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Towards 6G wireless communication networks:vision, enabling technologies, and new paradigm shifts

Science China(Information Sciences) 2021年第1期64卷 5-78页

作者： Xiaohu YOU Cheng-Xiang WANG Jie HUANG Xiqi GAO Zaichen ZHANG Mao WANG Yongming HUANG Chuan ZHANG Yanxiang JIANG Jiaheng WANG Min ZHU Bin SHENG Dongming WANG Zhiwen PAN Pengcheng ZHU Yang YANG Zening LIU Ping ZHANG Xiaofeng TAO Shaoqian LI Zhi CHEN Xinying MA Chih-Lin I Shuangfeng HAN Ke LI Chengkang PAN Zhimin ZHENG Lajos HANZO Xuemin (Sherman) SHEN Yingjie Jay GUO Zhiguo DING Harald HAAS Wen TONG Peiying ZHU Ganghua YANG Jun WANG Erik G.LARSSON Hien Quoc NGO Wei HONG Haiming WANG Debin HOU Jixin CHEN Zhe CHEN Zhangcheng HAO Geoffrey Ye LI Rahim TAFAZOLLI Yue GAO H.Vincent POOR Gerhard P.FETTWEIS Ying-Chang LIANG National Mobile Communications Research Laboratory School of Information Science and EngineeringSoutheast University Purple Mountain Laboratories Shanghai Institute of Fog Computing Technology (SHIFT) ShanghaiTech University Research Center for Network Communication Peng Cheng Laboratory State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications National Engineering Laboratory for Mobile Network Technologies Beijing University of Posts and Telecommunications National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China (UESTC) China Mobile Research Institute School of Electronics and Computer Science University of Southampton Department of Electrical and Computer Engineering University of Waterloo Global Big Data Technologies Centre (GBDTC) University of Technology Sydney School of Electrical and Electronic Engineering The University of Manchester LiFi Research and Development Centre Institute for Digital CommunicationsSchool of EngineeringThe University of Edinburgh Huawei Technologies Canada Co. Ltd. Huawei Technologies Department of Electrical Engineering (ISY) Link?ping University Institute of Electronics Communications & Information Technology (ECIT)Queen's University Belfast State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast University School of Electrical and Computer Engineering Georgia Institute of Technology 5G Innovation Centre University of Surrey Princeton University Vodafone Chair Mobile Communications Systems Technische Universit?t Dresden Center for Intelligent Networking and Communications (CINC) University of Electronic Science and Technology of China (UESTC)

The fifth generation(5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability,and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation(6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle(UAV) communication networks, thus achieving a space-airground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6GHz, millimeter wave(mmWave), terahertz(THz),and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence(AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable f

关键词： 6G vision network architecture air interface and transmission technologies space-air-ground-sea integrated network all spectra artificial intelligence network security

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Risk-averse MPC via visual-inertial input and recurrent networks for online collision avoidance

arXiv

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arXiv 2020年

作者： Schperberg, Alexander Chen, Kenny Tsuei, Stephanie Jewett, Michael Hooks, Joshua Soatto, Stefano Mehta, Ankur Hong, Dennis The Robotics and Mechanisms Laboratory Department of Mechanical and Aerospace Engineering United States The Laboratory for Embedded Machines and Ubiquitous Robots Department of Electrical and Computer Engineering United States The UCLA Vision Lab Department of Computer Science United States The University of California Los Angeles Los AngelesCA90095 United States

In this paper, we propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties for safer navigation through cluttered environments. Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates through each step of our MPC's finite time horizon. The RNN model is trained on a dataset that comprises of robot and landmark poses generated from camera images and inertial measurement unit (IMU) readings via a state-of-the-art visual-inertial odometry framework. To detect and extract object locations for avoidance, we use a custom-trained convolutional neural network model in conjunction with a feature extractor to retrieve 3D centroid and radii boundaries of nearby obstacles. The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms, demonstrating autonomous behaviors that can plan fast and collision-free paths towards a goal point. Copyright © 2020, The Authors. All rights reserved.

关键词： Model predictive control

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Ethics of Foundation Models in Computational Pathology: Overview of Contemporary Issues and Future Implications

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Du, Rui Fei Carbonell, Eduard Lloret Huang, Jiaxuan Liu, Sheng Wang, Xiaohang Shen, Dinggang Ke, Jing Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China Dulwich College Shanghai Shanghai201111 China Tongji University School of Medicine Department of Thyroid and Breast Surgery Shanghai Fourth People’s Hospital Shanghai200434 China Shanghai Jiao Tong University School of Medicine Department of Radiation Oncology Ren Ji Hospital Shanghai200127 China ShanghaiTech University School of Biomedical Engineering State Key Laboratory of Advanced Medical Materials and Devices Shanghai201203 China Shanghai Clinical Research and Trial Center Shanghai200231 China Shanghai United Imaging Intelligence Co. Ltd Shanghai201807 China University of New South Wales School of Computer Science and Engineering SydneyNSW2052 Australia

Artificial intelligence (AI) has profoundly transformed our lives, reshaping industries and impacting nearly every aspect of society over the past few decades. It has recently become even more influential, primarily due to the rise of foundation models representing a new paradigm in AI development. These models, characterized by their large-scale training on vast datasets, have unique capabilities such as emergence and transference, enabling them to generalize across diverse tasks. Since their introduction, foundation models have been increasingly applied in fields such as autonomous driving, computer vision, marketing, finance, industrial robotics, and healthcare. Pathologists worldwide use computational methods to analyze diseases that profoundly impact human well-being, including cancer diagnosis and staging, genetic mutation prediction, and treatment and prognosis forecasting. In this article, we discuss how, despite the promise of foundation models in various applications, their development and application in computational pathology remain challenging due to inherent characteristics such as emergence, homogenization, hallucination, transference, compositionality, and explainability. While powerful, these traits introduce numerous ethical concerns and challenges, impacting safety and reliability, patient privacy, accountability, and equity and fairness in healthcare access. We examine these ethical issues, focusing on key concerns like algorithmic discrimination and misuse, accuracy, privacy breaches, transparency, public accessibility, and accountability. Furthermore, potential solutions to these challenges are analyzed, offering future perspectives on promoting the development and application of more ethical AI and foundation models in computational pathology. These insights aim to guide foundation models toward responsible integration of AI in healthcare. © 2025 Institute of electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Diseases

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Hybrid Reinforcement Learning-Based Eco-Driving Strategy for Connected and Automated Vehicles at Signalized Intersections

arXiv

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arXiv 2022年

作者： Bai, Zhengwei Hao, Peng Shangguan, Wei Cai, Baigen Barth, Matthew J. The Department of Electrical and Computer Engineering University of California at Riverside RiversideCA92507 United States The Center for Environmental Research and Technology University of California at Riverside RiversideCA92507 United States The State Key Laboratory of Rail Traffic Control and Safety The Beijing Engineering Research Center of EMC GNSS Technology for Rail Transportation School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China

Taking advantage of both vehicle-to-everything (V2X) communication and automated driving technology, connected and automated vehicles are quickly becoming one of the transformative solutions to many transportation problems. However, in a mixed traffic environment at signalized intersections, it is still a challenging task to improve overall throughput and energy efficiency considering the complexity and uncertainty in the traffic system. In this study, we proposed a hybrid reinforcement learning (HRL) framework which combines the rule-based strategy and the deep reinforcement learning (deep RL) to support connected eco-driving at signalized intersections in mixed traffic. vision-perceptive methods are integrated with vehicle-to-infrastructure (V2I) communications to achieve higher mobility and energy efficiency in mixed connected traffic. The HRL framework has three components: a rule-based driving manager that operates the collaboration between the rule-based policies and the RL policy;a multi-stream neural network that extracts the hidden features of vision and V2I information;and a deep RL-based policy network that generate both longitudinal and lateral eco-driving actions. In order to evaluate our approach, we developed a Unity-based simulator and designed a mixed-traffic intersection scenario. Moreover, several baselines were implemented to compare with our new design, and numerical experiments were conducted to test the performance of the HRL model. The experiments show that our HRL method can reduce energy consumption by 12.70% and save 11.75% travel time when compared with a state-of-the-art model-based Eco-Driving approach. © 2022, CC BY-NC-ND.

关键词： Reinforcement learning

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vision-driven NMPC for Autonomous Aerial Navigation in Subterranean Environments ⁎

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IFAC-PapersOnLine 2020年第2期53卷 9288-9294页

作者： Christoforos Kanellakis Petros Karvelis Sina Sharif Mansouri Ali-akbar Agha-mohammadi George Nikolakopoulos The authors are with the Robotics Team Department of Computer Electrical and Space Engineering Luleå University of Technology Luleå Sweden The author is with Jet Propulsion Laboratory California Institute of Technology Pasadena CA 91109

This work establishes a novel robocentric Non-linear Model Predictive Control (NMPC) framework for fast fully autonomous navigation of quadrotors in featureless dark tunnel environments. Additionally, this work leverages the processing of a single camera to generate direction commands along the tunnel axis, while regulating the platform’s altitude. The extracted visual dynamics are coupled in the sequel with the NMPC problem, structured around the Proximal Averaged Newton-type method for Optimal Control (PANOC), which is a fast numerical optimization method that is not sensitive to ill conditioning and is suitable for embedded NMPC implementations. Multiple fully realistic simulation results demonstrate the effectiveness of the proposed method in challenging environments.

关键词： Autonomous Vehicles Robot Navigation Non linear model predictive control MAV

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Compressive Sensing-Based Grant-Free Massive Access for 6G Massive Communication

arXiv

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arXiv 2023年

作者： Gao, Zhen Ke, Malong Mei, Yikun Qiao, Li Chen, Sheng Ng, Derrick Wing Kwan Poor, H. Vincent The MIIT Key Laboratory of Complex-Field Intelligent Sensing Beijing Institute of Technology Beijing100081 China Jiaxing314019 China The Advanced Technology Research Institute Beijing Institute of Technology Jinan250307 China The Wireless Product Division Ruijie Network Co. Ltd. Fuzhou350108 China The School of Information and Electronics Beijing Institute of Technology Beijing100081 China The School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom The School of Electrical Engineering and Telecommunications University of New South Wales Sydney2052 Australia The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08542 United States

The advent of the sixth-generation (6G) of wireless communications has given rise to the necessity to connect vast quantities of heterogeneous wireless devices, which requires advanced system capabilities far beyond existing network architectures. In particular, such massive communication has been recognized as a prime driver that can empower the 6G vision of future ubiquitous connectivity, supporting Internet of Human-Machine-Things for which massive access is critical. This paper surveys the most recent advances toward massive access in both academic and industry communities, focusing primarily on the promising compressive sensing-based grant-free massive access paradigm. We first specify the limitations of existing random access schemes and reveal that the practical implementation of massive communication relies on a dramatically different random access paradigm from the current ones mainly designed for human-centric communications. Then, a compressive sensing-based grant-free massive access roadmap is presented, where the evolutions from single-antenna to large-scale antenna array-based base stations, from single-station to cooperative massive multiple-input multiple-output systems, and from unsourced to sourced random access scenarios are detailed. Finally, we discuss the key challenges and open issues to shed light on the potential future research directions of grant-free massive access. Copyright © 2023, The Authors. All rights reserved.

关键词： Internet of things

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: Multi-Dimensional Spatial Reuse Enhancement for Directional Millimeter-Wave Wireless Networks

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IEEE Transactions on Mobile Computing 2021年第12期21卷 4439-4455页

作者： Yi Yang Anfu Zhou Dongzhu Xu Kun Liang Huadong Ma Teng Wei Jianhua Liu Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering University of Wisconsin-Madison Madison WI USA OPPO Mobile Communications Company Ltd. Research Institute Beijing China

Millimeter wave (mmWave) wireless networks are envisioned to bring a very high degree of spatial reuse, i.e., multiple links can operate simultaneously without interference. The vision, however, is becoming doubtful, as recent studies found that non-negligible interference exists due to imperfect beam patterns. In this paper, we extensively measure the spatial reuse issue in a dense 60 GHz mmWave network consisting of multiple access points (AP) and users. Our measurement quantifies the impact of interference on network performance and finds that the existing prediction based on interference-resolving approaches are insufficient. Motivated by the findings, we propose MDSR, which enhances the spatial reuse in 60 GHz mmWave networks. Instead of relying on interference prediction, MDSR takes a new measurement principle of building a conflict graph that implicitly takes into account the impact of both beam imperfection and reflections. Using the conflict graph, MDSR improves the spatial reuse from three dimensions: AP association, user scheduling, and beam selection, which can determine the optimal AP-user-beam combination and minimize interference in each scheduling cycle. We prototype and evaluate MDSR on the testbed using commodity mmWave radios. The evaluation results demonstrate that MDSR improves network throughput by multi-folds compared with the state-of-the-art one.

关键词： Interference Throughput Signal to noise ratio Millimeter wave measurements Switches Sensors Particle measurements

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A Reinforcement Learning Based Multiple Strategy Framework for Tracking a Moving Target

A Reinforcement Learning Based Multiple Strategy Framework f...

引用

IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Zixuan Huo Shilong Dai Mingxing Yuan Xiang Chen Xuebo Zhang Institute of Robotics and Automatic Information System Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin China Department of Electrical and Computer Engineering University of Windsor Ontario Canada

ISBN: (数字)9781728167947

ISBN: (纸本)9781728167954

The pursuit-evasion game has been a classic research topic in the field of mobile robotics. Reinforcement learning (RL), which shows outstanding advantages in the decision-making area, is a widely used method in pursuitevasion game. This paper proposes a hierarchical framework in which RL allows the pursuer to select an appropriate strategy for the current condition in the upper level from multiple underlying strategies in the lower level. Through the analysis of existing motion planning algorithms, the dynamic window approach (DWA) and the proportional guidance method (PG) are used as the lower level strategies of the framework. Individual discussions on the merits and limitations of the two motion planning algorithms indicate a possible complementarity between them. Simulations are carried out and the corresponding results validate the excellent performance of the proposed approach.

关键词： Games Robots Planning Heuristic algorithms Dynamics Target tracking Trajectory

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Propagating confidences through cNNs for sparse data regression 29

Propagating confidences through cNNs for sparse data regress...

引用

29th British Machine vision Conference, BMVC 2018

作者： Eldesokey, Abdelrahman Felsberg, Michael Khan, Fahad Shahbaz Computer Vision Laboratory Department of Electrical Engineering Linkoping University Linkoping Sweden Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates

In most computer vision applications, convolutional neural networks (CNNs) operate on dense image data generated by ordinary cameras. Designing CNNs for sparse and irregularly spaced input data is still an open problem with numerous applications in autonomous driving, robotics, and surveillance. To tackle this challenging problem, we introduce an algebraically-constrained convolution layer for CNNs with sparse input and demonstrate its capabilities for the scene depth completion task. We propose novel strategies for determining the confidence from the convolution operation and propagating it to consecutive layers. Furthermore, we propose an objective function that simultaneously minimizes the data error while maximizing the output confidence. Comprehensive experiments are performed on the KITTI depth benchmark and the results clearly demonstrate that the proposed approach achieves superior performance while requiring three times fewer parameters than the state-of-the-art methods. Moreover, our approach produces a continuous pixel-wise confidence map enabling information fusion, state inference, and decision support. © 2018. The copyright of this document resides with its authors.

关键词： Convolution

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Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge

arXiv

引用

arXiv 2025年

作者： Imran, Muhammad Krebs, Jonathan R. Sivaraman, Vishal Balaji Zhang, Teng Kumar, Amarjeet Ueland, Walker R. Fassler, Michael J. Huang, Jinlong Sun, Xiao Wang, Lisheng Shi, Pengcheng Rokuss, Maximilian Baumgartner, Michael Kirchhof, Yannick Maier-Hein, Klaus H. Isensee, Fabian Liu, Shuolin Han, Bing Nguyen, Bong Thanh Shin, Dong-Jin Ji-Woo, Park Choi, Mathew Uhm, Kwang-Hyun Ko, Sung-Jea Lee, Chanwoong Chun, Jaehee Kim, Jin Sung Zhang, Minghui Zhang, Hanxiao You, Xin Gu, Yun Pan, Zhaohong Liu, Xuan Liang, Xiaokun Tiefenthaler, Markus Almar-Munoz, Enrique Schwab, Matthias Kotyushev, Mikhail Epifanov, Rostislav Wodzinski, Marek Müller, Henning Qayyum, Abdul Mazher, Moona Niederer, Steven A. Wang, Zhiwei Yang, Kaixiang Ren, Jintao Korreman, Stine Sofia Gao, Yuchong Zeng, Hongye Zheng, Haoyu Zheng, Rui Yue, Jinghua Zhou, Fugen Liu, Bo Cosman, Alexander Liang, Muxuan Zhao, Chang Upchurch, Gilbert R. Ma, Jun Zhou, Yuyin Cooper, Michol A. Shao, Wei Department of Medicine University of Florida GainesvilleFL32611 United States Department of Surgery University of Florida GainesvilleFL32611 United States Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States Department of Computer Information Science and Engineering University of Florida GainesvilleFL32611 United States Shanghai Jiao Tong University Shanghai China Electronic & Information Engineering School Harbin Institute of Technology [Shenzhen Shenzhen China Division of Medical Image Computing German Cancer Research Center [DKFZ Heidelberg Germany CO. LTD Beijing China MedAI Seoul Korea Republic of Korea University Seoul Korea Republic of Department of Radiation Oncology Yonsei Cancer Center Heavy Ion Therapy Research Institute Yonsei University College of Medicine Seoul Korea Republic of Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Medical University of Innsbruck Innsbruck Austria Novosibirsk State University Novosibirsk Russia AGH University of Krakow Krakow Poland Sierre Switzerland University of Geneva Geneva Switzerland The Sense Innovation and Research Center Sion Switzerland National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Hubei Wuhan China Aarhus University Department of Clinical Medicine Nordre Palle Juul-Jensens Blvd. 11 Aarhus8200 Denmark Shanghaitech University Pudong Shanghai China Image Processing Center Beihang University Beijing China Department of Computer Science and Engineering University of California Santa CruzCA95064 United States Department of Biostatistics University of Florida GainesvilleFL32611 United States University Health Network ON Canada Agronomy Department University of F

Multi-class segmentation of the aorta in computed tomography angiography (CTA) scans is essential for diagnosing and planning complex endovascular treatments for patients with aortic dissections. However, existing methods reduce aortic segmentation to a binary problem, limiting their ability to measure diameters across different branches and zones. Furthermore, no open-source dataset is currently available to support the development of multi-class aortic segmentation methods. To address this gap, we organized the AortaSeg24 MICCAI Challenge, introducing the first dataset of 100 CTA volumes annotated for 23 clinically relevant aortic branches and zones. This dataset was designed to facilitate both model development and validation. The challenge attracted 121 teams worldwide, with participants leveraging state-of-the-art frameworks such as nnU-Net and exploring novel techniques, including cascaded models, data augmentation strategies, and custom loss functions. We evaluated the submitted algorithms using the Dice Similarity Coefficient (DSC) and Normalized Surface Distance (NSD), highlighting the approaches adopted by the top five performing teams. This paper presents the challenge design, dataset details, evaluation metrics, and an in-depth analysis of the top-performing algorithms. The annotated dataset, evaluation code, and implementations of the leading methods are publicly available to support further research. All resources can be accessed at https://***. © 2025, CC BY-NC-SA.

关键词： computerized tomography

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