Showing papers on "Network topology published in 2022"

A Comprehensive Review of Power Converter Topologies and Control Methods for Electric Vehicle Fast Charging Applications

Abstract: Wide-scale adoption and projected growth of electric vehicles (EVs) necessitate research and development of power electronic converters to achieve high power, low-cost, and reliable charging solutions for the EV battery. This paper presents a comprehensive review of EV off-board chargers that consist of ac-dc and dc-dc power stages from the power network to the EV battery. Although EV chargers are categorized into two types, namely, on-board and off-board chargers, it is essential to utilize off-board chargers for dc fast and ultra-fast charging so that volume and weight of EV can be reduced significantly. Here, we discuss the state-of-the-art topologies and control methods of both ac-dc and dc-dc power stages for off-board chargers, focusing on technical details, ongoing progress, and challenges. In addition, most of the recent multiport EV chargers integrating PV, energy storage, EV, and grid are presented. Moreover, comparative analysis has been carried out for the topologies and the control schemes of ac-dc rectifiers, dc-dc converters, and multiport converters in terms of architecture, power and voltage levels, efficiency, bidirectionality, control variables, advantages, and disadvantages which can be used as a guideline for future research directions in EV charging solutions.

A comprehensive review on advanced charging topologies and methodologies for electric vehicle battery

Abstract: The rise of greenhouse gas levels in the atmosphere is a severe climate change concern. A significant part, such as CO2 emission, comes from internal combustion engine-driven vehicles, incited the automotive sector to focus more on the sustainable electric transportation system. However, electric vehicles face significant charging time, charging methods, and range anxiety challenges. To overcome these challenges, charging technologies for electric vehicle batteries play an essential role. Many types of electric vehicle charging topologies have been discussed in the literature and implemented in many practical applications. This paper presents a state of art criticism of advanced converter topologies and charging methodology for electric vehicle applications. Apart from the conventional topologies, this manuscript has covered the comparative criticism of the recently proposed EV charging technologies regarding charging methods, control strategies, and power levels. Further, this paper discussed the different onboard chargers with their power factor correction topologies, drawbacks, and required corrections. This manuscript provides the research directions for the academic and industrial communities.

Jupiter evolving: transforming google's datacenter network via optical circuit switches and software-defined networking

Abstract: We present a decade of evolution and production experience with Jupiter datacenter network fabrics. In this period Jupiter has delivered 5x higher speed and capacity, 30% reduction in capex, 41% reduction in power, incremental deployment and technology refresh all while serving live production traffic. A key enabler for these improvements is evolving Jupiter from a Clos to a direct-connect topology among the machine aggregation blocks. Critical architectural changes for this include: A datacenter interconnection layer employing Micro-Electro-Mechanical Systems (MEMS) based Optical Circuit Switches (OCSes) to enable dynamic topology reconfiguration, centralized Software-Defined Networking (SDN) control for traffic engineering, and automated network operations for incremental capacity delivery and topology engineering. We show that the combination of traffic and topology engineering on direct-connect fabrics achieves similar throughput as Clos fabrics for our production traffic patterns. We also optimize for path lengths: 60% of the traffic takes direct path from source to destination aggregation blocks, while the remaining transits one additional block, achieving an average block-level path length of 1.4 in our fleet today. OCS also achieves 3x faster fabric reconfiguration compared to pre-evolution Clos fabrics that used a patch panel based interconnect.

Switched-Capacitor Multilevel Inverters: A Comprehensive Review

Abstract: Multilevel inverters (MLIs) with switched-capacitor (SC) units have been a widely rehearsed research topic in power electronics since the last decade. Inductorless/transformerless operation with voltage-boosting feature and inherent capacitor self-voltage balancing performance with a reduced electromagnetic interference make the SC-MLI an attractive converter over the other available counterparts for various applications. There have been many developed SC-MLI structures recently put forward, where different basic switching techniques are used to generate multiple (discrete) output voltage levels. In general, the priority of the topological development is motivated by the number of output voltage levels, overall voltage gain, and full dc-link voltage utilization, while reducing the component counts and stress on devices for better efficiency and power density. To facilitate the direction of future research in SC-MLIs, this article presents a comprehensive review, critical analysis, and categorization of the existing topologies. Common fundamental units are generalized and summarized with their merits and demerits. Ultimately, major challenges and research directions are outlined leading to the future technology roadmap for more practical applications.

An Overview of Soft Open Points in Electricity Distribution Networks

Abstract: Soft open points (SOPs) are power electronic devices that are usually placed at normally open points of electricity distribution networks to provide flexible power control to the networks. This paper gives a comprehensive overview of both academic research and industrial practice on SOPs in electricity distribution networks. The topologies of SOPs as multi-functional power electronic devices are identified and compared, which include back-to-back voltage source converters, multi-terminal voltage source converters, unified power flow controllers, and direct AC-to-AC modular multilevel converters. The academic research is reviewed in three aspects, i.e., benefit quantification, control, and optimal siting and sizing of SOPs. The benefit quantification indices are categorized into feeder load balancing, voltage profile improvement, power losses reduction, three-phase balancing and DG hosting capacity enhancement. The control of SOPs is summarized as a three-level control structure, where the system-level and converter-level control are further discussed. For optimal siting and sizing of SOPs, problem formulation and solution methods are analyzed. Besides the academic research, practical industrial projects of SOPs worldwide are also summarized. Finally, opportunities of research and industrial application of SOPs are discussed.

A <i>Q</i>-Learning-Based Topology-Aware Routing Protocol for Flying <i>Ad Hoc</i> Networks

Abstract: Flying ad hoc networks (FANETs) have emanated over the last few years for numerous civil and military applications. Owing to underlying attributes, such as a dynamic topology, node mobility in 3-D space, and the limited energy of unmanned aerial vehicles (UAVs), a routing protocol for FANETs is challenging to design. Exiting topology-based routing is unsuitable for highly dynamic FANETs. Location-based routing protocols can be preferred for FANETs owing to their scalability, but are based on one-hop neighbor information and do not contemplate the reachability of further appropriate nodes for forwarding. Owing to the rapid mobility of UAVs, the topology frequently changes; thus, some route entries in the routing table can become invalid and the next-hop nodes may be unavailable before a timeout. That is, the routing decision based on one-hop neighbors cannot assure a successful delivery. In this study, we propose a novel $Q$ -learning-based topology-aware routing (QTAR) protocol for FANETs to provide reliable combinations between the source and destination. The proposed QTAR improves the routing decision by considering two-hop neighbor nodes, extending the local view of the network topology. With the ${Q}$ -learning technique, QTAR adaptively adjusts the routing decision according to the network condition. Our simulation results reveal that QTAR outstrips the existing routing protocols in respect of various performance metrics under distinct scenarios.

Robust Control Strategies for Microgrids: A Review

Abstract: Microgrids consisting of photovoltaic (PV) power plants and wind farms have been widely accepted in power systems for reliability enhancement and power loss reduction. Microgrids are capable of providing voltage and frequency support, improving power quality, and achieving proper power-sharing. To achieve such goals and deal with the nonlinear behavior in such systems, appropriate robust control strategies are required to be adopted. This article presents a comprehensive review of robust control methods for microgrids, including AC, DC, and hybrid microgrids, with different topologies and different types of interconnection to conventional power systems based on recently published research studies. The main control objectives, along with proposed control methods, are comparatively discussed for different types of microgrids. Furthermore, several research gaps in this area related to the scalability, robustness assessment, and evaluation approach are discussed. Recommendations are made that can potentially open new research lines to enhance the effectiveness of robust controllers for AC, DC, and hybrid microgrids.

Computation and visualization of cell–cell signaling topologies in single-cell systems data using Connectome

Abstract: Single-cell RNA-sequencing data has revolutionized our ability to understand of the patterns of cell-cell and ligand-receptor connectivity that influence the function of tissues and organs. However, the quantification and visualization of these patterns in a way that informs tissue biology are major computational and epistemological challenges. Here, we present Connectome, a software package for R which facilitates rapid calculation and interactive exploration of cell-cell signaling network topologies contained in single-cell RNA-sequencing data. Connectome can be used with any reference set of known ligand-receptor mechanisms. It has built-in functionality to facilitate differential and comparative connectomics, in which signaling networks are compared between tissue systems. Connectome focuses on computational and graphical tools designed to analyze and explore cell-cell connectivity patterns across disparate single-cell datasets and reveal biologic insight. We present approaches to quantify focused network topologies and discuss some of the biologic theory leading to their design.

Non-isolated high step-up DC/DC converters – An overview

Abstract: High step-up, high efficiency, low cost DC/DC converters have operated as an interface to make use of the renewable energy system generated power. In order to obtain desired output voltage, the DC/AC voltage conversion to AC mains voltage is an important consideration mainly achieved through inverters. Taking into acoount the performance of the non-isolated high step-up DC/DC converters for the renewable energy systems, the substantial amount of topologies studied in past years are the non-isolated high step-up DC/DC converters. Based on proposed and generalized configurations, the non-isolated high step-up DC/DC converters are classified into several categories and reviewed in this paper. So, to clarify the distinguishing solutions, the key features; topological variations, merits and demerits of these converters are discussed and compared. This review work aims to give a well-informed and a well-detailed general framework about these converters and facilitates to derive the new well topologies in the future.

Misalignment-Tolerant Dual-Transmitter Electric Vehicle Wireless Charging System With Reconfigurable Topologies

Abstract: Wirelesscharging for electric vehicles (EVs) enjoys many benefits, such as convenience, safety, and automation. One of the major issues concerning EV wireless charging is misalignment tolerance along the door-to-door direction of the EV. This letter proposes a misalignment-tolerant dual-transmitter EV wireless charging system with a reconfigurable topology. At central positions, the system can be reconfigured to the S-S (series-series) topology where the two transmitting coils are connected in series to feed the load. At boundary positions, the two transmitting coils form the LCCC-S (inductor-capacitor-capacitor-capacitor-series) topology to enhance power transfer capability and tolerate weak couplings. In this way, not only the output power can be smoothed with door-to-door misalignment, but also wireless charging is guaranteed at weak couplings. Experimental results reveal that within the cover area of the transmitting coils, high-efficiency stable output can be achieved.

An improved influence maximization method for social networks based on genetic algorithm

Abstract: Over the recent decade, much research has been conducted in the field of social networks. The structure of these networks has been irregular, complex, and dynamic, and certain challenges such as network topology, scalability, and high computational complexities are typically evident. Because of the changes in the structure of social networks over time and the widespread diffusion of ideas, seed sets also need to change over time. Since there have been limited studies on highly dynamical changes in real networks, this research intended to address the network dynamicity in the classical influence maximization problem, which discovers a small subset of nodes in a social network and maximizes the influence spread. To this end, we used soft computing methods (i.e., a dynamic generalized genetic algorithm) in social networks under independent cascade models to obtain a dynamic seed set. We modeled several graphs in a specified timestamp through which the edges and the nodes changed within different time intervals. Attempts were made to find influential individuals in each of these graphs and maximize individuals’ influences in social networks, which could thereby lead to changes in the members of the seed set. The proposed method was evaluated using standard datasets. The results showed that due to the reduction of the search areas and competition, the proposed method has higher scalability and accuracy to identify influential nodes in these snapshot graphs as compared with other comparable algorithms.

A Graph Neural Network-Based Digital Twin for Network Slicing Management

Abstract: Network slicing has emerged as a promising networking paradigm to provide resources tailored for Industry 4.0 and diverse services in 5G networks. However, the increased network complexity poses a huge challenge in network management due to virtualized infrastructure and stringent quality-of-service requirements. Digital twin (DT) technology paves a way for achieving cost-efficient and performance-optimal management, through creating a virtual representation of slicing-enabled networks digitally to simulate its behaviors and predict the time-varying performance. In this article, a scalable DT of network slicing is developed, aiming to capture the intertwined relationships among slices and monitor the end-to-end (E2E) metrics of slices under diverse network environments. The proposed DT exploits the novel graph neural network model that can learn insights directly from slicing-enabled networks represented by non-Euclidean graph structures. Experimental results show that the DT can accurately mirror the network behaviour and predict E2E latency under various topologies and unseen environments.

Parameter Identification in Power Transmission Systems Based on Graph Convolution Network

Abstract: Parameter Identification plays an important role in electric power transmission systems. Existing approaches for parameter identification tasks typically have two limitations: (1) They generally ignored development trend of historical data, and did not mine characteristics of corresponding power grid branches. (2) They did not consider the constraints of power grid topology, and treated different branches independently. Therefore, they could not characterize correlations between the center node and its neighborhoods. To overcome these limitations, this work proposes a multi-task graph convolutional neural network (MT-GCN) which utilizes the graph convolutional network (GCN) and the fully convolutional network (FCN) as building blocks for parameter identification. Specially, GCN can extract the structure information to enhance local feature extraction. FCN is a decoding module following GCN module, and it is used to identify the parameters of each branch according to its characteristics. Compared with previous methods, the proposed method is significantly improved in accuracy. Besides, this method is robust to measurement noise and errors, and can cope with multiple conditions in real power transmission systems.

PlaceRAN: optimal placement of virtualized network functions in Beyond 5G radio access networks

Abstract: The fifth-generation mobile evolution introduces Next-Generation Radio Access Networks (NG-RAN), splitting the RAN protocol stack into the eight disaggregated options combined into three network units, i.e., Central, Distributed, and Radio. The disaggregated units reach full interoperability on Open RAN. Further advances allow the RAN software to be virtualized (vNG-RAN) on top of general-purpose hardware, enabling the management of disaggregated units and protocols as radio functions. The placement of these functions is challenging since the best decision must be based on multiple constraints, e.g., the RAN protocol stack split, routing paths in network topologies with restricted bandwidth and latency, asymmetric computational resources, etc. The literature does not deal with general placement problems with high functional split options and protocol stack analysis. This article proposes the first exact model for positioning radio functions for vNG-RAN planning, named PlaceRAN, as a Binary Integer Linear Programming (BILP) problem. The objective is to minimize the computing resources and maximize the aggregation of radio functions. The evaluation considered two realistic network topologies, and the results reveal that PlaceRAN achieves an optimized high-performance aggregation level. It is flexible for RAN deployment overcoming the network restrictions, and up to date with the most advanced vNG-RAN design and development.

Topology Derivation of Multiple-Port DC–DC Converters Based on Voltage-Type Ports

Abstract: Multiple-port dc–dc converters are characterized by a variety of kinds and a large number of circuit topologies. In this article, we aim to reveal the intrinsic relationship among the topologies of multiple-port dc–dc converters and propose the topology derivation method. First, the voltage- and current-type ports are summarized from basic dc–dc converters, and the approach of converting current-type ports into voltage-type ports is discussed. Then, according to Kirchhoff's law, four types of multiple-input multiple-output converters named input-port-series output-port-series, input-port-series output-port-parallel, input-port-parallel output-port-series, and input-port-parallel output-port-parallel are presented. Second, the topology derivation method of multiple-port bidirectional dc–dc converters based on the voltage-type ports is studied in terms of power flow paths in various operation modes, and then the topology optimization method is proposed. Particularly, a flow diagram for the optimal design procedure is given to guide the topology derivation for some specific requirements. Based on the proposed approach, a family of multiple-port dc–dc converters can be derived, which provides lots of viable candidates for practical engineering. Furthermore, one derived converter named the parallel-type three-port bidirectional buck converter is analyzed in three operation modes to demonstrate the topology derivation. Finally, the effectiveness of the above theoretical analysis is verified by those experimental results.

A Graph Neural Network-Based Digital Twin for Network Slicing Management

Abstract: Network slicing has emerged as a promising networking paradigm to provide resources tailored for Industry 4.0 and diverse services in 5G networks. However, the increased network complexity poses a huge challenge in network management due to virtualized infrastructure and stringent quality-of-service requirements. Digital twin (DT) technology paves a way for achieving cost-efficient and performance-optimal management, through creating a virtual representation of slicing-enabled networks digitally to simulate its behaviors and predict the time-varying performance. In this article, a scalable DT of network slicing is developed, aiming to capture the intertwined relationships among slices and monitor the end-to-end (E2E) metrics of slices under diverse network environments. The proposed DT exploits the novel graph neural network model that can learn insights directly from slicing-enabled networks represented by non-Euclidean graph structures. Experimental results show that the DT can accurately mirror the network behaviour and predict E2E latency under various topologies and unseen environments.

A Cost-Effective Segmented Dynamic Wireless Charging System With Stable Efficiency and Output Power

Abstract: This article presents a cost-effective dynamic wireless power transfer (DWPT) system for efficient and stable output power charging of autonomous moving equipment. The proposed system is realized based on a low-cost segmented configuration and a flexible operating strategy. Specifically, the configuration is a combination of a dynamic T -series/series topology and extended transmitter (Tx) coils. The topology eliminates the cross-coupling impact of adjacent Tx coils and tunes the dynamic circuits at resonance. It makes the switching circuits equivalent to a unified analytical model for simplified control, which reduces the cost of inverter, decoupling, compensation, and position detection in the DWPT. The extended Tx coil with a simplified structure, which is obtained from winding coupling characteristics and a finite-element analysis based algorithm, is proposed to improve the moving misalignment tolerance, thereby reducing the number of Tx segments. An operating principle with three modes behind the proposed strategy is designed to fully utilize the efficient coupling area of individual Tx segment and improve the efficiency in the transition region from one segment to the other one. The operating parameters including the transition and compensation are obtained based on the topology and its operating principles. Position detection and power regulation methods are developed and embedded in the system control to configure/coordinate the segmented coils as the strategy and mitigate power fluctuation in the transition region. The performance and effectiveness of the proposed cost-effective DWPT system are evaluated based on experimental results on a scaled-down prototype.

A Novel Approach to Uncertainty Quantification in Groundwater Table Modeling by Automated Predictive Deep Learning

Abstract: Abstract Uncertainty quantification ( UQ ) is an important benchmark to assess the performance of artificial intelligence ( AI ) and particularly deep learning ensembled-based models. However, the ability for UQ using current AI -based methods is not only limited in terms of computational resources but it also requires changes to topology and optimization processes, as well as multiple performances to monitor model instabilities. From both geo-engineering and societal perspectives, a predictive groundwater table ( GWT ) model presents an important challenge, where a lack of UQ limits the validity of findings and may undermine science-based decisions. To overcome and address these limitations, a novel ensemble, an automated random deactivating connective weights approach ( ARDCW ), is presented and applied to retrieved geographical locations of GWT data from a geo-engineering project in Stockholm, Sweden. In this approach, the UQ was achieved via a combination of several derived ensembles from a fixed optimum topology subjected to randomly switched off weights, which allow predictability with one forward pass. The process was developed and programmed to provide trackable performance in a specific task and access to a wide variety of different internal characteristics and libraries. A comparison of performance with Monte Carlo dropout and quantile regression using computer vision and control task metrics showed significant progress in the ARDCW . This approach does not require changes in the optimization process and can be applied to already trained topologies in a way that outperforms other models.

Adaptive bipartite consensus of competitive linear multi‐agent systems with asynchronous intermittent communication

Abstract: In this article, bipartite consensus is investigated for linear multi‐agent systems (MASs) via adaptive asynchronous intermittent control. Adaptive asynchronous intermittent bipartite consensus protocols are proposed for MASs with antagonistic links under both fixed and connected switching topologies. By using gauge transformation and stability theory, convergency analysis is given and some conditions of bipartite consensus are obtained. It is turned out that bipartite consensus can be realized if the communication rate is no less than a threshold under the assumption that the network is connected and structurally balanced. Finally, two simulation examples are provided to verify the effectiveness of the obtained results.

Consensus Switching of Second-Order Multiagent Systems With Time Delay

Abstract: This technical correspondence studies the consensus problem for second-order multiagent systems under network topologies with a directed spanning tree. Consensus analysis for systems with the distributed delayed proportional-integral (PI)-type controller is given. Crossing directions of the characteristic roots can be identified by a sufficient condition. If the rightward crossing condition holds, the delay margin can be obtained to guarantee that the systems reach consensus if and only if the time delay is less than the critical value. Otherwise, it is possible that the systems switch from consensus to nonconsensus and back to the consensus as the delay increases. Simulation examples are provided to demonstrate the theoretical analysis.

Cerberus: The Power of Choices in Datacenter Topology Design - A Throughput Perspective

Abstract: The bandwidth and latency requirements of modern datacenter applications have led researchers to propose various topology designs using static, dynamic demand-oblivious (rotor), and/or dynamic demand-aware switches. However, given the diverse nature of datacenter traffic, there is little consensus about how these designs would fare against each other. In this work, we analyze the throughput of existing topology designs under different traffic patterns and study their unique advantages and potential costs in terms of bandwidth and latency "tax''. To overcome the identified inefficiencies, we propose Cerberus, a unified, two-layer leaf-spine optical datacenter design with three topology types. Cerberus systematically matches different traffic patterns with their most suitable topology type: e.g., latency-sensitive flows are transmitted via a static topology, all-to-all traffic via a rotor topology, and elephant flows via a demand-aware topology. We show analytically and in simulations that Cerberus can improve throughput significantly compared to alternative approaches and operate datacenters at higher loads while being throughput-proportional.

Integrating electric vehicles as virtual power plants: A comprehensive review on vehicle-to-grid (V2G) concepts, interface topologies, marketing and future prospects

Abstract: Global factors such as energy consumption and environmental issues encourage the utilization of electric vehicles (EVs) as alternative energy sources besides transportation. Recently, the development of virtual power plants integrated with clean energy sources has also enhanced the role of EVs in the transportation industry. Vehicle-grid integration (VGI) provides a practical and economical solution to improve energy sustainability and feed consumers on the user side. Although technical developments in the field show that the energy sector supports the effective use of EVs in stationary applications, the research studies confirm that scientific and industrial developments continue to improve the performance of using EVs as virtual power plants. However, a comprehensive study is needed to demonstrate the concepts, interfacing, and marketing of virtual power plants integrated with EVs for researchers and scientists working in this field. To this end, the current study aims to provide an extensive overview on the system configurations, interface topologies, marketing, and future perspectives in integrating EVs as virtual power plants. In this context, the integration concepts of VGI are investigated under the headings of stand-alone, grid-connected, transitional, and grid-supported operations. Then, VGI topologies are examined in terms of energy generation/storage units used in EVs, single-stage/two-stage/hybrid-multi-stage based systems, and grid-connection types & parameters. In the following section, the research projects and marketing values based on a large number of target data are introduced to show the current status of the VGI field. Lastly, future aspects, including charging strategies, intelligent technologies, and technical issues, are addressed and clarified.

Distributed Support Vector Machines Over Dynamic Balanced Directed Networks

Abstract: In this letter, we consider the binary classification problem via distributed Support Vector Machines (SVMs), where the idea is to train a network of agents, with limited share of data, to cooperatively learn the SVM classifier for the global database. Agents only share processed information regarding the classifier parameters and the gradient of the local loss functions instead of their raw data. In contrast to the existing work, we propose a continuous-time algorithm that incorporates network topology changes in discrete jumps. This hybrid nature allows us to remove chattering that arises because of the discretization of the underlying CT process. We show that the proposed algorithm converges to the SVM classifier over time-varying weight balanced directed graphs by using arguments from the matrix perturbation theory.

The Impact of Distributed Energy Storage on Distribution and Transmission Networks’ Power Quality

Abstract: This study investigates the effect of distributed Energy Storage Systems (ESSs) on the power quality of distribution and transmission networks. More specifically, this project aims to assess the impact of distributed ESS integration on power quality improvement in certain network topologies compared to typical centralized ESS architecture. Furthermore, an assessment is made to see if the network topology in which an ESS position supports its ability to restore node voltage magnitude within acceptable ranges. The power quality of a benchmark interconnected distribution and transmission network was determined using NEPLAN software. Following that, twelve variants of the benchmark were modeled, each with a different ESS integration architecture and (or) topology. Their power quality performance was compared to that of a benchmark network in addition to several cross analyses to determine the relative impact on power quality within the context of their respective ESS integration methodologies. The findings of this study buttress the understanding that the distributed ESS integration architecture within the distribution network topology, where the majority of consumer loads are connected, provides the strongest case for voltage magnitude power quality compensation, as required by the UK Electrical System Grid Code’s 5% rated node voltage compliance processes regulation.

Electrical Machines Winding Technology: Latest Advancements for Transportation Electrification

Abstract: The ever-increasing demand for higher-power dense electrical machines has resulted in different electrical, mechanical, and thermal stresses, which can eventually cause machine failure. For this reason, the management of stresses and losses must be thoughtfully investigated to have a highly reliable electrical machine. The literature agrees that winding losses are the dominant loss mechanism in many electrical machines. However, statements vary on how to mitigate these losses along with the aforementioned stresses. To avoid winding failure, a study of the various winding topologies would allow for a better consideration of the challenges and limitations in the performance of different electrical machines. To this aim, this paper introduces a comprehensive review for different winding topologies. Many reported cases in the literature are summarized and compared. Moreover, the utilization of additive manufacturing (AM) in the production of the machine windings is presented, showing a high level of maturity of this emerging technology. Finally, different challenges facing the design of machine windings are introduced including the AC high frequency losses, thermal management, mechanical and acoustic problems, insulation aging, automated production, and winding manufacturability.

Energy-Aware Hierarchical Resource Management and Backhaul Traffic Optimization in Heterogeneous Cellular Networks

Abstract: The dense deployment of small-cell networks is a key feature of the next-generation mobile networks employed to provide the necessary capacity increase. The small cells are installed in the areas covered by macro base stations (eNBs) to supply the required local capacity based on the known concept of the hierarchical HetNets. Moreover, small-cell networks use high-capacity backhaul links on millimeter-wave bands to develop multihop topologies to mitigate the costs of data transmission. Nonetheless, green networking gains great importance for the uncontrolled installation of too many small cells may escalate operational costs and emit more carbon dioxide. This article proposes a dynamic optimization model to minimize the overall energy consumption of fifth-generation (5G) heterogeneous networks and provide the essential coverage and capacity. Optimizing carrier allocation and power utilization, the proposed model determines when to turn on or off small cells to meet the quality of service constraints of users with the highest level of energy efficiency. We also proposed a multihop backhauling strategy to effectively use the existing infrastructure of small-cell networks for simultaneous dual-hop transmissions. The numerical results indicated considerable rates of power saving in different traffic models while guaranteeing the throughput requirements for uniform and hotspot user equipment distribution patterns. Also, according to the simulation results, energy efficiency and system data rates can significantly be improved.

STAR-IOS Aided NOMA Networks: Channel Model Approximation and Performance Analysis

Abstract: Simultaneous transmitting and reflecting intelligent omini-surfaces (STAR-IOSs) are able to achieve full coverage "smart radio environments". By splitting the energy or altering the active number of STAR-IOS elements, STAR-IOSs provide high flexibility of successive interference cancellation (SIC) orders for non-orthogonal multiple access (NOMA) systems. Based on the aforementioned advantages, this paper investigates a STAR-IOS-aided downlink NOMA network with randomly deployed users. We first propose three tractable channel models for different application scenarios, namely the central limit model, the curve fitting model, and the M-fold convolution model. More specifically, the central limit model fits the scenarios with large-size STAR-IOSs while the curve fitting model is extended to evaluate multi-cell networks. However, these two models cannot obtain accurate diversity orders. Hence, we figure out the M-fold convolution model to derive accurate diversity orders. We consider three protocols for STAR-IOSs, namely, the energy splitting (ES) protocol, the time switching (TS) protocol, and the mode switching (MS) protocol. Based on the ES protocol, we derive analytical outage probability expressions for the paired NOMA users by the central limit model and the curve fitting model. Based on three STAR-IOS protocols, we derive the diversity gains of NOMA users by the M-fold convolution model. The analytical results reveal that the diversity gain of NOMA users is equal to the active number of STAR-IOS elements. Numerical results indicate that 1) in high signal-to-noise ratio regions, the central limit model performs as an upper bound, while a lower bound is obtained by the curve fitting model; 2) the TS protocol has the best performance but requesting more time blocks than other protocols; 3) the ES protocol outperforms the MS protocol as the ES protocol has higher diversity gains.

Observer-Based Event-Triggered Formation Control of Multi-Agent Systems With Switching Directed Topologies

Abstract: This paper investigates the formation control problem for linear multi-agent systems under switching directed topologies. Based on absolute or relative outputs, we propose two distributed observer-based event-triggered control schemes. Both schemes can guarantee the boundedness of formation errors under sufficient conditions. The schemes can also avoid Zeno behaviors by giving an estimation for the lower bound of sampling intervals. Finally, simulations and experiments validate the proposed approaches.