Proceedings of the IEEE最新文献

{"title":"Proceedings of the IEEE Publication Information","authors":"","doi":"10.1109/JPROC.2024.3406006","DOIUrl":"https://doi.org/10.1109/JPROC.2024.3406006","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"112 4","pages":"C2-C2"},"PeriodicalIF":20.6,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10556791","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proceedings of the IEEE: Stay Informed. Become Inspired","authors":"","doi":"10.1109/JPROC.2024.3406014","DOIUrl":"https://doi.org/10.1109/JPROC.2024.3406014","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"112 4","pages":"C4-C4"},"PeriodicalIF":20.6,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10556794","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fairness and Bias in Robot Learning","authors":"Laura Londoño;Juana Valeria Hurtado;Nora Hertz;Philipp Kellmeyer;Silja Voeneky;Abhinav Valada","doi":"10.1109/JPROC.2024.3403898","DOIUrl":"10.1109/JPROC.2024.3403898","url":null,"abstract":"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.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"112 4","pages":"305-330"},"PeriodicalIF":20.6,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141177465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RIS-Aided Cell-Free Massive MIMO Systems for 6G: Fundamentals, System Design, and Applications","authors":"Enyu Shi;Jiayi Zhang;Hongyang Du;Bo Ai;Chau Yuen;Dusit Niyato;Khaled B. Letaief;Xuemin Shen","doi":"10.1109/JPROC.2024.3404491","DOIUrl":"https://doi.org/10.1109/JPROC.2024.3404491","url":null,"abstract":"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.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"112 4","pages":"331-364"},"PeriodicalIF":20.6,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward Resilient Modern Power Systems: From Single-Domain to Cross-Domain Resilience Enhancement","authors":"Hao Huang;H. Vincent Poor;Katherine R. Davis;Thomas J. Overbye;Astrid Layton;Ana E. Goulart;Saman Zonouz","doi":"10.1109/JPROC.2024.3405709","DOIUrl":"https://doi.org/10.1109/JPROC.2024.3405709","url":null,"abstract":"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.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"112 4","pages":"365-398"},"PeriodicalIF":20.6,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10556785","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scanning the Issue","authors":"","doi":"10.1109/JPROC.2024.3406128","DOIUrl":"https://doi.org/10.1109/JPROC.2024.3406128","url":null,"abstract":"Robot learning has advanced tremendously in the last decade. From learning low-level manipulation skills to long-horizon mobile manipulation tasks and autonomous driving, machine learning has accelerated the advancement in the entire spectrum of robotic domains. Much of this success has been fueled by data-driven learning algorithms, massive, curated datasets, and the doubling of computational capacity each year. We also witness more and more learned robotic systems performing tasks in human- centered environments alongside humans. Notable areas include robots in collaborative manufacturing, agriculture, logistics, and search and rescue operations.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"112 4","pages":"302-304"},"PeriodicalIF":20.6,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10556790","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Integrated Sensing and Communication Revolution for 6G: Vision, Techniques, and Applications","authors":"Nuria González-Prelcic;Musa Furkan Keskin;Ossi Kaltiokallio;Mikko Valkama;Davide Dardari;Xiao Shen;Yuan Shen;Murat Bayraktar;Henk Wymeersch","doi":"10.1109/JPROC.2024.3397609","DOIUrl":"10.1109/JPROC.2024.3397609","url":null,"abstract":"Future wireless networks will integrate sensing, learning, and communication to provide new services beyond communication and to become more resilient. Sensors at the network infrastructure, sensors on the user equipment (UE), and the sensing capability of the communication signal itself provide a new source of data that connects the physical and radio frequency (RF) environments. A wireless network that harnesses all these sensing data can not only enable additional sensing services but also become more resilient to channel-dependent effects such as blockage and better support adaptation in dynamic environments as networks reconfigure. In this article, we provide a vision for integrated sensing and communication (ISAC) networks and an overview of how signal processing, optimization, and machine learning (ML) techniques can be leveraged to make them a reality in the context of 6G. We also include some examples of the performance of several of these strategies when evaluated using a simulation framework based on a combination of ray-tracing measurements and mathematical models that mix the digital and physical worlds.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"112 7","pages":"676-723"},"PeriodicalIF":23.2,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141085263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wireless Information and Energy Transfer in the Era of 6G Communications","authors":"Constantinos Psomas;Konstantinos Ntougias;Nikita Shanin;Dongfang Xu;Kenneth Mayer;Nguyen Minh Tran;Laura Cottatellucci;Kae Won Choi;Dong In Kim;Robert Schober;Ioannis Krikidis","doi":"10.1109/JPROC.2024.3395178","DOIUrl":"10.1109/JPROC.2024.3395178","url":null,"abstract":"Wireless information and energy transfer (WIET) represents an emerging paradigm that employs controllable transmission of radio frequency signals for the dual purpose of data communication and wireless charging. As such, WIET is widely regarded as an enabler of envisioned sixth-generation (6G) use cases that rely on energy-sustainable Internet-of-Things (IoT) networks, such as smart cities and smart grids. Meeting the quality-of-service demands of WIET, in terms of both data transfer and power delivery, requires effective codesign of the information and energy signals. In this article, we present the main principles and design aspects of WIET, focusing on its integration in 6G networks. First, we discuss how conventional communication notions, such as resource allocation and waveform design, need to be revisited in the context of WIET. Next, we consider various candidate 6G technologies that can boost WIET efficiency, namely, holographic multiple-input multiple-output, near-field beamforming, terahertz communication, intelligent reflecting surfaces (IRSs), and reconfigurable (fluid) antenna arrays. We introduce respective WIET design methods, analyze the promising performance gains of these WIET systems, and discuss challenges, open issues, and future research directions. Finally, a near-field energy beamforming scheme and a power-based IRS beamforming algorithm are experimentally validated using a wireless energy transfer testbed. The vision of WIET in communication systems has been gaining momentum in recent years, with constant progress with respect to theoretical and also practical aspects. The comprehensive overview of the state of the art of WIET presented in this article highlights the potential of WIET systems and their overall benefits in 6G networks.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"112 7","pages":"764-804"},"PeriodicalIF":23.2,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141073914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"3-D-Printed Terahertz Metalenses for Next-Generation Communication and Imaging Applications","authors":"Geng-Bo Wu;Jin Chen;Chenfeng Yang;Ka Fai Chan;Mu Ku Chen;Din Ping Tsai;Chi Hou Chan","doi":"10.1109/JPROC.2024.3395891","DOIUrl":"10.1109/JPROC.2024.3395891","url":null,"abstract":"Three-dimensional (3-D) printing, also known as additive manufacturing, provides a novel and cost-effective approach for implementing microwave devices. With the rapid advancement and improved manufacturing resolution of the 3-D printing technology, additive manufacturing has enabled the design and fabrication of electronic devices in higher terahertz (THz) frequency bands, contributing to bridging the gap between microwaves and photonics. Simultaneously, metalenses have garnered significant attention due to their ability to shape electromagnetic (EM) wavefronts. Metalens technology offers a promising solution for wave focusing, surpassing traditional dielectric lenses with advantages such as reduced weight and low loss, particularly at THz frequencies. In this article, we present an overview of the development of 3-D-printed THz metalenses, ranging from single metalenses to dual-layer and trilayer configurations. The functionality of the metalenses becomes more powerful, from replacing conventional light-focusing dielectric lenses for single-layer metalenses, achieving 2-D beam scanning, holographic imaging, and reconfigurable orbital angular momentum (OAM) for dual-layer metalenses, to enabling 3-D focus scanning for trilayer metalenses. We also discuss practical measurement technologies for THz metalenses and briefly outline the prospective to propel the 3-D-printed metalens technology forward.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"112 8","pages":"1033-1050"},"PeriodicalIF":23.2,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140907226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In-Band Full-Duplex: The Physical Layer","authors":"Besma Smida;Risto Wichman;Kenneth E. Kolodziej;Himal A. Suraweera;Taneli Riihonen;Ashutosh Sabharwal","doi":"10.1109/JPROC.2024.3366768","DOIUrl":"10.1109/JPROC.2024.3366768","url":null,"abstract":"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.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"112 5","pages":"433-462"},"PeriodicalIF":23.2,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140067668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}