Pub Date : 2024-08-20DOI: 10.1109/JPROC.2024.3434194
{"title":"Proceedings of the IEEE Publication Information","authors":"","doi":"10.1109/JPROC.2024.3434194","DOIUrl":"https://doi.org/10.1109/JPROC.2024.3434194","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"112 5","pages":"C2-C2"},"PeriodicalIF":23.2,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10640258","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142013271","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}
Pub Date : 2024-08-19DOI: 10.1109/JPROC.2024.3440211
Nathan J. Kong;J. Joe Payne;James Zhu;Aaron M. Johnson
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.
{"title":"Saltation Matrices: The Essential Tool for Linearizing Hybrid Dynamical Systems","authors":"Nathan J. Kong;J. Joe Payne;James Zhu;Aaron M. Johnson","doi":"10.1109/JPROC.2024.3440211","DOIUrl":"10.1109/JPROC.2024.3440211","url":null,"abstract":"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.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"112 6","pages":"585-608"},"PeriodicalIF":23.2,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142007272","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}
Artificial intelligence (AI) technologies have emerged as pivotal enablers across a multitude of industries, including consumer electronics, healthcare, and manufacturing, largely due to their significant resurgence over the past decade. The transformative power of AI is primarily derived from the utilization of deep neural networks (DNNs), which require extensive data for training and substantial computational resources for processing. Consequently, DNN models are typically trained and deployed on resource-rich cloud servers. However, due to potential latency issues associated with cloud communications, deep learning (DL) workflows (e.g., DNN training and inference) are increasingly being transitioned to wireless edge networks in proximity to end-user devices (EUDs). This shift is designed to support latency-sensitive applications and has given rise to a new paradigm of edge AI, which will play a critical role in upcoming sixth-generation (6G) networks to support ubiquitous AI applications. Despite its considerable potential, edge AI faces substantial challenges, mostly due to the dichotomy between the resource limitations of wireless edge networks and the resource-intensive nature of DL. Specifically, the acquisition of large-scale data, as well as the training and inference processes of DNNs, can rapidly deplete the battery energy of EUDs. This necessitates an energy-conscious approach to edge AI to ensure both optimal and sustainable performance. In this article, we present a contemporary survey on green edge AI. We commence by analyzing the principal energy consumption components of edge AI systems to identify the fundamental design principles of green edge AI. Guided by these principles, we then explore energy-efficient design methodologies for the three critical tasks in edge AI systems, including training data acquisition, edge training, and edge inference. Finally, we underscore potential future research directions to further enhance the energy efficiency (EE) of edge AI.
{"title":"Green Edge AI: A Contemporary Survey","authors":"Yuyi Mao;Xianghao Yu;Kaibin Huang;Ying-Jun Angela Zhang;Jun Zhang","doi":"10.1109/JPROC.2024.3437365","DOIUrl":"10.1109/JPROC.2024.3437365","url":null,"abstract":"Artificial intelligence (AI) technologies have emerged as pivotal enablers across a multitude of industries, including consumer electronics, healthcare, and manufacturing, largely due to their significant resurgence over the past decade. The transformative power of AI is primarily derived from the utilization of deep neural networks (DNNs), which require extensive data for training and substantial computational resources for processing. Consequently, DNN models are typically trained and deployed on resource-rich cloud servers. However, due to potential latency issues associated with cloud communications, deep learning (DL) workflows (e.g., DNN training and inference) are increasingly being transitioned to wireless edge networks in proximity to end-user devices (EUDs). This shift is designed to support latency-sensitive applications and has given rise to a new paradigm of edge AI, which will play a critical role in upcoming sixth-generation (6G) networks to support ubiquitous AI applications. Despite its considerable potential, edge AI faces substantial challenges, mostly due to the dichotomy between the resource limitations of wireless edge networks and the resource-intensive nature of DL. Specifically, the acquisition of large-scale data, as well as the training and inference processes of DNNs, can rapidly deplete the battery energy of EUDs. This necessitates an energy-conscious approach to edge AI to ensure both optimal and sustainable performance. In this article, we present a contemporary survey on green edge AI. We commence by analyzing the principal energy consumption components of edge AI systems to identify the fundamental design principles of green edge AI. Guided by these principles, we then explore energy-efficient design methodologies for the three critical tasks in edge AI systems, including training data acquisition, edge training, and edge inference. Finally, we underscore potential future research directions to further enhance the energy efficiency (EE) of edge AI.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"112 7","pages":"880-911"},"PeriodicalIF":23.2,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141991776","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}
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.
{"title":"Brain-Inspired Computing: A Systematic Survey and Future Trends","authors":"Guoqi Li;Lei Deng;Huajin Tang;Gang Pan;Yonghong Tian;Kaushik Roy;Wolfgang Maass","doi":"10.1109/JPROC.2024.3429360","DOIUrl":"10.1109/JPROC.2024.3429360","url":null,"abstract":"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.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"112 6","pages":"544-584"},"PeriodicalIF":23.2,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141986412","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}
The relentless demand for data in our society has driven the continuous evolution of wireless technologies to enhance network capacity. While current deployments of 5G have made strides in this direction using massive multiple-input-multiple-output (MIMO) and millimeter-wave (mmWave) bands, all existing wireless systems operate in a half-duplex (HD) mode. Full-duplex (FD) wireless communication, on the other hand, enables simultaneous transmission and reception (STAR) of signals at the same frequency, offering advantages such as enhanced spectrum efficiency, improved data rates, and reduced latency. This article presents a comprehensive review of FD wireless systems, with a focus on hardware design, implementation, cross-layered considerations, and applications. The major bottleneck in achieving FD communication is the presence of self-interference (SI) signals from the transmitter (TX) to the receiver, and achieving SI cancellation (SIC) with real-time adaption is critical for FD deployment. The review starts by establishing a system-level understanding of FD wireless systems, followed by a review of the architectures of antenna interfaces and integrated RF and baseband (BB) SI cancellers, which show promise in enabling low-cost, small-form-factor, portable FD systems. We then discuss digital cancellation techniques, including digital signal processing (DSP)- and learning-based algorithms. The challenges presented by FD phased-array and MIMO systems are discussed, followed by system-level aspects, including optimization algorithms, opportunities in the higher layers of the networking protocol stack, and testbed integration. Finally, the relevance of FD systems in applications such as next-generation (xG) wireless, mmWave repeaters, radars, and noncommunication domains is highlighted. Overall, this comprehensive review provides valuable insights into the design, implementation, and applications of FD wireless systems while opening up new directions for future research.
{"title":"Doubling Down on Wireless Capacity: A Review of Integrated Circuits, Systems, and Networks for Full Duplex","authors":"Aravind Nagulu;Negar Reiskarimian;Tingjun Chen;Sasank Garikapati;Igor Kadota;Tolga Dinc;Sastry Lakshmi Garimella;Manav Kohli;Alon Simon Levin;Gil Zussman;Harish Krishnaswamy","doi":"10.1109/JPROC.2024.3438755","DOIUrl":"10.1109/JPROC.2024.3438755","url":null,"abstract":"The relentless demand for data in our society has driven the continuous evolution of wireless technologies to enhance network capacity. While current deployments of 5G have made strides in this direction using massive multiple-input-multiple-output (MIMO) and millimeter-wave (mmWave) bands, all existing wireless systems operate in a half-duplex (HD) mode. Full-duplex (FD) wireless communication, on the other hand, enables simultaneous transmission and reception (STAR) of signals at the same frequency, offering advantages such as enhanced spectrum efficiency, improved data rates, and reduced latency. This article presents a comprehensive review of FD wireless systems, with a focus on hardware design, implementation, cross-layered considerations, and applications. The major bottleneck in achieving FD communication is the presence of self-interference (SI) signals from the transmitter (TX) to the receiver, and achieving SI cancellation (SIC) with real-time adaption is critical for FD deployment. The review starts by establishing a system-level understanding of FD wireless systems, followed by a review of the architectures of antenna interfaces and integrated RF and baseband (BB) SI cancellers, which show promise in enabling low-cost, small-form-factor, portable FD systems. We then discuss digital cancellation techniques, including digital signal processing (DSP)- and learning-based algorithms. The challenges presented by FD phased-array and MIMO systems are discussed, followed by system-level aspects, including optimization algorithms, opportunities in the higher layers of the networking protocol stack, and testbed integration. Finally, the relevance of FD systems in applications such as next-generation (xG) wireless, mmWave repeaters, radars, and noncommunication domains is highlighted. Overall, this comprehensive review provides valuable insights into the design, implementation, and applications of FD wireless systems while opening up new directions for future research.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"112 5","pages":"405-432"},"PeriodicalIF":23.2,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141986411","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}
Pub Date : 2024-08-07DOI: 10.1109/JPROC.2024.3435012
Maxime Fontana;Michael Spratling;Miaojing Shi
Multitask learning (MTL) aims to learn multiple tasks simultaneously while exploiting their mutual relationships. By using shared resources to simultaneously calculate multiple outputs, this learning paradigm has the potential to have lower memory requirements and inference times compared to the traditional approach of using separate methods for each task. Previous work in MTL has mainly focused on fully supervised methods, as task relationships (TRs) can not only be leveraged to lower the level of data dependency of those methods but also improve the performance. However, MTL introduces a set of challenges due to a complex optimization scheme and a higher labeling requirement. This article focuses on how MTL could be utilized under different partial supervision settings to address these challenges. First, this article analyses how MTL traditionally uses different parameter sharing techniques to transfer knowledge in between tasks. Second, it presents different challenges arising from such a multiobjective optimization (MOO) scheme. Third, it introduces how task groupings (TGs) can be achieved by analyzing TRs. Fourth, it focuses on how partially supervised methods applied to MTL can tackle the aforementioned challenges. Lastly, this article presents the available datasets, tools, and benchmarking results of such methods. The reviewed articles, categorized following this work, are available at https://github.com/Klodivio355/MTL-CV-Review .
{"title":"When Multitask Learning Meets Partial Supervision: A Computer Vision Review","authors":"Maxime Fontana;Michael Spratling;Miaojing Shi","doi":"10.1109/JPROC.2024.3435012","DOIUrl":"10.1109/JPROC.2024.3435012","url":null,"abstract":"Multitask learning (MTL) aims to learn multiple tasks simultaneously while exploiting their mutual relationships. By using shared resources to simultaneously calculate multiple outputs, this learning paradigm has the potential to have lower memory requirements and inference times compared to the traditional approach of using separate methods for each task. Previous work in MTL has mainly focused on fully supervised methods, as task relationships (TRs) can not only be leveraged to lower the level of data dependency of those methods but also improve the performance. However, MTL introduces a set of challenges due to a complex optimization scheme and a higher labeling requirement. This article focuses on how MTL could be utilized under different partial supervision settings to address these challenges. First, this article analyses how MTL traditionally uses different parameter sharing techniques to transfer knowledge in between tasks. Second, it presents different challenges arising from such a multiobjective optimization (MOO) scheme. Third, it introduces how task groupings (TGs) can be achieved by analyzing TRs. Fourth, it focuses on how partially supervised methods applied to MTL can tackle the aforementioned challenges. Lastly, this article presents the available datasets, tools, and benchmarking results of such methods. The reviewed articles, categorized following this work, are available at \u0000<uri>https://github.com/Klodivio355/MTL-CV-Review</uri>\u0000.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"112 6","pages":"516-543"},"PeriodicalIF":23.2,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141904438","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}
Pub Date : 2024-08-05DOI: 10.1109/JPROC.2024.3419158
Muhammad Abdelghaffar;Thomas Valerrian Pasca Santhappan;Yeliz Tokgoz;Kiran Mukkavilli;and Tingfang Ji
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.
{"title":"Subband Full-Duplex Large-Scale Deployed Network Designs and Tradeoffs","authors":"Muhammad Abdelghaffar;Thomas Valerrian Pasca Santhappan;Yeliz Tokgoz;Kiran Mukkavilli;and Tingfang Ji","doi":"10.1109/JPROC.2024.3419158","DOIUrl":"10.1109/JPROC.2024.3419158","url":null,"abstract":"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.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"112 5","pages":"487-510"},"PeriodicalIF":23.2,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141895552","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}
Pub Date : 2024-06-26DOI: 10.1109/JPROC.2024.3416050
Sisi Miao;Claus Kestel;Lucas Johannsen;Marvin Geiselhart;Laurent Schmalen;Alexios Balatsoukas-Stimming;Gianluigi Liva;Norbert Wehn;Stephan Ten Brink
Error correction coding (i.e., channel coding) is a key ingredient of any digital communications system. In mobile wireless communications, channel codes have evolved from simple convolutional codes in Global System for Mobile Communications (GSM) (2G), parallel concatenated (turbo) codes in Universal Mobile Telecommunications Service (UMTS) (3G), and long-term evolution (LTE) (4G), to carefully designed multirate/multilength low-density parity-check (LDPC) codes in 5G, combined with polar codes for short messages on the synchronization channel. Based on this rich history, and by accounting for the technological advances in very large-scale integration, this article will outline some recent trends in channel coding as they may be applied in 6G systems, ranging from novel approaches for short blocklengths such as automorphism ensemble decoding, via ideas of coding for multiple access, to concepts for unified coding schemes that may simplify encoding/decoding hardware at competitive error-correcting performance.
{"title":"Trends in Channel Coding for 6G","authors":"Sisi Miao;Claus Kestel;Lucas Johannsen;Marvin Geiselhart;Laurent Schmalen;Alexios Balatsoukas-Stimming;Gianluigi Liva;Norbert Wehn;Stephan Ten Brink","doi":"10.1109/JPROC.2024.3416050","DOIUrl":"10.1109/JPROC.2024.3416050","url":null,"abstract":"Error correction coding (i.e., channel coding) is a key ingredient of any digital communications system. In mobile wireless communications, channel codes have evolved from simple convolutional codes in Global System for Mobile Communications (GSM) (2G), parallel concatenated (turbo) codes in Universal Mobile Telecommunications Service (UMTS) (3G), and long-term evolution (LTE) (4G), to carefully designed multirate/multilength low-density parity-check (LDPC) codes in 5G, combined with polar codes for short messages on the synchronization channel. Based on this rich history, and by accounting for the technological advances in very large-scale integration, this article will outline some recent trends in channel coding as they may be applied in 6G systems, ranging from novel approaches for short blocklengths such as automorphism ensemble decoding, via ideas of coding for multiple access, to concepts for unified coding schemes that may simplify encoding/decoding hardware at competitive error-correcting performance.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"112 7","pages":"653-675"},"PeriodicalIF":23.2,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141461602","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}
Pub Date : 2024-06-18DOI: 10.1109/JPROC.2024.3409428
Bruno Clerckx;Yijie Mao;Zhaohui Yang;Mingzhe Chen;Ahmed Alkhateeb;Liang Liu;Min Qiu;Jinhong Yuan;Vincent W. S. Wong;Juan Montojo
Multiple access (MA) is a crucial part of any wireless system and refers to techniques that make use of the resource dimensions (e.g., time, frequency, power, antenna, code, and message) to serve multiple users/devices/machines/ services, ideally in the most efficient way. Given the increasing need of multifunctional wireless networks for integrated communications, sensing, localization, and computing, coupled with the surge of machine learning (ML)/artificial intelligence (AI) in wireless networks, MA techniques are expected to experience a paradigm shift in 6G and beyond. In this article, we provide a tutorial, survey, and outlook on past, emerging, and future MA techniques and pay particular attention to how wireless network intelligence and multifunctionality will lead to a rethinking of those techniques. This article starts with an overview of orthogonal, physical-layer multicasting, space domain, power domain (PD), rate-splitting, code-domain MAs, MAs in other domains, and random access (RA), and highlights the importance of conducting research in universal MA (UMA) to shrink instead of grow the knowledge tree of MA schemes by providing a unified understanding of MA schemes across all resource dimensions. It then jumps into rethinking MA schemes in the era of wireless network intelligence, covering AI for MA such as AI-empowered resource allocation, optimization, channel estimation, and receiver designs, for different MA schemes, and MA for AI such as federated learning (FL)/edge intelligence and over-the-air computation (AirComp). We then discuss MA for network multifunctionality and the interplay between MA and integrated sensing, localization, and communications, covering MA for joint sensing and communications, multimodal sensing-aided communications, multimodal sensing and digital twin-assisted communications, and communication-aided sensing/localization systems. We finish with studying MA for emerging intelligent applications such as semantic communications (SeComs), virtual reality (VR), and smart radio and reconfigurable intelligent surfaces (RISs), before presenting a roadmap toward 6G standardization. Throughout the text, we also point out numerous directions that are promising for future research.
多重接入（MA）是任何无线系统的重要组成部分，是指利用资源维度（如时间、频率、功率、天线、代码和信息）为多个用户/设备/机器/服务提供服务的技术，最好是以最有效的方式提供服务。鉴于多功能无线网络对集成通信、传感、定位和计算的需求与日俱增，再加上机器学习（ML）/人工智能（AI）在无线网络中的迅猛发展，预计 MA 技术将在 6G 及以后经历一次范式转变。在本文中，我们将对过去、新兴和未来的 MA 技术进行介绍、调查和展望，并特别关注无线网络的智能性和多功能性将如何导致对这些技术的重新思考。本文首先概述了正交、物理层组播、空间域、功率域 (PD)、速率分割、码域 MA、其他域中的 MA 以及随机接入 (RA)，并强调了开展通用 MA (UMA) 研究的重要性，通过提供对所有资源维度 MA 方案的统一理解，缩小而不是扩大 MA 方案的知识树。然后，我们将跳转到对无线网络智能时代的无线宽带接入方案的重新思考，其中包括针对不同无线宽带接入方案的无线宽带接入人工智能，如人工智能驱动的资源分配、优化、信道估计和接收器设计，以及针对人工智能的无线宽带接入，如联合学习（FL）/边缘智能和空中计算（AirComp）。然后，我们将讨论用于网络多功能性的人工智能，以及人工智能与综合传感、定位和通信之间的相互作用，包括用于联合传感和通信、多模态传感辅助通信、多模态传感和数字孪生辅助通信，以及通信辅助传感/定位系统的人工智能。最后，我们研究了用于新兴智能应用的 MA，如语义通信 (SeComs)、虚拟现实 (VR)、智能无线电和可重构智能表面 (RIS)，然后提出了实现 6G 标准化的路线图。在全文中，我们还指出了许多很有希望的未来研究方向。
{"title":"Multiple Access Techniques for Intelligent and Multifunctional 6G: Tutorial, Survey, and Outlook","authors":"Bruno Clerckx;Yijie Mao;Zhaohui Yang;Mingzhe Chen;Ahmed Alkhateeb;Liang Liu;Min Qiu;Jinhong Yuan;Vincent W. S. Wong;Juan Montojo","doi":"10.1109/JPROC.2024.3409428","DOIUrl":"10.1109/JPROC.2024.3409428","url":null,"abstract":"Multiple access (MA) is a crucial part of any wireless system and refers to techniques that make use of the resource dimensions (e.g., time, frequency, power, antenna, code, and message) to serve multiple users/devices/machines/ services, ideally in the most efficient way. Given the increasing need of multifunctional wireless networks for integrated communications, sensing, localization, and computing, coupled with the surge of machine learning (ML)/artificial intelligence (AI) in wireless networks, MA techniques are expected to experience a paradigm shift in 6G and beyond. In this article, we provide a tutorial, survey, and outlook on past, emerging, and future MA techniques and pay particular attention to how wireless network intelligence and multifunctionality will lead to a rethinking of those techniques. This article starts with an overview of orthogonal, physical-layer multicasting, space domain, power domain (PD), rate-splitting, code-domain MAs, MAs in other domains, and random access (RA), and highlights the importance of conducting research in universal MA (UMA) to shrink instead of grow the knowledge tree of MA schemes by providing a unified understanding of MA schemes across all resource dimensions. It then jumps into rethinking MA schemes in the era of wireless network intelligence, covering AI for MA such as AI-empowered resource allocation, optimization, channel estimation, and receiver designs, for different MA schemes, and MA for AI such as federated learning (FL)/edge intelligence and over-the-air computation (AirComp). We then discuss MA for network multifunctionality and the interplay between MA and integrated sensing, localization, and communications, covering MA for joint sensing and communications, multimodal sensing-aided communications, multimodal sensing and digital twin-assisted communications, and communication-aided sensing/localization systems. We finish with studying MA for emerging intelligent applications such as semantic communications (SeComs), virtual reality (VR), and smart radio and reconfigurable intelligent surfaces (RISs), before presenting a roadmap toward 6G standardization. Throughout the text, we also point out numerous directions that are promising for future research.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"112 7","pages":"832-879"},"PeriodicalIF":23.2,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141945616","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}
Intelligent surfaces (ISs) have emerged as a key technology to empower a wide range of appealing applications for wireless networks, due to their low cost, high energy efficiency, flexibility of deployment, and capability of constructing favorable wireless channels/radio environments. Moreover, the recent advent of several new IS architectures further expanded their electromagnetic functionalities from passive reflection to active amplification, simultaneous reflection, and refraction, as well as holographic beamforming. However, the research on ISs is still in rapid progress and there have been recent technological advances in ISs and their emerging applications that are worthy of a timely review. Thus, in this article, we provide a comprehensive survey on the recent development and advances of ISs-aided wireless networks. Specifically, we start with an overview on the anticipated use cases of ISs in future wireless networks such as 6G, followed by a summary of the recent standardization activities related to ISs. Then, the main design issues of the commonly adopted reflection-based IS and their state-of-the-art solutions are presented in detail, including reflection optimization, deployment, signal modulation, wireless sensing, and integrated sensing and communications. Finally, recent progress and new challenges in advanced IS architectures are discussed to inspire future research.
智能表面（ISs）因其低成本、高能效、部署灵活以及能够构建有利的无线信道/无线电环境，已成为无线网络广泛应用的关键技术。此外，最近出现的几种新型 IS 架构进一步扩展了其电磁功能，从被动反射扩展到主动放大、同步反射和折射以及全息波束成形。然而，对 IS 的研究仍在快速进行中，IS 的最新技术进展及其新兴应用值得及时回顾。因此，在本文中，我们将对 ISs 辅助无线网络的最新发展和进展进行全面考察。具体来说，我们首先概述了 ISs 在未来无线网络（如 6G）中的预期用例，然后总结了近期与 ISs 相关的标准化活动。然后，详细介绍通常采用的基于反射的 IS 的主要设计问题及其最新解决方案，包括反射优化、部署、信号调制、无线传感以及集成传感和通信。最后，讨论了先进 IS 架构的最新进展和新挑战，以启发未来的研究。
{"title":"Intelligent Surfaces Empowered Wireless Network: Recent Advances and the Road to 6G","authors":"Qingqing Wu;Beixiong Zheng;Changsheng You;Lipeng Zhu;Kaiming Shen;Xiaodan Shao;Weidong Mei;Boya Di;Hongliang Zhang;Ertugrul Basar;Lingyang Song;Marco Di Renzo;Zhi-Quan Luo;Rui Zhang","doi":"10.1109/JPROC.2024.3397910","DOIUrl":"10.1109/JPROC.2024.3397910","url":null,"abstract":"Intelligent surfaces (ISs) have emerged as a key technology to empower a wide range of appealing applications for wireless networks, due to their low cost, high energy efficiency, flexibility of deployment, and capability of constructing favorable wireless channels/radio environments. Moreover, the recent advent of several new IS architectures further expanded their electromagnetic functionalities from passive reflection to active amplification, simultaneous reflection, and refraction, as well as holographic beamforming. However, the research on ISs is still in rapid progress and there have been recent technological advances in ISs and their emerging applications that are worthy of a timely review. Thus, in this article, we provide a comprehensive survey on the recent development and advances of ISs-aided wireless networks. Specifically, we start with an overview on the anticipated use cases of ISs in future wireless networks such as 6G, followed by a summary of the recent standardization activities related to ISs. Then, the main design issues of the commonly adopted reflection-based IS and their state-of-the-art solutions are presented in detail, including reflection optimization, deployment, signal modulation, wireless sensing, and integrated sensing and communications. Finally, recent progress and new challenges in advanced IS architectures are discussed to inspire future research.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"112 7","pages":"724-763"},"PeriodicalIF":23.2,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141945618","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}
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