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User pairing plays an important role in device‐to‐device (D2D) relay communication, contributing significantly to maintaining low energy consumption, high throughput, and overall energy efficiency in the communication system. To achieve these purposes, an attention‐based long short‐term memory motion prediction model (AT‐LSTM) and propose a joint power control algorithm. Leveraging these techniques, we also propose a D2D user pairing algorithm, distance–power–SINR pairing algorithm (DPSPA), which comprehensively considers factors such as D2D communication distances, transmit power, and signal‐to‐interference‐plus‐noise ratio. Initially, the AT‐LSTM model is utilized to predict the location of users. Subsequently, the distance between the user terminal device and each communication point and the base station, filtering cache points, and non‐cache points within the D2D communication radius are calculated. Then, based on the distance, required transmission power, and signal‐to‐interference‐plus‐noise ratio of each point, the evaluation index (the best matching product) is obtained. Finally, the point with the maximum best matching product is selected for D2D direct communication mode, D2D relay communication mode, or cellular communication mode. Simulation results demonstrate that DPSPA effectively reduces system energy consumption, enhances system throughput, and improves overall energy efficiency. [ABSTRACT FROM AUTHOR]

In this paper, a single-phase five-level active neutral-point-clamped with coupled inductors (ANPC-CI) rectifier topology is studied to meet the needs of efficient and reliable power electronic converters. A sliding mode model predictive power control method is proposed to achieve high-efficiency energy conversion, precise voltage control, and low harmonic distortion. By analyzing the working states, establishing a mathematical model and introducing the theory of instantaneous power, the outer loop sliding mode power control and the inner loop model predictive power control algorithms are designed. When compared with traditional PI-DPC algorithm, this algorithm avoids the tedious setting of parameters, reduces input power fluctuations, and improves the system dynamic response speed. The superiority of the sliding mode model predictive power control is verified by simulation and experimental results. [ABSTRACT FROM AUTHOR]

The present study introduces a nature inspired improved liver cancer algorithm (ILCA) for solving the non‐convex engineering optimization issues. The traditional LCA (t‐LCA) inspires from the conduct of liver tumours and integrates biological ethics during the optimization procedure. However, t‐LCA facing stagnation issues and may trap into local optima. To avoid such issues and provide the optimal solution, there are some modifications are implemented into the internal structure of t‐LCA based on Weibull flight operator, mutation‐based approach, quasi‐opposite‐based learning and gorilla troops exploitation‐based mechanisms to enhance the overall strength of the algorithm to obtain the global solution. For validation of ILCA, the non‐parametric and the statistical analysis are performed using benchmark standard functions. Moreover, ILCA is applied to resolve the stochastic renewable‐based (wind turbines + PVs) optimal power flow problem using a modified RER‐based IEEE 57‐bus. The objective of this work is to obtain the minimum predicted power losses and enhance the predicted voltage stability. By incorporation of renewable resources into the modified IEEE57‐bus network can help the system to reduce the power losses from 5.6622 to 3.8142 MW, while the voltage stability is enhanced from 0.1700 to 0.1164 p.u. [ABSTRACT FROM AUTHOR]

Aiming to improve the voltage distribution and realize the proportional sharing of active and reactive power in the distribution network (DN), this article proposes a distributed optimal control strategy based on the grouping cooperation mechanism of the distributed generation (DG). The proposed strategy integrates the local information of the DG and the global information of the DN. Considering the high resistance/reactance ratio of DN, distributed optimization control strategies for node voltage control and active power management are developed with the consensus variable of active utilization rate. And distributed strategy for reactive power management is proposed with a consensus variable of reactive utilization rate. The convergence of the distributed control system for each group is proved. The validity and robustness of the proposed strategy are verified by several simulations in the IEEE 33‐bus system. [ABSTRACT FROM AUTHOR]

With the continuous expansion of the photovoltaic industry, the application of solar photovoltaic power generation systems is becoming increasingly widespread. Due to the obvious intermittency and volatility of photovoltaic power generation, integration of large‐scale photovoltaic power generation into the power grid can cause certain impacts on the security and stability of the grid. Photovoltaic power prediction is essential to solve this problem, as it can improve the quality of photovoltaic grid connection, optimize grid scheduling, and ensure the safe operation of the grid. In this article, the deep learning method is selected for photovoltaic power prediction. Based on the analysis of the OctConv (Octave Convolution) network structure, the AOctConv (Attention Octave Convolution) convolutional neural network structure is proposed, which is combined with the ResNet50 backbone network to obtain AOC‐ResNet50. It is then applied to the prediction of the generation of photovoltaic power. The prediction performance is compared with the ResNet50 network and the Oct‐ResNet50 network, and it is found that the AOC‐ResNet50 network has the best prediction performance, with an MAE of only 0.176888. Based on the exemplar work, a framework is proposed to illustrate this method. Its general application is discussed. [ABSTRACT FROM AUTHOR]

To provide inertia support to the power grid, the grid side frontend DC/AC inverter of the bidirectional electric vehicle (EV) charging facilities can use the virtual synchronous generator (VSG) control in its grid-side DC/AC converter. However, the existing VSG control cannot actually realize the inertia support due to the limitations of the existing cascaded power converters control structure of the bidirectional EV charger. This paper proposes a new control infrastructure for VSG based bi-directional EV charging facilities. The proposed control instantaneously tracks the inertial supporting surge power by DC/DC so as to stabilize the DC bus voltage. Meanwhile, the droop coefficient based power values are also fed forward to DC/DC controls to support grid frequency control over a larger time scale. Finally, the experiment results show that the proposed method improves the DC bus voltage stability of the EV charger while allowing the EV to participate in the power system frequency regulation, which is a key feature of the Vehicle to Grid applications. [ABSTRACT FROM AUTHOR]

In this paper, a modular resonant inverter is proposed for high frequency industrial heating applications. To maintain a uniform heating profile, the inverter is operated in constant power mode. A hybrid voltage and frequency control is proposed. Voltage control is used for active power tracking while frequency control is used to minimise circulating current due to reactive power and to achieve soft switching for the inverter switches. Analytical and test results are shown to verify the proposed approach. [ABSTRACT FROM AUTHOR]

The ultra‐dense deployment of pico cells in 5G heterogeneous networks (HetNets) has raised serious concerns regarding interference and energy consumption. Both industry and academia are focusing on enhancing network energy efficiency (EE) while maintaining satisfactory quality of service (QoS) levels. However, finding an optimal solution to NEE is very challenging, especially in ultra‐dense HetNets. Here, a user association and power management algorithm is presented that follows a heuristic approach and aims to maximize EE while satisfying other network requirements. The proposed algorithm associates users based on criteria that consider the users' EE and minimizes energy consumption by intermittently switching into sleep mode base stations with the highest impact on overall network EE. The performance of this solution is evaluated in a realistic multi‐cell two‐tier scenario considering both co‐tier and cross‐tier interference by comparing it with two other solutions: a heuristic approach based on standardized eICIC, and an optimization approach based on Lagrangian dual decomposition. The simulation results show that our solution outperforms benchmarking solutions in terms of EE, average user rate, and network throughput while minimizing energy consumption. [ABSTRACT FROM AUTHOR]

IMT-2020 (International Mobile Telecommunications-2020) is the prevailing mobile communication technology at the moment, significantly affecting societal progress. Nevertheless, the roll-out of the IMT-2020 system has introduced numerous interferences to existing services. The coexistence with fixed satellite services has become a topical issue currently under consideration. This paper discusses the compatibility and interference issues between IMT-2020 and the 14 GHz FSS (fixed-satellite service) uplink, as well as the spectrum access issue solved by artificial intelligence methods. The study shows that the interference from IMT-2020 macro-base stations to FSS space stations exceeds the ITU standard by approximately 10 dB. To control the interference, a partition-based power control algorithm is proposed, which divides ground base stations into multiple areas and virtualizes each area's base stations into a single large base station then applies power control to maximize the total transmission power of the base stations within the area. Furthermore, three intra-partition power control algorithms are introduced: average power allocation, power allocation based on channel gain, andna power allocation method based on the maximum intra-partition sum rate. Additionally, under the assumption that dynamic satellite nodes are available in the system for ground user access, a spectrum access algorithm utilizing deep reinforcement learning is designed. Simulation results confirm the effectiveness of the proposed scheme, which can reduce the interference from the IMT-2020 system to the FSS service below the threshold, ensuring harmonious coexistence of the two services. [ABSTRACT FROM AUTHOR]

This paper introduces a novel finite control set predictive direct power control method for grid‐connected converters without cost function evaluations. Unlike conventional predictive direct power control, since the proposed method does not use the model parameters, their uncertainties do not cause prediction error and inappropriate voltage vector selection. The method employs a new form of voltage vector selection based on the slopes of active and reactive powers. The slopes are predicted in a manner with a low sensitivity to sampling noise, without updating a look‐up table, and recursive methods. Hence, there are no stagnation and convergence issues. Also, the proposed method avoids startup problems caused by data‐lacking due to directly regulating the active and reactive power by a switching logic. Flexible power oscillations control with balanced sinusoidal grid currents without any signal sequence extraction can also be achieved under this method in unbalanced grid conditions. The proposed method is assessed by both simulation and experimental studies, and its performance is compared with existing robust combined and model predictive control methods. The outcomes highlight the influence of the proposed approach and establish its superiority over the other considered methods. [ABSTRACT FROM AUTHOR]

To accommodate the high-frequency and low-frequency power fluctuations of renewable energy generation with a lower system cost, a multi-electrolyzer hybrid hydrogen production system demonstrated potential for application. This study achieved the integration of proton exchange membrane electrolyzers and alkaline electrolyzers through hybrid control in a photovoltaic (PV)- transformer-electrolyzer coupled system for the first time, enabling the pre-regulation of the electrolyzer power based on the LSTM forecast of the power generation of PV, to avoid the impact of sudden large power fluctuations on the electrolyzers, with a lower system cost and higher production rate. Experiment results indicate that this hybrid hydrogen production system reduces system initial investment cost by 27.2% compared to a pure PEMWE system and increases hydrogen production by 58.7% compared to a pure AWE system. • A new hybrid hydrogen production system is proposed. • Coupling of power electronic devices and multiple electrolyzers is simulated. • A high-efficiency and low-cost hydrogen production system adaptable to power fluctuations is established. • Power pre-regulation for multiple electrolyzers based on PV power prediction is established. [ABSTRACT FROM AUTHOR]

Summary: 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 (SCN). This paper investigates the handoff optimization in SCN with power‐domain non‐orthogonal multiple access (NOMA) that uses successive interference cancelation (SIC), considering the fairness among base stations (BSs). We study the joint base station association and power control problem by considering the motion of mobile users (MUs) and load balancing in the SCNs. Under the maximum allowable transmit power and minimum average‐rate constraints, two optimization problems are formulated using the number of associated MUs, 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 SCN. [ABSTRACT FROM AUTHOR]

Summary In recent years, technological developments with user demands, reduced production, and launch costs have rapidly increased the number of Low Earth Orbit (LEO) satellites in space. Since LEO satellites use the same frequency band as existing Geostationary Earth Orbit (GEO) satellites, the interference coordination between the two satellite networks is vital. In order to minimize the co‐existent interference between these satellite networks, studies perform on different interference mitigation strategies. In this paper, analysis and comparative assessment of these interference mitigation techniques are presented for the co‐existent Non‐Geostationary Earth (NGEO) and GEO systems. More specifically, power control (PC) and spatial isolation‐based link adaptation (SILA) techniques are studied comparatively for the performance evaluation. It is shown that the communication link bandwidth is more efficiently utilized in the SILA technique when compared with the PC technique. Moreover, the multi‐objective optimization problem (MOP) approach in the SILA technique is demonstrated to be more effective when compared with the single‐objective optimization problem (SOP) approach used in the PC technique as the simultaneous prioritizing objective functions outperforms single prioritization. Finally, it is shown that when the PC technique is applied together with the SILA technique, the exclusive angle (EA) can be reduced up to 8% for 100 Mbps, and 8.5% for 200 Mbps transmission bit rates in different operational scenarios. The presented performance evaluation in this paper may help the satellite operator or decision‐maker gain insights on which mitigation technique can be used in the case of a co‐existent interference. [ABSTRACT FROM AUTHOR]

This study examined the integrated benefits of 5G New Radio, network slicing, and reinforcement learning (RL) mechanisms in addressing the challenges associated with the increasing proliferation of intelligent objects in communication networks. This study proposed an innovative architecture that initially employed network slicing to efficiently segregate and manage various service types. Subsequently, this architecture was enhanced by applying RL to optimize the subchannel and power allocation strategies. This dual approach was proven through simulation studies conducted in a suburban setting, highlighting the effectiveness of the method for optimizing the use of available frequency bands. The results highlighted significant improvements in mitigating interference and adapting to the dynamic conditions of the network, thereby ensuring efficient dynamic resource allocation. Further, the application of an RL algorithm enabled the system to adjust resources adaptively based on real-time network conditions, thereby proving the flexibility and responsiveness of the scheme to changing network scenarios. [ABSTRACT FROM AUTHOR]

Cell-Free Massive Multiple-Input–Multiple-Output (CF-MIMO) systems have transformed the landscape of wireless communication, offering unparalleled enhancements in Spectral Efficiency and interference mitigation. Nevertheless, the large-scale deployment of CF-MIMO presents significant challenges in processing signals in a scalable manner. This study introduces an innovative methodology that leverages the capabilities of Dynamic Mode Decomposition (DMD) to tackle the complexities of Channel Estimation in CF-MIMO wireless systems. By extracting dynamic modes from a vast array of received signal snapshots, DMD reveals the evolving characteristics of the wireless channel across both time and space, thereby promising substantial improvements in the accuracy and adaptability of channel state information (CSI). The efficacy of the proposed methodology is demonstrated through comprehensive simulations, which emphasize its superior performance in highly mobile environments. For performance evaluation, the most common techniques have been employed, comparing the proposed algorithms with traditional methods such as MMSE (Minimum Mean Squared Error), MRC (Maximum Ration Combining), and ZF (Zero Forcing). The evaluation metrics used are standard in the field, namely the Cumulative Distribution Function (CDF) and the average UL/DL Spectral Efficiency. Furthermore, the study investigates the impact of DMD-enabled Channel Estimation on system performance, including beamforming strategies, spatial multiplexing within realistic time- and delay-correlated channels, and overall system capacity. This work underscores the transformative potential of incorporating DMD into massive MIMO wireless systems, advancing communication reliability and capacity in increasingly dynamic and dense wireless environments. [ABSTRACT FROM AUTHOR]

Abstract User pairing plays an important role in device‐to‐device (D2D) relay communication, contributing significantly to maintaining low energy consumption, high throughput, and overall energy efficiency in the communication system. To achieve these purposes, an attention‐based long short‐term memory motion prediction model (AT‐LSTM) and propose a joint power control algorithm. Leveraging these techniques, we also propose a D2D user pairing algorithm, distance–power–SINR pairing algorithm (DPSPA), which comprehensively considers factors such as D2D communication distances, transmit power, and signal‐to‐interference‐plus‐noise ratio. Initially, the AT‐LSTM model is utilized to predict the location of users. Subsequently, the distance between the user terminal device and each communication point and the base station, filtering cache points, and non‐cache points within the D2D communication radius are calculated. Then, based on the distance, required transmission power, and signal‐to‐interference‐plus‐noise ratio of each point, the evaluation index (the best matching product) is obtained. Finally, the point with the maximum best matching product is selected for D2D direct communication mode, D2D relay communication mode, or cellular communication mode. Simulation results demonstrate that DPSPA effectively reduces system energy consumption, enhances system throughput, and improves overall energy efficiency.

Abstract Aiming to improve the voltage distribution and realize the proportional sharing of active and reactive power in the distribution network (DN), this article proposes a distributed optimal control strategy based on the grouping cooperation mechanism of the distributed generation (DG). The proposed strategy integrates the local information of the DG and the global information of the DN. Considering the high resistance/reactance ratio of DN, distributed optimization control strategies for node voltage control and active power management are developed with the consensus variable of active utilization rate. And distributed strategy for reactive power management is proposed with a consensus variable of reactive utilization rate. The convergence of the distributed control system for each group is proved. The validity and robustness of the proposed strategy are verified by several simulations in the IEEE 33‐bus system.

Abstract With the continuous expansion of the photovoltaic industry, the application of solar photovoltaic power generation systems is becoming increasingly widespread. Due to the obvious intermittency and volatility of photovoltaic power generation, integration of large‐scale photovoltaic power generation into the power grid can cause certain impacts on the security and stability of the grid. Photovoltaic power prediction is essential to solve this problem, as it can improve the quality of photovoltaic grid connection, optimize grid scheduling, and ensure the safe operation of the grid. In this article, the deep learning method is selected for photovoltaic power prediction. Based on the analysis of the OctConv (Octave Convolution) network structure, the AOctConv (Attention Octave Convolution) convolutional neural network structure is proposed, which is combined with the ResNet50 backbone network to obtain AOC‐ResNet50. It is then applied to the prediction of the generation of photovoltaic power. The prediction performance is compared with the ResNet50 network and the Oct‐ResNet50 network, and it is found that the AOC‐ResNet50 network has the best prediction performance, with an MAE of only 0.176888. Based on the exemplar work, a framework is proposed to illustrate this method. Its general application is discussed.

Abstract The present study introduces a nature inspired improved liver cancer algorithm (ILCA) for solving the non‐convex engineering optimization issues. The traditional LCA (t‐LCA) inspires from the conduct of liver tumours and integrates biological ethics during the optimization procedure. However, t‐LCA facing stagnation issues and may trap into local optima. To avoid such issues and provide the optimal solution, there are some modifications are implemented into the internal structure of t‐LCA based on Weibull flight operator, mutation‐based approach, quasi‐opposite‐based learning and gorilla troops exploitation‐based mechanisms to enhance the overall strength of the algorithm to obtain the global solution. For validation of ILCA, the non‐parametric and the statistical analysis are performed using benchmark standard functions. Moreover, ILCA is applied to resolve the stochastic renewable‐based (wind turbines + PVs) optimal power flow problem using a modified RER‐based IEEE 57‐bus. The objective of this work is to obtain the minimum predicted power losses and enhance the predicted voltage stability. By incorporation of renewable resources into the modified IEEE57‐bus network can help the system to reduce the power losses from 5.6622 to 3.8142 MW, while the voltage stability is enhanced from 0.1700 to 0.1164 p.u.

Abstract The ultra‐dense deployment of pico cells in 5G heterogeneous networks (HetNets) has raised serious concerns regarding interference and energy consumption. Both industry and academia are focusing on enhancing network energy efficiency (EE) while maintaining satisfactory quality of service (QoS) levels. However, finding an optimal solution to NEE is very challenging, especially in ultra‐dense HetNets. Here, a user association and power management algorithm is presented that follows a heuristic approach and aims to maximize EE while satisfying other network requirements. The proposed algorithm associates users based on criteria that consider the users’ EE and minimizes energy consumption by intermittently switching into sleep mode base stations with the highest impact on overall network EE. The performance of this solution is evaluated in a realistic multi‐cell two‐tier scenario considering both co‐tier and cross‐tier interference by comparing it with two other solutions: a heuristic approach based on standardized eICIC, and an optimization approach based on Lagrangian dual decomposition. The simulation results show that our solution outperforms benchmarking solutions in terms of EE, average user rate, and network throughput while minimizing energy consumption.

Abstract This paper introduces a novel finite control set predictive direct power control method for grid‐connected converters without cost function evaluations. Unlike conventional predictive direct power control, since the proposed method does not use the model parameters, their uncertainties do not cause prediction error and inappropriate voltage vector selection. The method employs a new form of voltage vector selection based on the slopes of active and reactive powers. The slopes are predicted in a manner with a low sensitivity to sampling noise, without updating a look‐up table, and recursive methods. Hence, there are no stagnation and convergence issues. Also, the proposed method avoids startup problems caused by data‐lacking due to directly regulating the active and reactive power by a switching logic. Flexible power oscillations control with balanced sinusoidal grid currents without any signal sequence extraction can also be achieved under this method in unbalanced grid conditions. The proposed method is assessed by both simulation and experimental studies, and its performance is compared with existing robust combined and model predictive control methods. The outcomes highlight the influence of the proposed approach and establish its superiority over the other considered methods.

Cell-Free Massive Multiple-Input–Multiple-Output (CF-MIMO) systems have transformed the landscape of wireless communication, offering unparalleled enhancements in Spectral Efficiency and interference mitigation. Nevertheless, the large-scale deployment of CF-MIMO presents significant challenges in processing signals in a scalable manner. This study introduces an innovative methodology that leverages the capabilities of Dynamic Mode Decomposition (DMD) to tackle the complexities of Channel Estimation in CF-MIMO wireless systems. By extracting dynamic modes from a vast array of received signal snapshots, DMD reveals the evolving characteristics of the wireless channel across both time and space, thereby promising substantial improvements in the accuracy and adaptability of channel state information (CSI). The efficacy of the proposed methodology is demonstrated through comprehensive simulations, which emphasize its superior performance in highly mobile environments. For performance evaluation, the most common techniques have been employed, comparing the proposed algorithms with traditional methods such as MMSE (Minimum Mean Squared Error), MRC (Maximum Ration Combining), and ZF (Zero Forcing). The evaluation metrics used are standard in the field, namely the Cumulative Distribution Function (CDF) and the average UL/DL Spectral Efficiency. Furthermore, the study investigates the impact of DMD-enabled Channel Estimation on system performance, including beamforming strategies, spatial multiplexing within realistic time- and delay-correlated channels, and overall system capacity. This work underscores the transformative potential of incorporating DMD into massive MIMO wireless systems, advancing communication reliability and capacity in increasingly dynamic and dense wireless environments.

Abstract Grid‐forming inverters are used for voltage regulation and frequency control in autonomous hybrid microgrids and multi‐microgrid systems by imitating synchronous generators. However, in microgrids with weak grids including low inertia levels and small X/R ratios, these inverters interact with each other, and as a result low‐frequency oscillations (LFO) arise. LFO impacts the frequency stability of multi‐microgrid systems. Nevertheless, LFO can be mitigated by the load‐frequency control system, which serves as a secondary control mechanism. However, the presence of wind turbines and photovoltaic systems in hybrid microgrids adds complexity to the operation of the load‐frequency control due to the uncertainty associated with these renewable energy resources, and various controllers have been employed. This paper proposes a novel approach to enhance the performance of the load‐frequency control system and suppress LFO. The presented technique reduces the complexity of the hybrid microgrid structure by reducing the number of controllers. The model predictive control (MPC) technique is utilized for load‐frequency control and the weight parameters of the MPC are determined using the rain optimization algorithm. The proposed method demonstrates improved dynamic response, reduced overshoot and undershoot responses, decreased controller complexity, and effective LFO suppression. The simulation results verify the effectiveness of the method.

In the conventional LTE homogeneous network, sufficient transmit power of user equipment (UE) is determined by open-loop power control (OL-PC) and closed-loop power control (CL-PC) schemes. However, in a Het-Net environment, setting the UE’s transmit power requires delicate responsiveness to handle the severe and complicated uplink interference. In this paper, an interference-aware uplink power control mechanism based on Heuristic game theory approach is proposed for devices coexisting in a heterogeneous wireless network. Various wireless constraints like channel response, path loss, fading, shadowing, interference and metrics like SNR, SINR, throughput and bit rates are considered. Uplink power is controlled by suitably selecting the penalization factor (β) based on a simple Heuristic game theory approach considering the possible wireless constraints of each user depending on its location in the cell under consideration. The algorithm is framed in such a way to reduce inter-cell interference, limit transmit power, enhance bit rates and throughput of users. A significant improvement of 5.2% in the user coverage/distribution is achieved as a result of interference management compared to conventional power control scheme and power control with convex pricing.

Abstract Integration of unmanned aerial vehicles (UAVs) with ultra-reliable and low-latency communication (URLLC) systems can improve the real-time communication performance for various industrial internet of things (IIoT) applications. Designing an intelligent resource allocation system is one of the challenges posed by an energy-constrained UAV communication system. Therefore, we formulate a sum rate maximization problem, subject to the UAVs’ energy by optimizing the blocklength allocation and the power control jointly in the uplink UAV-assisted URLLC systems, in which the probabilistic channel model between UAV and users is adopted. The problem is difficult to solve due to the non-convex objective function and the energy constraints, and also challenging to make fast decision in the complex communication environment. Thus, we propose a deep reinforcement learning (DRL)-based scheme to optimize the blocklength allocation and power control jointly. First, transform the original problem into the multi-agent reinforcement learning process, where each subcarrier is regarded as the agent that optimizes its individual blocklength allocation and power control. Then, each agent makes the intelligent decision to obtain the maximum reward value depending on the weighted segmented reward function, which is related to the UAV energy consumption and user rates to improve the rate performance. Finally, the simulation results show that the proposed scheme outperforms the benchmark schemes and has the stable convergence in different settings, such as the learning rate, the error probability, the subcarrier spacing, and the number of users.

Since non-orthogonal multiple access (NOMA) has become a promising technology to enhance network energy efficiency (EE) thanks to its low latency,high reliability and massive connectivity, the optimization of EE in NOMA-based uplink heterogeneous networks (HetNets) is considered in this paper. The optimization problem for EE maximization is formulated as a combination of user association (base station selection, sub-channel allocation) and power control, which is hardly tractable. To address it, a user association algorithm satisfied the many-to-one relationship between mobile users, base stations and sub-channels is firstly proposed based on matching theory. Then, an iterative power control algorithm evolved from fractional programming and difference of convex programming is presented by proving the concavity of the sum-rate of sub-channels. Finally, simulation results are shown that NOMA-based HetNets equipped with our designed algorithms yield much higher EE than that with the compared schemes. [ABSTRACT FROM AUTHOR]

The primary problem for a resource-limited Wireless Sensor Network is how to extend the system reliability without sacrificing system performance like reception rate and network connectivity. This method effectively deploys sensor nodes and ensures connectivity between the nodes and the transceiver station. Reinforcement Learning (RL) can effectively schedule the sensor nodes' unsupervised activities. A Nash Q-Learning inspired node task scheduling (QL-TS) for service and connection management has been described in this work. An energy-saving MAC Protocol (ESMACP) has been developed to extend the system lifetime. The primary aim of this model is to use the suggested QL-TS-ESMACP that allows sensor devices to learn their best action with minimal energy. The correctness and dependability of the QL-TS-ESMACP can be demonstrated by comparing it with other existing methods. QL-TS-ESMACP outperforms other models in terms of energy efficiency, coverage, node lifespan, and packet delivery ratio in the simulation. [ABSTRACT FROM AUTHOR]

In the conventional LTE homogeneous network, sufficient transmit power of user equipment (UE) is determined by open-loop power control (OL-PC) and closed-loop power control (CL-PC) schemes. However, in a Het-Net environment, setting the UE’s transmit power requires delicate responsiveness to handle the severe and complicated uplink interference. In this paper, an interference-aware uplink power control mechanism based on Heuristic game theory approach is proposed for devices coexisting in a heterogeneous wireless network. Various wireless constraints like channel response, path loss, fading, shadowing, interference and metrics like SNR, SINR, throughput and bit rates are considered. Uplink power is controlled by suitably selecting the penalization factor (β) based on a simple Heuristic game theory approach considering the possible wireless constraints of each user depending on its location in the cell under consideration. The algorithm is framed in such a way to reduce inter-cell interference, limit transmit power, enhance bit rates and throughput of users. A significant improvement of 5.2% in the user coverage/ distribution is achieved as a result of interference management compared to conventional power control scheme and power control with convex pricing. [ABSTRACT FROM AUTHOR]

Grid‐forming inverters are used for voltage regulation and frequency control in autonomous hybrid microgrids and multi‐microgrid systems by imitating synchronous generators. However, in microgrids with weak grids including low inertia levels and small X/R ratios, these inverters interact with each other, and as a result low‐frequency oscillations (LFO) arise. LFO impacts the frequency stability of multi‐microgrid systems. Nevertheless, LFO can be mitigated by the load‐frequency control system, which serves as a secondary control mechanism. However, the presence of wind turbines and photovoltaic systems in hybrid microgrids adds complexity to the operation of the load‐frequency control due to the uncertainty associated with these renewable energy resources, and various controllers have been employed. This paper proposes a novel approach to enhance the performance of the load‐frequency control system and suppress LFO. The presented technique reduces the complexity of the hybrid microgrid structure by reducing the number of controllers. The model predictive control (MPC) technique is utilized for load‐frequency control and the weight parameters of the MPC are determined using the rain optimization algorithm. The proposed method demonstrates improved dynamic response, reduced overshoot and undershoot responses, decreased controller complexity, and effective LFO suppression. The simulation results verify the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Improving node energy consumption efficiency to increase network survival time is one of the main research priorities in wireless sensor networks (WSNs). This work addresses issues with the current ant colony routing algorithm, including uneven node energy, higher communication cost, and routing loop creation. An improved EEABR algorithm (N-EABR, New Energy-efficient ant based routing) is proposed, based on the existing Energy-efficient ant based routing (EEABR) algorithm. This algorithm adds the combination of "pkt_src" (ant packet source address) and "sq_num" (ant packet sequence number) in the node neighbours list and redefines the pheromone updating equation. By sending brief ant packets, this approach efficiently reduces the loop effect and ensures that node energy is distributed evenly throughout the network.The power control based ant colony routing (PCABR)algorithm, which extracts the RSSI value of "Hello" packets to derive the optimal sending power, to conserve the sending energy, and to avoid energy wastage, is further introduced in this paper in order to reduce the energy consumption of nodes when sending packets. The findings of the simulation demonstrate that the N-EEABR and PCABR algorithms both significantly outperform the other methods in terms of path optimisation accuracy and node energy use efficiency and network energy balance. This study is important because it will increase node energy consumption efficiency and network survival time. It also offers helpful suggestions for optimizing routing algorithms for wireless sensor networks. [ABSTRACT FROM AUTHOR]

Summary: Devices in close proximity can directly communicate with each other through device‐to‐device (D2D) communication, bypassing the need for base stations. To minimize the delay for D2D active clients, we use a sequential approach to reuse cellular‐type resources. Unlike conventional methods that focus solely on uplink or downlink resource distribution, our study introduces a novel optimization technique to maximize network throughput. Our proposal incorporates a hybrid approach that combines both downlink and uplink techniques for resource allocation. We utilize random forest and game theory algorithms to ensure smooth D2D communication while reducing interference between cellular and D2D pairings. Our hybrid structure effectively addresses challenges arising from intra‐ and inter‐cell interferences resulting from spectrum reusability and deployment. This approach also improves power control and quality of service. Traditionally, NP‐hard optimization solutions are proposed for mixed‐integer nonlinear problems. In our study, the critical stages include channel assignment and energy allocation. The objective problem in resource allocation considers parameters such as the transmission power of D2D active clients, cellular users, connection distance, base stations, and quality of service constraints. Our proposed hybrid method aims to enhance overall spectrum efficiency and network throughput. Simulated results demonstrate the superiority of our modified hybrid technique compared to existing joint resource allocation methods. This comprehensive approach represents a significant advancement in addressing the complexities associated with D2D communication, offering improved efficiency and performance in contemporary network environments. [ABSTRACT FROM AUTHOR]

To send and receive data simultaneously, reduce network interference, and effectively improve energy efficiency, this paper analyzes the network characteristics of Multi-Radio Multi-Channel Wireless Sensor Network and optimizes the power and channel of each radio frequency interface. Based on the novel energy efficiency model and energy consumption efficiency model, this paper establishes a distributed optimization game model using Game Theory to maximize network energy efficiency, minimize energy consumption, and ensure network connectivity. Based on the game model, an efficient distributed resource allocation game algorithm is designed, in which a joint power control and channel allocation can quickly converge to Nash Equilibrium with low complexity. At the same time, the simulation results show that the distributed resource allocation game algorithm can effectively improve the energy efficiency, reduce network interference and bit error rate to ensure the successful data transmission. [ABSTRACT FROM AUTHOR]

The increasingly extensive use of non-linear loads, mostly including static power converters, in large industry, commercial, and domestic applications, as well as the spread of renewable energy sources in distribution-generated units, make the use of the most efficient power quality improvement systems a current concern. The use of active power filters proved to be the most advanced solution with the best compensation performance for harmonics, reactive power, and load unbalance. Thus, issues related to improving the power quality through active power filters are very topical and addressed by many researchers. This paper presents a topical review on the shunt active power filters in three-phase, three-wire systems. The power circuit and configurations of shunt active filtering systems are considered, including the multilevel topologies and use of advanced power semiconductor devices with lower switching losses and higher switching frequencies. Several compensation strategies, reference current generation methods, current control techniques, and DC-voltage control are pointed out and discussed. The direct power control method is also discussed. New advanced control methods that have better performance than conventional ones and gained attention in the recent literature are highlighted. The current state of renewable energy sources integration with shunt active power filters is analyzed. Concerns regarding the optimum placement and sizing of the active power filters in a given power network to reduce the investment costs are also presented. Trends and future developments are discussed at the end of this paper. For a rigorous substantiation, more than 250 publications on this topic, most of them very recent, constitute the basis of bibliographic references and can assist readers who are interested to explore the subject in greater detail. [ABSTRACT FROM AUTHOR]

Physical-layer security (PLS) has attracted much attention in wireless communications and has been considered one of the main candidates for enhancing wireless security in future 6G networks. Recent studies in the PLS area have focused on investigating and analyzing the characteristics of secure transmissions in multiuser networks (e.g., the massive number of Internet of Things (IoT) devices in 6G networks). Due to the difficulty of obtaining the exact secrecy capacity region in wireless multiuser networks, several alternative methods are used to characterize the secrecy performance of multiuser networks. For example, we can analyze the secrecy sum-rate scaling in terms of the number of users based on multiuser diversity (MUD). In this paper, we propose an opportunistic user scheduling scheme that achieves optimal MUD gain, combined with a power control mechanism for reducing information leakage to multiple eavesdroppers in wireless networks. The proposed scheme considers multiuser transmissions in one scheduling time slot by adopting orthogonal random beamforming at the receiver to exploit the full degrees-of-freedom gain with an assumption that each user (or IoT device) is equipped with a single antenna, and base station and eavesdroppers have multiple antennas. The main contribution of this paper is to derive the analytic result of the achievable secrecy sum-rate scaling in a high signal-to-noise ratio (SNR) regime. We evaluate the performance of the proposed scheduling scheme with a power control mechanism through simulations with both internal and external eavesdropping scenarios. We further discuss the extensibility of our analysis to various applications such as satellite communications and IoT networks. [ABSTRACT FROM AUTHOR]

Carrier phase‐shifted sine pulse width modulation is a common modulation strategy for medium‐ and low‐voltage cascaded H‐bridges (CHB). This paper proposes a novel jittered‐carrier phase‐shifted sine pulse width modulation (JCPS‐SPWM) to reduce the total harmonic distortion (THD) of the converter. It makes the carrier jitter regularly while the total switching times remain unchanged, which reduces the THD of the bridge arm voltage and current by moving the low‐order output harmonics of the bridge arm voltage and current to filterable high‐order harmonics. Since the total number of switching times remains unchanged, this modulation strategy will not cause any increase in switching loss. A seven‐level CHB simulation model and an experimental prototype are built to verify the effectiveness of the approach. The results show that harmonic content can be reduced by 47.5% compared with the traditional method, thus verifying the effectiveness of the JCPS‐SPWM. [ABSTRACT FROM AUTHOR]

In this paper we consider a scenario where there are two wireless body area networks (WBANs) interfere with each other from a game theoretic perspective. In particular, we envision two WBANs playing a potential game to enhance their performance by decreasing interference to each other. Decreasing interference extends the sensors' batteries life time and reduces the number of re‐transmissions. We derive the required conditions for the game to be a potential game and its associated the Nash equilibrium (NE). Specifically, we formulate a game where each WBAN has three strategies. Depending on the payoff of each strategy, the game can be designed to achieve a desired NE. Furthermore, we employ a learning algorithm to achieve that NE. In particular, we employ the Fictitious play (FP) learning algorithm as a distributed algorithm that WBANs can use to approach the NE. The simulation results show that the NE is mainly a function of the power cost parameter and a reliability factor that we set depending on each WBAN setting (patient). However, the power cost factor is more dominant than the reliability factor according to the linear cost function formulation that we use throughout this work. [ABSTRACT FROM AUTHOR]

Recently, the ever-increasing demands including higher rate and connection stability have bottlenecked the user experience of the traditional cellular networks. To this end, the Cell-Free (CF) network, which is characterized as a user-centric architecture, is viewed as a promising paradigm to enhance the user experience. Aimed at this point, this paper investigates the joint optimization problem including user matching, sub-channel allocation and power controlling for the mmWave CF network to maximize the revenue of the operators. Firstly, the revenue maximization oriented joint optimization problem of the mmWave CF network is formulated as mixed integer non-convex and non-linear programming, which is NP-hard problem and is intractable to search an optimal solution in within polynomial time. Secondly, the original problem is decomposed into three sub-problems, i.e., user association sub-problem, the sub-channel allocation sub-problem and the power controlling sub-problem under the consideration of the matching quotas, rate demand and transmission power, etc. Thirdly, a many-to-many matching based user association algorithm and an alternating iterative joint resource management algorithm, which is composed of the harmony search based sub-channel allocation sub-algorithm and the interior-point method based power controlling sub-algorithm, are proposed to obtain a sub-optimal solution, and the computational complexity of the proposed algorithms are also analyzed. Finally, the performance superiorities of the proposed algorithms are demonstrated through extensive simulations, and it is demonstrated that the proposed algorithms can outperform the proposed algorithm outperforms the PUAA algorithm by 5.73% and the SRAA algorithm by 11.25% in terms of operator revenue. [ABSTRACT FROM AUTHOR]

The rapid development of wireless-powered Internet of Things (IoT) networks, supported by multiple unmanned aerial vehicles (UAVs) and full-duplex technologies, has opened new avenues for simultaneous data transmission and energy harvesting. In this context, optimizing energy efficiency (EE) is crucial for ensuring sustainable and efficient network operation. This paper proposes a novel approach to EE optimization in multi-UAV-aided wireless-powered IoT networks, focusing on balancing the uplink data transmission rates and total system energy consumption within an orthogonal frequency-division multiple access (OFDMA) framework. This involves formulating the EE optimization problem as a Multi-Objective Optimization Problem (MOOP), consisting of the maximization of the uplink total rate and the minimization of the total system energy consumption, which is then transformed into a Single-Objective Optimization Problem (SOOP) using the Tchebycheff method. To address the non-convex nature of the resulting SOOP, characterized by combinatorial variables and coupled constraints, we developed an iterative algorithm that combines Block Coordinate Descent (BCD) with Successive Convex Approximation (SCA). This algorithm decouples the subcarrier assignment and power control subproblems, incorporates a penalty term to relax integer constraints, and alternates between solving each subproblem until convergence is reached. Simulation results demonstrate that our proposed method outperforms baseline approaches in key performance metrics, highlighting the practical applicability and robustness of our framework for enhancing the efficiency and sustainability of real-world UAV-assisted wireless networks. Our findings provide insights for future research on extending the proposed framework to scenarios involving dynamic UAV mobility, multi-hop communication, and enhanced energy management, thereby supporting the development of next-generation sustainable communication systems.

To provide inertia support to the power grid, the grid side frontend DC/AC inverter of the bidirectional electric vehicle (EV) charging facilities can use the virtual synchronous generator (VSG) control in its grid-side DC/AC converter. However, the existing VSG control cannot actually realize the inertia support due to the limitations of the existing cascaded power converters control structure of the bidirectional EV charger. This paper proposes a new control infrastructure for VSG based bi-directional EV charging facilities. The proposed control instantaneously tracks the inertial supporting surge power by DC/DC so as to stabilize the DC bus voltage. Meanwhile, the droop coefficient based power values are also fed forward to DC/DC controls to support grid frequency control over a larger time scale. Finally, the experiment results show that the proposed method improves the DC bus voltage stability of the EV charger while allowing the EV to participate in the power system frequency regulation, which is a key feature of the Vehicle to Grid applications.