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Toward Resilient Modern Power Systems: From Single-Domain to Cross-Domain Resilience Enhancement
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| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 112 (No. 4) |
| Halaman | : | 365-398 |
| Abstrak | : | Modern power systems are the backbone of our society, supplying electric energy for daily activities. With the integration of communication networks and high penetration of renewable energy sources (RESs), modern power systems have evolved into a cross-domain multilayer complex system of systems with improved efficiency, controllability, and sustainability. However, increasing numbers of unexpected events, including natural disasters, extreme weather, and cyberattacks, are compromising the functionality of modern power systems and causing tremendous societal and economic losses. Resilience, a desirable property, is needed in modern power systems to ensure their capability to withstand all kinds of hazards while maintaining their functions. This article presents a systematic review of recent power system resilience enhancement techniques and proposes new directions for enhancing modern power systems’ resilience considering their cross-domain multilayer features. We first answer the question, “what is power system resilience?” from the perspectives of its definition, constituents, and categorization. It is important to recognize that power system resilience depends on two interdependent factors: network design and system operation. Following that, we present a review of articles published since 2016 that have developed innovative methodologies to improve power system resilience and categorize them into infrastructural resilience enhancement and operational resilience enhancement. We discuss their problem formulations and proposed quantifiable resilience measures, as well as point out their merits and limitations. Finally, we argue that it is paramount to leverage higher order subgraph studies and scientific machine learning (SciML) for modern power systems to capture the interdependence and interactions across heterogeneous networks and data for holistically enhancing their infrastructural and operational resilience. |
Internet-Based Social Engineering Psychology, Attacks, and Defenses: A Survey
| Pengarang | : | Theodore Tangie Longtchi |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 112 (No. 3) |
| Halaman | : | 210-246 |
| Abstrak | : | Internet-based social engineering (SE) attacks are a major cyber threat. These attacks often serve as the first step in a sophisticated sequence of attacks that target, among other things, victims’ credentials and can cause financial losses. The problem has received mounting attention in recent years, with many publications proposing defenses against SE attacks. Despite this, the situation has not improved. In this article, we aim to understand and explain this phenomenon by investigating the root cause of the problem. To this end, we examine Internet-based SE attacks and defenses through a unique lens based on psychological factors (PFs) and psychological techniques (PTs). We find that there is a key discrepancy between attacks and defenses: SE attacks have deliberately exploited 46 PFs and 16 PTs in total, but existing defenses have only leveraged 16 PFs and seven PTs in total. This discrepancy may explain why existing defenses have achieved limited success and prompt us to propose a systematic roadmap for future research. |
RIS-Aided Cell-Free Massive MIMO Systems for 6G: Fundamentals, System Design, and Applications
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 112 (No. 4) |
| Halaman | : | 331-364 |
| Abstrak | : | An introduction of intelligent interconnectivity for people and things has posed higher demands and more challenges for sixth-generation (6G) networks, such as high spectral efficiency and energy efficiency (EE), ultralow latency, and ultrahigh reliability. Cell-free (CF) massive multiple-input-multiple-output (mMIMO) and reconfigurable intelligent surface (RIS), also called intelligent reflecting surface (IRS), are two promising technologies for coping with these unprecedented demands. Given their distinct capabilities, integrating the two technologies to further enhance wireless network performances has received great research and development attention. In this article, we provide a comprehensive survey of research on RIS-aided CF mMIMO wireless communication systems. We first introduce system models focusing on system architecture and application scenarios, channel models, and communication protocols. Subsequently, we summarize the relevant studies on system operation and resource allocation, providing in-depth analyses and discussions. Following this, we present practical challenges faced by RIS-aided CF mMIMO systems, particularly those introduced by RIS, such as hardware impairments (HIs) and electromagnetic interference (EMI). We summarize the corresponding analyses and solutions to further facilitate the implementation of RIS-aided CF mMIMO systems. Furthermore, we explore an interplay between RIS-aided CF mMIMO and other emerging 6G technologies, such as millimeter wave (mmWave) and terahertz (THz), simultaneous wireless information and power transfer (SWIPT), next-generation multiple access (NGMA), and unmanned aerial vehicle (UAV). Finally, we outline several research directions for future RIS-aided CF mMIMO systems. |
An Overview of Advancements in Multimotor Drives: Structural Diversity, Advanced Control, Specific Technical Challenges, and Solutions
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 112 (No. 3) |
| Halaman | : | 184-209 |
| Abstrak | : | Multimotor drives have become increasingly important in modern industrial applications due to their ability to provide superior performance, efficiency, and flexibility compared to single-motor systems. Hence, this article presents an overview of recent advancements in multimotor drives, focusing on three main areas: structural diversity, advanced control, and emerging challenges and solutions. First, the various structural configurations of multimotor drives are summarized, which include parallel, cascaded, and hybrid configurations. The features as well as component motors and converters of each configuration are discussed, along with the selection rules of a particular configuration for a given application. Second, from the perspective of different performance requirements, the advanced control technologies used for multimotor drives are discussed. Then, this article highlights the technical challenges associated with multimotor drives, including coordination control, mutual interference, communication, interdependent fault diagnosis, and power quality. Meanwhile, viable solutions to these challenges are summarized. Finally, a discussion of the future directions and opportunities for further research and development in the field of multimotor drives is presented. Through this article, scholars and engineers can gain a comprehensive understanding of current and future developments in multimotor drives, contributing to continued research in this field and facilitating successful integration into various applications. |
Fairness and Bias in Robot Learning
| Pengarang | : | Laura Londoño |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 112 (No. 4) |
| Halaman | : | 305-330 |
| Abstrak | : | Machine learning (ML) has significantly enhanced the abilities of robots, enabling them to perform a wide range of tasks in human environments and adapt to our uncertain real world. Recent works in various ML domains have highlighted the importance of accounting for fairness to ensure that these algorithms do not reproduce human biases and consequently lead to discriminatory outcomes. With robot learning systems increasingly performing more and more tasks in our everyday lives, it is crucial to understand the influence of such biases to prevent unintended behavior toward certain groups of people. In this work, we present the first survey on fairness in robot learning from an interdisciplinary perspective spanning technical, ethical, and legal challenges. We propose a taxonomy for sources of bias and the resulting types of discrimination due to them. Using examples from different robot learning domains, we examine scenarios of unfair outcomes and strategies to mitigate them. We present early advances in the field by covering different fairness definitions, ethical and legal considerations, and methods for fair robot learning. With this work, we aim to pave the road for groundbreaking developments in fair robot learning. |
Saltation Matrices: The Essential Tool for Linearizing Hybrid Dynamical Systems
| Pengarang | : | Nathan J. Kong |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 112 (No. 6) |
| Halaman | : | 585-608 |
| Abstrak | : | Hybrid dynamical systems, i.e., systems that have both continuous and discrete states, are ubiquitous in engineering but are difficult to work with due to their discontinuous transitions. For example, a robot leg is able to exert very little control effort, while it is in the air compared to when it is on the ground. When the leg hits the ground, the penetrating velocity instantaneously collapses to zero. These instantaneous changes in dynamics and discontinuities (or jumps) in state make standard smooth tools for planning, estimation, control, and learning difficult for hybrid systems. One of the key tools for accounting for these jumps is called the saltation matrix. The saltation matrix is the sensitivity update when a hybrid jump occurs and has been used in a variety of fields, including robotics, power circuits, and computational neuroscience. This article presents an intuitive derivation of the saltation matrix and discusses what it captures, where it has been used in the past, how it is used for linear and quadratic forms, how it is computed for rigid body systems with unilateral constraints, and some of the structural properties of the saltation matrix in these cases. |
Subband Full-Duplex Large-Scale Deployed Network Designs and Tradeoffs
| Pengarang | : | Muhammad Abdelghaffar |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 112 (No. 5) |
| Halaman | : | 487-510 |
| Abstrak | : | Time-division duplex (TDD) and frequency-division duplex (FDD) are mainly used in commercial new radio (NR) deployments, where the time- or frequency-domain resources are split between downlink (DL) and uplink (UL). Full duplex (FD) will enable 5G-advanced and 6G systems to go beyond TDD and FDD operation into a new duplexing mode that leverages the benefits of both TDD/FDD deployments. It achieves higher throughput and lower latency while enabling flexible UL/DL scheduling. However, there are several challenges that need to be overcome to enable FD operation in large-scale system deployment, including intranode and internode [user equipment (UE) and next-generation node B (5G base station)] interference along with intercarrier interference. In this article, we present solutions to mitigate self-interference (SI) and cross-link interference (CLI) in 5G-advanced/6G systems, provide system-level evaluations, and discuss the outcome of Third Generation Partnership Project (3GPP) study item on duplexing evolution. We introduce the concept of subband FD (SBFD) as an effective solution for a macro network to achieve the key features of FD, such as latency reduction and UL link budget improvement. Finally, we present the field test results for the performance of world-first SBFD prototype of high transmit power massive-multi-input-multioutput (MIMO) macro network. |
Brain-Inspired Computing: A Systematic Survey and Future Trends
| Pengarang | : | Lei Deng |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 112 (No. 6) |
| Halaman | : | 544-584 |
| Abstrak | : | Brain-inspired computing (BIC) is an emerging research field that aims to build fundamental theories, models, hardware architectures, and application systems toward more general artificial intelligence (AI) by learning from the information processing mechanisms or structures/functions of biological nervous systems. It is regarded as one of the most promising research directions for future intelligent computing in the post-Moore era. In the past few years, various new schemes in this field have sprung up to explore more general AI. These works are quite divergent in the aspects of modeling/algorithm, software tool, hardware platform, and benchmark data since BIC is an interdisciplinary field that consists of many different domains, including computational neuroscience, AI, computer science, statistical physics, material science, and microelectronics. This situation greatly impedes researchers from obtaining a clear picture and getting started in the right way. Hence, there is an urgent requirement to do a comprehensive survey in this field to help correctly recognize and analyze such bewildering methodologies. What are the key issues to enhance the development of BIC? What roles do the current mainstream technologies play in the general framework of BIC? Which techniques are truly useful in real-world applications? These questions largely remain open. To address the above issues, in this survey, we first clarify the biggest challenge of BIC: how can AI models benefit from the recent advancements in computational neuroscience? With this challenge in mind, we will focus on discussing the concept of BIC and summarize four components of BIC infrastructure development: 1) modeling/algorithm; 2) hardware platform; 3) software tool; and 4) benchmark data. For each component, we will summarize its recent progress, main challenges to resolve, and future trends. Based on these studies, we present a general framework for the real-world applications of BIC systems, which is promising to benefit both AI and brain science. Finally, we claim that it is extremely important to build a research ecology to promote prosperity continuously in this field. |
A State of the Art Survey on Full Duplex Network Design
| Pengarang | : | Yonghwi Kim |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 112 (No. 5) |
| Halaman | : | 463-486 |
| Abstrak | : | Full-duplex (FD) technology is gaining popularity for integration into a wide range of wireless networks due to its demonstrated potential in recent studies. In contrast to half-duplex (HD) technology, the implementation of FD in networks necessitates considering internode interference (INI) from various network perspectives. When deploying FD technology in networks, several critical factors must be taken into account. These include self-interference (SI) and the requisite SI cancellation (SIC) processes, as well as the selection of multiple user equipment (UE) per time slot. In addition, INI, including cross-link interference (CLI) and intercell interference (ICI), becomes a crucial issue during concurrent uplink (UL) and downlink (DL) transmission and reception, similar to SI. Since most INIs are challenging to eliminate, a comprehensive investigation that covers radio resource control (RRC), medium access control (MAC), and the physical (PHY) layer is essential in the context of FD network design, rather than focusing on individual network layers and types. This article covers state-of-the-art studies, including protocols and documents from the third-generation partnership project (3GPP) for FD, MAC protocol, user scheduling, and CLI handling. The methods are also compared through a network-level system simulation based on 3-D ray tracing. |
In-Band Full-Duplex: The Physical Layer
| Pengarang | : | Besma Smida |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 112 (No. 5) |
| Halaman | : | 433-462 |
| Abstrak | : | In this article, we review the key concepts and the progress in the design of physical-layer aspects of in-band full-duplex (IBFD) communications. One of the fundamental challenges in realizing IBFD is self-interference that can be up to 100 dB stronger than signals of interest. Thus, we start by reviewing state-of-the-art research in self-interference cancellation, addressing both model-based and emerging machine learning-based methods. Then, we turn our attention to new wireless systems with many degrees of freedom for which the traditional IBFD designs do not gracefully scale and, hence, require many innovations to enable IBFD. We provide an extensive review of basic concepts and state of the art in massive multiple-input–multiple-output IBFD. Then, we consider the mmWave band IBFD and review advanced physical-layer architectures. The above review provides the proper context to discuss IBFD innovations and new challenges for sixth-generation networks and beyond, where wireless networks are envisioned to be multifunctional, combining communications with functions such as sensing, cognitive radios, physical-layer security, and wireless power transfer. We conclude this article with a status update on the adoption of IBFD in communication standards. |