) generates the isolated grid voltage and the frequency controller varies the energy stored/supplied by an Energy Storage System and consumed by the controllable loads to balance the power. In this paper, a Battery‐based Energy Storage System (BESS) uses Li‐Ion batteries with a Dual Active Bridge (DAB) and a grid‐tie inverter connected to the isolated network. The controllable load is an Irrigation Water Supply System (IWSS), consisting of a pump supplying water to a reservoir tank. The pump is driven by a variable speed drive that uses a Permanent Magnet Synchronous Motor (PMSM). The coordinated control of BESS and IWSS gives full priority to the BESS for harnessing the wind potential whereas the IWSS consumes the excess of wind power. The full Wind Diesel Power System (WDPS) is modelled and simulated to validate the proposed system for different case scenarios. [ABSTRACT FROM AUTHOR]

The paper evaluates how the control of transmitted power affects the intensity of radiation in a mobile network cell. Cell models without power control, with standard power control and a model with power control and channel reallocation are considered. The relative reduction of radiation is evaluated and several examples of calculations are presented. Remarks are given on the dependence of radiation reduction on the number of traffic channels, traffic intensity and signal attenuation coefficient. The assessment procedure and results are based on previously verified traffic process simulation models. [ABSTRACT FROM AUTHOR]

The AIoT, with its artificial intelligence capabilities, can further enhance Device‐to‐Device (D2D) communication. Based on Non‐Orthogonal Multiple Access (NOMA), D2D technology can effectively alleviate wireless spectrum resource pressure and improve the capacity of heterogeneous cellular networks. However, it also introduces significant system interference issues. In this paper, a resource allocation algorithm is proposed for the NOMA‐D2D heterogeneous cellular network, based on a multi‐agent deep reinforcement learning framework. Firstly, the algorithm allocates appropriate channels to D2D clusters. Then, the power allocation factors and D2D transmit power are jointly optimized to suppress the interference and improve the system performance. Simulation results show that both the channel allocation efficiency and the power control performance of the system can be significantly improved. [ABSTRACT FROM AUTHOR]

Cognitive Radio (CR) is developed to provide effective spectrum usage. CR is much significant in improving the efficiency of the global internet in applications. The evolutionary measurement technology is utilised to improve the evaluation of channel-state information. The outcome attained very few spectrums sensing in CR for complex mobility. A good optimisation method is needed to improve the accurate channel state prediction in successful channel access. Thus, this paper aims to implement a novel power and channel allocation mechanism with the help of a new Modified Levy Flight-based Artificial Ecosystem Optimisation (MLF-AEO) Optimisation Strategy. This paper achieves the optimal power control and channel allocation mechanism intending to solve the multiple objective functions based on the constraints like Interference among users, Outage Probability, and throughput. The superiority of the proposed algorithm is thoroughly verified by various simulation results. [ABSTRACT FROM AUTHOR]

With the introduction of a high proportion of new energy sources, the power system needs new means to enhance its regulating flexibility. High-energy-consuming industrial loads on the demand side have significant potential to improve the grid's regulating flexibility. Unplanned power adjustments can affect normal load production. This paper proposes a power control strategy that considers the normal production order of loads, aiming to balance load response capacity requirements and safe production order. Taking copper electrolysis load as an example, based on the load process flow and the power characteristics of production equipment, factors affecting load production order and methods for calculating impact weights are analyzed, and a strategy to reduce the demand response to production order caused by power control is proposed. The effectiveness of the power control strategy is verified through simulation examples, providing a feasible solution for industrial loads to participate in demand-side response. [ABSTRACT FROM AUTHOR]

The fast development of the sensors in the wireless sensor networks (WSN) brings a big challenge of low energy consumption requirements, and Peer-to-peer (P2P) communication becomes the important way to break this bottleneck. However, the interference caused by different sensors sharing the spectrum and the power limitations seriously constrains the improvement of WSN. Therefore, in this paper, we proposed a deep reinforcement learning-based energy consumption optimization for P2P communication in WSN. Specifically, P2P sensors (PUs) are considered agents to share the spectrum of authorized sensors (AUs). An authorized sensor has permission to access specific data or systems, while a P2P sensor directly communicates with other sensors without needing a central server. One involves permission, the other is direct communication between sensors. Each agent can control the power and select the resources to avoid interference. Moreover, we use a double deep Q network (DDQN) algorithm to help the agent learn more detailed features of the interference. Simulation results show that the proposed algorithm can obtain a higher performance than the deep Q network scheme and the traditional algorithm, which can effectively lower the energy consumption for P2P communication in WSN. [ABSTRACT FROM AUTHOR]

This study discusses performance of the power control strategy for a Wind Turbine/Fuel Cell Hybrid System with a Doubly Fed Induction Generator (DFIG)-Direct Current (DC) link configuration. To overcome high cost and the complex structure of the previous paper, a new DFIG-DC link configuration is proposed that uses a single Voltage Source Inverter (VSI) and a diode rectifier to connect the stator and rotor to the DC-link, respectively. Also, the DFIG-DC system is modeled and a Stator Field-Oriented Control (SFOC) is adopted to control the VSI to extract the maximum power generated by the wind turbine. The model and the control of the DC-DC boost converter used to connect the hydrogen fuel cell to the DC-link are established. To achieve better performance of the proposed DFIG-DC system, the power control strategy has been tested by using the dSpace DS1104 processor board and the experimental results show the performance of the control strategy in terms of parameters regulation under a different scenario. [ABSTRACT FROM AUTHOR]

A novel voltage boost system using DC‐inputs direct electric‐power converter (D‐EPC) is proposed. D‐EPC is an inverter that can control the distribution of power from two sources. In this study, a voltage boost system without boost chopper was developed using the power distribution control of D‐EPC. The power distribution control can control the voltage of the smoothing capacitor in the D‐EPC. The effectiveness of the proposed voltage boost system was verified using a prototype of the D‐EPC circuit and controller. [ABSTRACT FROM AUTHOR]

Wind energy has emerged as a pivotal practice in the contemporary energy landscape, generated through gridconnected power sources aligning with the vernacular principles of systemic approaches. This study explores the surge in initiation rates, offering insights into various factors impacting sustainable electricity production. Intriguingly, this research delves into the intricacies of managing the variability and uncertainty inherent in energy demand, catalysing the integration of grid-based solutions that enhance sustainability. It probes the dynamic nature of power supply paradigms, revealing a journey of continuous enhancement by applying cutting-edge resource methodologies. Amidst the backdrop of global shifts in electricity dynamics, this study uncovers the profound implications of energy depletion and wasteful consumption practices, spotlighting a burgeoning movement towards optimising grid electricity resources on a macro scale. The intricacies and nuances of power supply challenges are comprehensively dissected, offering valuable insights. Furthermore, the study explores the pivotal role played by information technology innovators in consolidating the predictability of wind energy, augmenting its viability. It also aligns with forward-looking reviews, underscoring the actionable strategies taken. The culmination of these efforts not only enhances predictability but also unlocks a spectrum of reflective and adaptive resources in wind energy utilisation. [ABSTRACT FROM AUTHOR]

Abstract Conflict game theory is employed to analyze the countermeasure relationship between radar systems and jammers. To address this issue, a novel utility function and a Conflict Power Allocation Game (CPAG) algorithm are introduced for determining the Nash Equilibrium (NE) solution of the game. The utility function enables the calculation of optimal power response functions for both the radar system and the jammer, with the NE solution of the CPAG algorithm obtained through numerical computation. Simulation results demonstrate the convergence of the proposed CPAG algorithm to the NE solution, validating its efficacy. Finally, an analysis of the relationship between the utility function and pricing factors is conducted based on the proposed CPAG algorithm and simulation outcomes.

The handoff rate and load balancing are two important issues that have a great impact on the spectrum and energy efficiency in the small cell networks. This paper investigates the handoff optimization in small cell networks with power-domain non-orthogonal multiple access that uses successive interference cancellation, considering the fairness among base stations. We study the joint base station association and power control problem by considering the motion of mobile users and load balancing in the small cell networks. Under the maximum allowable transmit power and minimum average-rate constraints, two optimization problems are formulated using the number of associated mobile users, the number of handoffs, and the transmit power of all MUs. The total power consumption minimization and the system-wide and handoff utility maximization problems are combined into a single-stage optimization problem through the weighted sum method. We solve the formulated problem using a game theory-based algorithm and primal decomposition theory. The simulation results show that our proposed algorithm can significantly reduce the frequent handoffs and bring a fair power-controlled BS association in small cell networks.

Unmanned Aerial Vehicles (UAVs) as aerial base stations to provide communication services for ground users is a flexible and cost-effective paradigm in B5G. Besides, dynamic resource allocation and multi-connectivity can be adopted to further harness the potentials of UAVs in improving communication capacity, in such situations such that the interference among users becomes a pivotal disincentive requiring effective solutions. To this end, we investigate the Joint UAV-User Association, Channel Allocation, and transmission Power Control (J-UACAPC) problem in a multi-connectivity-enabled UAV network with constrained backhaul links, where each UAV can determine the reusable channels and transmission power to serve the selected ground users. The goal was to mitigate co-channel interference while maximizing long-term system utility. The problem was modeled as a cooperative stochastic game with hybrid discrete-continuous action space. A Multi-Agent Hybrid Deep Reinforcement Learning (MAHDRL) algorithm was proposed to address this problem. Extensive simulation results demonstrated the effectiveness of the proposed algorithm and showed that it has a higher system utility than the baseline methods.

Haptic communications is recognized as a promising enabler of extensive services by enabling real-time haptic control and feedback in remote environments, e.g., teleoperation and autonomous driving. Considering the strict transmission requirements on reliability and latency, Device-to-Device (D2D) communications is introduced to assist haptic communications. In particular, the teleoperators with poor channel quality are assisted by auxiliaries, and each auxiliary and its corresponding teleoperator constitute a D2D pair. However, the haptic interaction and the scarcity of radio resources pose severe challenges to the resource allocation, especially facing the sporadic packet arrivals. First, the contention-based access scheme is applied to achieve low-latency transmission, where the resource scheduling latency is omitted and users can directly access available resources. In this context, we derive the reliability index of D2D pairs under the contention-based access scheme, i.e., closed-loop packet error probability. Then, the reliability performance is guaranteed by bidirectional power control, which aims to minimize the sum packet error probability of all D2D pairs. Potential game theory is introduced to solve the problem with low complexity. Accordingly, a distributed power control algorithm based on synchronous log-linear learning is proposed to converge to the optimal Nash Equilibrium. Experimental results demonstrate the superiority of the proposed learning algorithm.

In this paper, we jointly design the power control and position dispatch for Multi-Unmanned Aerial Vehicle (UAV)-enabled communication in Device-to-Device (D2D) networks. Our objective is to maximize the total transmission rate of Downlink Users (DUs). Meanwhile, the Quality of Service (QoS) of all D2D users must be satisfied. We comprehensively considered the interference among D2D communications and downlink transmissions. The original problem is strongly non-convex, which requires high computational complexity for traditional optimization methods. And to make matters worse, the results are not necessarily globally optimal. In this paper, we propose a novel Graph Neural Networks (GNN) based approach that can map the considered system into a specific graph structure and achieve the optimal solution in a low complexity manner. Particularly, we first construct a GNN-based model for the proposed network, in which the transmission links and interference links are formulated as vertexes and edges, respectively. Then, by taking the channel state information and the coordinates of ground users as the inputs, as well as the location of UAVs and the transmission power of all transmitters as outputs, we obtain the mapping from inputs to outputs through training the parameters of GNN. Simulation results verified that the way to maximize the total transmission rate of DUs can be extracted effectively via the training on samples. Moreover, it also shows that the performance of proposed GNN-based method is better than that of traditional means.

Edge data centers (EDCs) have been widely developed recently to supply delay-sensitive computing services, which impose prohibitively increasing electricity costs for EDC operators. This paper presents a new spatiotemporal reallocation (STR) method for energy management in EDCs. This method uses spare resources, including servers and energy storage systems (ESSs) within EDCs to reduce energy costs based on both spatial and temporal features of spare resources. This solution: 1) reallocates flexible workload between EDCs within one cluster; and 2) coordinates the electricity load of data processing, ESSs and distributed energy resources (DERs) within one EDC cluster to gain benefits from flexible electricity tariffs. In addition, this paper for the first time develops a Bit-Watt transformation to simplify the STR method and represent the relationship between data workload and electricity consumption of EDCs. Case studies justifying the developed STR method delivers satisfying cost reductions with robustness. The STR method fully utilized both spatial and temporal features of spare resources in EDCs to gain benefits from 1) varying electricity tariffs, and 2) maximumly consuming DER generation.

We consider a Multi-UAV multicarrier non-orthogonal multiple access (MC-NOMA) downlink network in which each UAV serves a group of ground users within its designated cell. Our goal is to maximize the total downlink rate for each cell by simultaneously optimizing the subcarrier assignment and power control. However, the need to maximize the downlink rate for each individual UAV cell introduces conflicts because the optimization objectives for different cells are inherently at odds with each other, making this a Multi-Objective Optimization Problem (MOOP). We applied the weighted Tchebycheff method to convert the MOOP into a Single-Objective Optimization Problem (SOOP). The resulting SOOP remains a Mixed-Integer Nonlinear Programming (MINLP) problem. To address this, we first relax the combinatorial subcarrier assignment variables into continuous variables and then apply a penalty method to enforce binary constraints. To handle the nonconvexity of the objective function and constraints, we utilize the Big-M method and Successive Convex Approximation (SCA) to decouple the product terms and deal with nonconvexity. We then employed an iterative approach to obtain a suboptimal solution and continued the process until convergence was achieved. Simulations demonstrate that this method effectively balances the trade-offs among different cells, achieves significant performance improvements and successfully approximates Pareto optimal solutions.

This study investigates power control algorithms for cell-free massive MIMO (CF-M-MIMO) systems, with the aim of enhancing spectral efficiency (SE) and overall system capacity. A novel Heuristic Iterative Power Control (HIPC) algorithm is proposed and compared against the Equal Power Control (EPC) and Max-Min Fairness Power Control (MMFPC) algorithms within both small-cell and CF-M-MIMO systems under Minimum Mean Squared Error (MMSE) and Maximum Ratio (MR) conditions. The simulation results demonstrate that the HIPC algorithm significantly outperforms both EPC and MMFPC by achieving higher SE and improving user fairness, particularly in CF-M-MIMO systems. Moreover, the robustness and effectiveness of the HIPC algorithm under MR conditions underscore its practical utility. These contributions provide important guidance on the design and implementation of CF-M-MIMO systems, establishing the HIPC algorithm as a valuable tool for optimizing power control and enhancing system performance.

We propose a novel power control technique called PC-ECGNN, which uses edge convolution to optimize power allocation in wireless IoT networks. PC-ECGNN leverages interference link distances as edge features and desired link channel gains as initial vertex features, iteratively updating vertex features based on neighbors and edge features. PC-ECGNN is the first technique to incorporate edge convolution into power control and has been customized for the considered scenario, optimizing the neural network structure to provide fast convergence and high performance simultaneously. Experimental results show that PC-ECGNN outperformed the state-of-the-art PC-MPGNN, achieving a 4% increase in average spectral efficiency and a 4dBm reduction in average transmit power compared to PC-MPGNN. Furthermore, our technique demonstrates advantages over existing methods in dynamic environmental changes. The proposed model, trained in a fixed environment, showed minimal performance degradation across various test environments different from the training setting, outperforming traditional models trained in individual environments. When applying meta-learning, the proposed model achieved better performance in each test environment after additional fine-tuning with only 1% of the pre-training epochs, compared to models trained with the full number of epochs in each individual test environment.

Energy conservation is crucial in wireless ad hoc sensor network design to increase network lifetime. Since communication consumes a major part of the energy used by a sensor node, efficient communication is important. Topology control aims at achieving more efficient communication by dropping links and reducing interference among simultaneous transmissions by adjusting the nodes’ transmission power. Since dropping links make a network more susceptible to node failure, a fundamental problem in wireless sensor networks is to find a communication graph with minimum interference and minimum power assignment aiming at an induced topology that can satisfy fault-tolerant properties. In this paper, we examine and propose linear integer programming formulations and a hybrid meta-heuristic GRASP/VNS (Greedy Randomized Adaptive Search Procedure/Variable Neighborhood Search) to determine the transmission power of each node while maintaining a fault-tolerant network and simultaneously minimize the interference and the total power consumption. Optimal biconnected topologies for moderately sized networks with minimum interference and minimum power are obtained using a commercial solver. We report computational simulations comparing the integer programming formulations and the GRASP/VNS, and evaluate the effectiveness of three meta-heuristics in terms of the tradeoffs between computation time and solution quality. We show that the proposed meta-heuristics are able to find good solutions for sensor networks with up to 400 nodes and that the GRASP/VNS was able to systematically find the best lower bounds and optimal solutions.

Cell-free massive MIMO (CF-mMIMO) networks leverage seamless cooperation among numerous access points to serve a large number of users over the same time/frequency resources. This paper addresses the challenges of pilot and data power control, as well as pilot assignment, in the uplink of a cell-free massive MIMO (CF-mMIMO) network, where the number of users significantly exceeds that of the available orthogonal pilots. We first derive the closed-form expression of the achievable uplink rate of a user. Subsequently, harnessing the universal function approximation capability of artificial neural networks, we introduce a novel multi-task deep learning-based approach for joint power control and pilot assignment, aiming to maximize the minimum user rate. Our proposed method entails the design and unsupervised training of a deep neural network (DNN), employing a custom loss function specifically tailored to perform joint power control and pilot assignment, while simultaneously limiting the total network power usage. Extensive simulations demonstrate that our method outperforms the existing power control and pilot assignment strategies in terms of achievable network throughput, minimum user rate, and per-user energy consumption. The model versatility and adaptability are assessed by simulating two different scenarios, namely a urban macro (UMa) and an industrial one.

This paper presents an analysis of fifth-generation (5G)-Advanced uplink system-level performance with the coexistence of extended reality (XR) and enhanced mobile broadband (eMBB) traffic. Dense urban (DU) and indoor hotspot (InH) deployments are studied. The study investigates the influence of uplink power control (UPC) parameters on the XR capacity and proposes strategies to manage eMBB inter-cell interference through traffic-specific UPC settings. By jointly optimizing UPC parameters for each traffic type, this research aims to minimize the eMBB throughput degradation while safeguarding the XR capacity. The findings reveal the impact of deployment scenarios on XR and eMBB capacity, and the trade-offs involved in the UPC optimization. These findings offer valuable guidance to cellular operators for optimizing network configurations to accommodate emerging XR traffic alongside existing services.

In this paper, we propose a novel power control in vehicular 5G-connected network using Deep Reinforcement Learning (DRL) algorithm. We investigate power allocation for Connected Autonomous Vehicles (CAVs) on uplink connections in mm-wave bands between the CAVs and Roadside Units (RSUs). Our objective is to achieve the desired uplink transmission capacity using the minimum required power and minimize co-channel interference for neighboring cells. To achieve this goal, we use the Proximal Policy Optimization (PPO) algorithm implemented by modified actor-critic architecture to solve the problem. In the proposed architecture, a Deep Neural Network (DNN) model is used to gain the desired outputs of the problem. The suggested approach is fully compatible with the existing 3GPP-based 5G architecture and uses the available quantized information in cellular users’ measurement reports which provides seamless integration within existing RAN architectures. The performance of the proposed algorithm is compared with multiple power control algorithms in various road conditions. Simulation results show that the proposed algorithm outperforms the 3GPP-based power control algorithm in the dynamic road environment.

This paper presents a grid-forming (GFM) voltage-source inverter (VSI) with direct current regulation for a hybrid wind-solar generator, enabling stable operation at very weak grid conditions and under faults. The GFM-VSI interfaces a hybrid wind-solar generator without an intermediate dc-dc conversion to increase the system efficiency. The wind generator comprises a wind turbine with a permanent magnet synchronous generator (PMSG) interfaced by a voltage-source rectifier (VSR). The PMSG-VSR and a solar photovoltaic (PV) array are connected to the GFM-VSI's dc-side. The VSR is responsible for extracting wind power with a power reserve option. The GFM-VSI is implemented to extract solar power with a power reserve capability and support the grid voltage or reactive power. The stable operation of the proposed system is validated under very weak grid conditions, and it is shown that a similar hybrid wind-solar system with grid-following control is unstable under the same weak grid conditions. A complete small-signal state-space model of the proposed hybrid system is developed and analyzed. Nonlinear time-domain simulations and real-time simulation tests verify the model's accuracy and show the proposed system's effective performance under challenging operating scenarios, such as grid uncertainties and faults.

Transmit power control (PC) will become increasingly crucial in alleviating interference as the densification of the wireless networks continues towards 6G. However, the practicality of most PC methods suffers from high complexity, including the sensing and signalling overhead needed to obtain channel state information. In a highly dense scenario such as the deployment of short-range cells installed within production entities, termed in-factory subnetworks (InF-S), sensing and signalling overhead become a major limitation. In this paper, we represent the InF-S as a graph and resort to graph neural networks for solving the power control problem. We present four graph-attribution methods requiring different degrees of channel information corresponding to different levels of sensing and signalling overhead and study the complexity and performance tradeoffs of the resulting power control graph neural network (PCGNN) algorithms. We then propose a PCGNN method with scalable sensing and signalling graph attribution which can meet the stringent outage target while maximizing the global performance by 10% relative to fixed power control. We further verified the size generalizability and robustness of the PCGNN methods to different network settings.

Device-to-device (D2D) communications underlaying cellular networks enhance system capacity and bandwidth utilization. In this approach, secondary users (D2D devices) share the radio resources allocated to primary users (cellular UE), achieving increased system capacity and spectrum efficiency. However, the allocation of inappropriate resources and transmission power to devices introduces excessive co-channel interference, which could impair primary user communication. The proposed research addresses this challenge by developing a Multi-Value Bipartite Matching (MBM) Algorithm that jointly allocates resources and transmission power for both primary and secondary users maintaining individual data rate constraints. Numerical analyses show that this approach outperforms existing algorithms in determining appropriate power levels while meeting specified constraints, ultimately improving system capacity and reducing interference.

This study presents a novel approach to design an optimal and energy-efficient communication system tailored for wireless monitoring system in nuclear power facilities. It addresses the unique challenges of such environments, including high throughput demands for the size expansion of wireless sensor networks (WSN), limited power availability with long service time requirements, and severe signal attenuation, error rates, and loss packets in harsh through-wall scenarios. The proposed system utilizes a low-power single input multiple output (SIMO) Ultra-Wideband (UWB) system with orthogonal frequency division multiplexing (OFDM), enhancing spectrum efficiency through frequency and spatial diversities. We introduce a modified water-filling algorithm, designed to optimally allocate power across subchannels based on varying channel conditions when total power budget is undefined. This algorithm specifically targets on achieving necessary system throughput, which is a critical parameter in communication designs. Our simulation results demonstrate significant energy savings and reductions in bit error rate and outage probability, offering a robust solution for nuclear safety. Furthermore, we emphasize the gap in the existing literature regarding channel models for harsh though-wall environments by developing a straightforward and comprehensive channel model using ray-tracing techniques and Friis’ transmission equations. This model’s accuracy is validated through a comparison of calculated results against experimentally measured results, verifying its effectiveness and applicability in different through-wall communication scenarios.

The number of users in wireless networks, such as mobile and Internet-of-Things networks, is witnessing a tremendous increase, turning the available frequency spectrum into a scarce resource that needs to be efficiently utilized. Cognitive radio (CR) is a key technology for achieving spectrum efficiency by continuously sensing and detecting frequency bands that are not used by licensed primary users (PU) and allowing unlicensed secondary users (SUs) to use them. One of the main challenges in CR is the design of a medium access control (MAC) protocol that ensures efficient spectrum sharing by SUs without disrupting the connectivity of PUs. To achieve that, many of the existing MAC protocols in the literature allow multiple SU transmissions to proceed simultaneously by performing batch-based power control decisions to limit mutual interference between them. Interestingly, the majority of such protocols are demand-rate unaware; i.e., all SUs are granted the same data rate, regardless of their data rate demand. In this paper, we highlight the severe drawbacks of demand-rate unawareness and propose the rate-aware power-controlled channel assignment (RPCCA) MAC protocol, which performs batch-based simultaneous channel assignment decisions to competing SUs along with power control to limit mutual interference, while taking into account the variable demand-rate across SUs. Simulation experiments have demonstrated that the RPCCA protocol offers substantial performance improvements over existing demand-rate unaware CR-based MAC protocols.