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Editorial: Third Quarter 2023 IEEE Communications Surveys and Tutorials 编辑:第三季度2023 IEEE通信调查和教程
IF 35.6 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-08-22 DOI: 10.1109/COMST.2023.3301328
Dusit Niyato
I welcome you to the third issue of the IEEE Communications Surveys and Tutorials in 2023. This issue includes 18 papers covering different aspects of communication networks. In particular, these articles survey and tutor various issues in “Wireless Communications,” “Cyber Security,” “IoT and M2M,” “Internet Technologies,” “Network Virtualization,” and “Network and Service Management and Green Communications.” A brief account for each of these papers is given below.
我欢迎您参加2023年第三期IEEE通信调查和教程。本期包括18篇涉及通信网络不同方面的论文。特别是,这些文章调查并指导了《无线通信》、《网络安全》、《物联网与M2M》、《互联网技术》、《虚拟化》和《网络与服务管理与绿色通信》中的各种问题。下面简要介绍了每篇论文。
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引用次数: 0
Optimal Control and Communication Strategies in Multi-Energy Generation Grid 多能发电电网的最优控制与通信策略
IF 35.6 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-08-17 DOI: 10.1109/COMST.2023.3304982
Muhammad Waseem Khan;Guojie Li;Keyou Wang;Muhammad Numan;Linyun Xiong;Muhammad Azam Khan
Multi-energy generation grids (MEGGs) provide a promising solution for reliable operations of cooperative various distributed energy resources (DERs), supply environmentally friendly energy to remote/off-grid areas, and improve overall system performance in terms of efficiency, reliability, flexibility, and resiliency. However, with the penetration of grids and the presence of various DERs with unpredictable renewables-based power generation and intermittent power loads, the operational coordination and supervision tasks become more complex. The communication-based optimal distributed control approach plays a significant role in MEGGs for coordinating an assembly of spatially and heterogeneous DERs, which improves reliability, efficiency, scalability, robustness, and privacy-preserving compared with traditional centralized-based controls. Therefore, this article aims to study different grid architectures and provide a comprehensive survey of optimal control and communication strategies/systems (CCS) in MEGG. A well-organized and systematic discussion related to the topic has been provided and elaborated on: 1) energy production and distribution with various grid architectures and distributed generating units (DGUs) integration for sustainable power generation, importance of unit sizing and technologies selection, and their implementations and operations; 2) classification on numerous control architectures and techniques, their prominent features and impact on MEGG stability; 3) multiple advanced intelligent control strategies and their essential aspects and merits; 4) different promising communication networks and technologies with optimal communication protocols and standards along with their computational mechanism and potential operational objectives in MEGGs; 5) communication strategies features and reliability issues concerning data volume, data availability, data accuracy, data security and authentication, time synchronization, and the growth of countermeasures; and 6) finally, key research gaps are highlighted and some recommendations are provided for future research works to efficiently handle the MEGG control, security, and communication network requirements.
多能发电电网(MEGGs)为多种分布式能源(DERs)的合作可靠运行提供了一种有前景的解决方案,为偏远/离网地区提供环保能源,并在效率、可靠性、灵活性和弹性方面提高整个系统的性能。然而,随着电网的渗透和各种可再生能源发电不可预测和间歇性电力负荷的der的存在,运行协调和监管任务变得更加复杂。基于通信的最优分布式控制方法在megs中发挥了重要作用,可协调空间异构der的集合,与传统的基于集中式控制相比,提高了可靠性、效率、可扩展性、鲁棒性和隐私保护性。因此,本文旨在研究不同的网格体系结构,并提供MEGG中最优控制和通信策略/系统(CCS)的全面调查。与本主题相关的组织良好和系统的讨论已经提供并详细阐述了:1)能源生产和分配与各种电网架构和分布式发电机组(dgu)集成的可持续发电,机组规模和技术选择的重要性,以及它们的实施和操作;2)多种控制体系结构和技术的分类,它们的突出特点和对MEGG稳定性的影响;3)多种先进的智能控制策略及其基本方面和优点;4)具有最优通信协议和标准的不同有发展前景的通信网络和技术,以及它们的计算机制和潜在的megg操作目标;5)通信策略的特点和可靠性问题,涉及数据量、数据可用性、数据准确性、数据安全和认证、时间同步,以及对策的增长;6)最后指出了关键的研究空白,并为今后的研究工作提供了一些建议,以有效地处理MEGG控制、安全和通信网络的需求。
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引用次数: 0
Smart Substation Communications and Cybersecurity: A Comprehensive Survey 智能变电站通信与网络安全:综合调查
IF 35.6 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-08-15 DOI: 10.1109/COMST.2023.3305468
José Gaspar;Tiago Cruz;Chan-Tong Lam;Paulo Simões
Electrical grids generate, transport, distribute and deliver electrical power to consumers through a complex Critical Infrastructure which progressively shifted from an air-gaped to a connected architecture. Specifically, Smart Substations are important parts of Smart Grids, providing switching, transforming, monitoring, metering and protection functions to offer a safe, efficient and reliable distribution of electrical power to consumers. The evolution of electrical power grids was closely followed by the digitization of all its parts and improvements in communication and computing infrastructures, leading to an evolution towards digital smart substations with improved connectivity. However, connected smart substations are exposed to cyber threats which can result in blackouts and faults which may propagate in a chain reaction and damage electrical appliances connected across the electrical grid. This work organizes and offers a comprehensive review of architectural, communications and cybersecurity standards for smart substations, complemented by a threat landscape analysis and the presentation of a Defense-in-Depth strategy blueprint. Furthermore, this work examines several defense mechanisms documented in the literature, existing datasets, testbeds and evaluation methodologies, identifying the most relevant open issues which may guide and inspire future research work.
电网通过复杂的关键基础设施产生、运输、分配和向消费者提供电力,这些基础设施逐步从气隙结构转变为连接结构。具体来说,智能变电站是智能电网的重要组成部分,具有开关、变换、监控、计量和保护等功能,为用户提供安全、高效、可靠的电力分配。电网的发展紧随其后的是其所有部件的数字化以及通信和计算基础设施的改进,从而导致向数字智能变电站的发展,并改善了连接。然而,连接的智能变电站面临网络威胁,这可能导致停电和故障,这些故障可能在连锁反应中传播,并损坏连接在电网上的电器。这项工作组织并提供了智能变电站的架构、通信和网络安全标准的全面审查,并辅以威胁形势分析和纵深防御战略蓝图的介绍。此外,本工作考察了文献中记录的几种防御机制、现有数据集、测试平台和评估方法,确定了最相关的开放问题,这些问题可能指导和启发未来的研究工作。
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引用次数: 1
Blockchain Intelligence for Internet of Vehicles: Challenges and Solutions 面向车联网的区块链智能:挑战与解决方案
IF 35.6 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-08-15 DOI: 10.1109/COMST.2023.3305312
Xiaojie Wang;Hailin Zhu;Zhaolong Ning;Lei Guo;Yan Zhang
With the development of communication and networking technologies, the Internet of Vehicles (IoV) has become the foundation of smart transportation. The development of blockchain and Machine Learning (ML) has contributed to the pervasiveness of the IoV, and they can effectively address the current issues of decentralisation, cyber security and data privacy in the IoV. In this article, blockchain and ML in the IoV are both reviewed, and corresponding technologies to support blockchain intelligence in the IoV are summarized. Importantly, blockchain intelligence is proposed as a key to integrate blockchain and ML, combining the advantages of both to drive the rapid development of the IoV. We discuss general frameworks, issuses, requirements and advantages for the implementation of blockchain intelligence in the IoV. Driven by its advantages, we summarize solutions of blockchain intelligence in the IoV from four aspects, including reliable interaction, network security and data privacy, trustworthy environment and scalability. Finally, a summary of current unresolved issues and challenges of blockchain intelligence in the IoV is presented, which provides guidelines for the future development of the IoV.
随着通信和网络技术的发展,车联网(IoV)已成为智能交通的基础。区块链和机器学习(ML)的发展促进了车联网的普及,它们可以有效地解决当前车联网中的去中心化、网络安全和数据隐私问题。本文对车联网中的区块链和机器学习进行了综述,并总结了支持车联网中区块链智能的相应技术。重要的是,区块链智能被提出作为整合区块链和机器学习的关键,结合两者的优势,推动车联网的快速发展。我们讨论了在车联网中实现区块链智能的一般框架、问题、要求和优势。在其优势的驱动下,我们从可靠交互、网络安全和数据隐私、可信环境和可扩展性四个方面总结了区块链智能在车联网中的解决方案。最后,总结了当前车联网中区块链智能尚未解决的问题和挑战,为车联网的未来发展提供了指导。
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引用次数: 5
A Survey on Threat Hunting in Enterprise Networks 企业网络威胁搜索研究综述
IF 35.6 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-08-14 DOI: 10.1109/COMST.2023.3299519
Boubakr Nour;Makan Pourzandi;Mourad Debbabi
With the rapidly evolving technological landscape, the huge development of the Internet of Things, and the embracing of digital transformation, the world is witnessing an explosion in data generation and a rapid evolution of new applications that lead to new, wider, and more sophisticated threats that are complex and hard to be detected. Advanced persistence threats use continuous, clandestine, and sophisticated techniques to gain access to a system and remain hidden for a prolonged period of time, with potentially destructive consequences. Those stealthy attacks are often not detectable by advanced intrusion detection systems (e.g., LightBasin attack was detected in 2022 and has been active since 2016). Indeed, threat actors are able to quickly and intelligently alter their tactics to avoid being detected by security defense lines (e.g., prevention and detection mechanisms). In response to these evolving threats, organizations need to adopt new proactive defense approaches. Threat hunting is a proactive security line exercised to uncover stealthy attacks, malicious activities, and suspicious entities that could circumvent standard detection mechanisms. Additionally, threat hunting is an iterative approach to generate and revise threat hypotheses endeavoring to provide early attack detection in a proactive way. The proactiveness consists of testing and validating the initial hypothesis using various manual and automated tools/techniques with the objective of confirming/refuting the existence of an attack. This survey studies the threat hunting concept and provides a comprehensive review of the existing solutions for Enterprise networks. In particular, we provide a threat hunting taxonomy based on the used technique and a sub-classification based on the detailed approach. Furthermore, we discuss the existing standardization efforts. Finally, we provide a qualitative discussion on current advances and identify various research gaps and challenges that may be considered by the research community to design concrete and efficient threat hunting solutions.
随着快速发展的技术格局、物联网的巨大发展以及数字化转型的拥抱,世界正在见证数据生成的爆炸式增长和新应用的快速发展,这些新应用导致了新的、更广泛的、更复杂的威胁,这些威胁复杂且难以检测。高级持久性威胁使用连续的、秘密的和复杂的技术来获得对系统的访问,并在很长一段时间内保持隐藏,从而产生潜在的破坏性后果。这些隐形攻击通常无法被先进的入侵检测系统检测到(例如,LightBasin攻击于2022年被检测到,自2016年以来一直活跃)。事实上,威胁行为者能够快速而智能地改变他们的策略,以避免被安全防线(例如,预防和检测机制)检测到。为了应对这些不断变化的威胁,组织需要采用新的主动防御方法。威胁搜索是一种主动的安全措施,用于发现可能绕过标准检测机制的隐蔽攻击、恶意活动和可疑实体。此外,威胁搜索是一种迭代方法,用于生成和修改威胁假设,努力以主动的方式提供早期攻击检测。主动性包括使用各种手动和自动化工具/技术测试和验证初始假设,目的是确认/驳斥攻击的存在。本调查研究了威胁搜索的概念,并对现有的企业网络解决方案进行了全面的回顾。特别地,我们提供了基于所使用的技术的威胁狩猎分类和基于详细方法的子分类。此外,我们还讨论了现有的标准化工作。最后,我们对当前的进展进行了定性讨论,并确定了研究社区可能考虑的各种研究差距和挑战,以设计具体和有效的威胁狩猎解决方案。
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引用次数: 2
A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services 面向节能自动驾驶服务的近似边缘人工智能研究
IF 35.6 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-08-09 DOI: 10.1109/COMST.2023.3302474
Dewant Katare;Diego Perino;Jari Nurmi;Martijn Warnier;Marijn Janssen;Aaron Yi Ding
Autonomous driving services depends on active sensing from modules such as camera, LiDAR, radar, and communication units. Traditionally, these modules process the sensed data on high-performance computing units inside the vehicle, which can deploy intelligent algorithms and AI models. The sensors mentioned above can produce large volumes of data, potentially reaching up to 20 Terabytes. This data size is influenced by factors such as the duration of driving, the data rate, and the sensor specifications. Consequently, this substantial amount of data can lead to significant power consumption on the vehicle. Similarly, a substantial amount of data will be exchanged between infrastructure sensors and vehicles for collaborative vehicle applications or fully connected autonomous vehicles. This communication process generates an additional surge of energy consumption. Although the autonomous vehicle domain has seen advancements in sensory technologies, wireless communication, computing and AI/ML algorithms, the challenge still exists in how to apply and integrate these technology innovations to achieve energy efficiency. This survey reviews and compares the connected vehicular applications, vehicular communications, approximation and Edge AI techniques. The focus is on energy efficiency by covering newly proposed approximation and enabling frameworks. To the best of our knowledge, this survey is the first to review the latest approximate Edge AI frameworks and publicly available datasets in energy-efficient autonomous driving. The insights from this survey can benefit the collaborative driving service development on low-power and memory-constrained systems and the energy optimization of autonomous vehicles.
自动驾驶服务依赖于摄像头、激光雷达、雷达和通信单元等模块的主动传感。传统上,这些模块在车内的高性能计算单元上处理感知数据,这些计算单元可以部署智能算法和人工智能模型。上面提到的传感器可以产生大量数据,可能达到20tb。该数据大小受驾驶持续时间、数据速率和传感器规格等因素的影响。因此,大量的数据可能会导致车辆的大量功耗。同样,基础设施传感器和车辆之间将交换大量数据,用于协作车辆应用或完全连接的自动驾驶车辆。这种通信过程产生了额外的能源消耗激增。尽管自动驾驶汽车领域在传感技术、无线通信、计算和人工智能/机器学习算法方面取得了进步,但如何应用和整合这些技术创新以实现能源效率仍然存在挑战。本调查回顾并比较了联网车辆应用、车辆通信、近似和边缘人工智能技术。重点是通过涵盖新提出的近似和使能框架来提高能源效率。据我们所知,这项调查首次回顾了节能自动驾驶领域最新的近似边缘人工智能框架和公开可用的数据集。该调查的见解有助于低功耗和内存受限系统的协同驾驶服务开发,以及自动驾驶汽车的能源优化。
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引用次数: 0
Low Earth Orbit Satellite Security and Reliability: Issues, Solutions, and the Road Ahead 近地轨道卫星的安全性和可靠性:问题、解决方案和未来的道路
IF 35.6 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-08-04 DOI: 10.1109/COMST.2023.3296160
Pingyue Yue;Jianping An;Jiankang Zhang;Jia Ye;Gaofeng Pan;Shuai Wang;Pei Xiao;Lajos Hanzo
Low Earth Orbit (LEO) satellites undergo a period of rapid development driven by ever-increasing user demands, reduced costs, and technological progress. Since there is a lack of literature on the security and reliability issues of LEO Satellite Communication Systems (SCSs), we aim to fill this knowledge gap. Specifically, we critically appraise the inherent characteristics of LEO SCSs and elaborate on their security and reliability requirements. In light of this, we further discuss their vulnerabilities, including potential security attacks launched against them and reliability risks, followed by outlining the associated lessons learned. Subsequently, we discuss the corresponding security and reliability enhancement solutions, unveil a range of trade-offs, and summarize the lessons gleaned. Furthermore, we shed light on several promising future research directions for enhancing the security and reliability of LEO SCSs, such as integrated sensing and communication, computer vision aided communications, as well as challenges brought about by mega-constellation and commercialization. Finally, we summarize the lessons inferred and crystallize the take-away messages in our design guidelines.
在用户需求不断增长、成本降低和技术进步的推动下,近地轨道卫星经历了一段快速发展时期。由于缺乏关于低轨卫星通信系统(SCS)安全性和可靠性问题的文献,我们的目标是填补这一知识空白。具体而言,我们严格评估了LEO SCS的固有特性,并详细阐述了其安全性和可靠性要求。有鉴于此,我们进一步讨论了它们的漏洞,包括针对它们发起的潜在安全攻击和可靠性风险,然后概述了相关的经验教训。随后,我们讨论了相应的安全性和可靠性增强解决方案,揭示了一系列权衡,并总结了所吸取的教训。此外,我们还阐明了提高低轨卫星安全性和可靠性的几个有前景的未来研究方向,如集成传感和通信、计算机视觉辅助通信,以及超大星座和商业化带来的挑战。最后,我们总结了推断出的经验教训,并在我们的设计指南中明确了带走的信息。
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引用次数: 4
Traffic-Aware Reliable Scheduling in TSCH Networks for Industry 4.0: A Systematic Mapping Review 面向工业4.0的TSCH网络中基于流量感知的可靠调度:系统映射综述
IF 35.6 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-08-04 DOI: 10.1109/COMST.2023.3302157
Abdeldjalil Tabouche;Badis Djamaa;Mustapha Reda Senouci
Recently, mission-critical Industrial Internet of Things (IIoT) applications such as system automation, predictive maintenance, and anomaly detection have come into the spotlight of Industry 4.0 thanks to the promised benefits. The IEEE 802.15.4 Time-Slotted Channel Hopping (TSCH) mode, along with the IPv6 over TSCH (6TiSCH) initiative, are two key standards to accommodate the diverse traffic patterns, reliability, latency, and power efficiency needs of such IIoT applications. To manage the allocation of communication resources in TSCH networks, a Scheduling Function (SF) is implemented. Even though scheduling in the IIoT has been the subject of numerous reviews, the potential of taking traffic-awareness into account has not been fully investigated. Motivated by these facts, we classify and analyze, in this systematic mapping review, prominent SFs dealing with traffic-awareness in TSCH networks published between 2012 and 2022. As a result, we provide a multi-dimensional map to identify the current trends in traffic-aware TSCH scheduling and help assess how far a given proposal is supported or contradicted by the empirical evidence in the field. Consequently, we discuss some open challenges that require community attention and point out potential future research directions regarding the design, implementation, and evaluation of new traffic-aware SFs.
最近,关键任务的工业物联网(IIoT)应用,如系统自动化、预测性维护和异常检测,由于其承诺的好处,已经成为工业4.0的焦点。IEEE 802.15.4时隙信道跳变(TSCH)模式以及IPv6 over TSCH (6TiSCH)倡议是适应此类IIoT应用的各种流量模式、可靠性、延迟和能效需求的两个关键标准。为了管理TSCH网络中通信资源的分配,调度功能(Scheduling Function, SF)被实现。尽管工业物联网中的调度已经成为众多审查的主题,但考虑到交通意识的潜力尚未得到充分调查。基于这些事实,我们对2012年至2022年期间发表的处理TSCH网络交通意识的杰出sf进行了分类和分析。因此,我们提供了一个多维地图,以确定交通感知的TSCH调度的当前趋势,并帮助评估给定建议在多大程度上得到了该领域经验证据的支持或反对。因此,我们讨论了一些需要社区关注的开放挑战,并指出了未来潜在的研究方向,包括设计、实施和评估新的交通感知安全系统。
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引用次数: 0
Boosting TCP & QUIC Performance in mmWave, Terahertz, and Lightwave Wireless Networks: A Survey 在毫米波、太赫兹和光波无线网络中提高TCP和QUIC性能:调查
IF 35.6 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-08-03 DOI: 10.1109/COMST.2023.3301820
E. Khorov;A. Krasilov;M. Susloparov;L. Kong
Emerging wireless systems target to provide multi-Gbps data rates for each user, which can be achieved by utilizing ultra-wide channels available at mmWave, terahertz, and lightwave frequencies. In contrast to the traditional spectrum below 6 GHz, these high-frequency bands raise many issues, complicating their usage. For example, because of high signal attenuation and blockage by obstacles, the data rates in a high-frequency band may quickly vary by several orders of magnitude. This peculiarity is often considered a challenge for modern transport layer protocols, such as Transmission Control Protocol (TCP) or Quick UDP Internet Connections (QUIC). Their key component is the Congestion Control Algorithm (CCA), which tries to determine a data sending rate that maximizes throughput and avoids network congestion. Many recent studies show that the performance of the existing CCAs significantly degrades if mobile devices communicate with high-frequency bands and propose some solutions to address this problem. The goal of this survey is twofold. First, we classify the reasons for poor TCP & QUIC performance in high-frequency bands. Second, we comprehensively review the solutions already designed to solve these problems. In contrast to existing studies and reviews that mainly focus on the comparison of various CCAs, we consider solutions working at different layers of the protocol stack, i.e., from the transport layer down to the physical layer, as well as cross-layer solutions. Based on the analysis, we conclude the survey with recommendations on which solutions provide the highest gains in high-frequency bands.
新兴无线系统的目标是为每个用户提供多gbps的数据速率,这可以通过利用毫米波、太赫兹和光波频率的超宽信道来实现。与6ghz以下的传统频谱相比,这些高频频段带来了许多问题,使其使用复杂化。例如,由于高信号衰减和障碍物阻塞,高频波段的数据速率可能很快变化几个数量级。这种特性通常被认为是对现代传输层协议(如传输控制协议(TCP)或快速UDP Internet连接(QUIC))的挑战。它们的关键组件是拥塞控制算法(CCA),它试图确定最大吞吐量和避免网络拥塞的数据发送速率。最近的许多研究表明,如果移动设备与高频频段通信,现有cca的性能会显著下降,并提出了一些解决方案来解决这一问题。这项调查的目的有两个。首先,我们对高频频段TCP和QUIC性能差的原因进行了分类。其次,我们全面回顾为解决这些问题而设计的解决方案。现有的研究和评论主要集中在各种cca的比较上,与之相反,我们考虑了在协议栈的不同层工作的解决方案,即从传输层到物理层,以及跨层解决方案。在分析的基础上,我们总结了调查,并建议哪些解决方案在高频频段提供最高的增益。
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引用次数: 1
Machine Learning for Large-Scale Optimization in 6G Wireless Networks 6G无线网络大规模优化的机器学习
IF 35.6 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-08-01 DOI: 10.1109/COMST.2023.3300664
Yandong Shi;Lixiang Lian;Yuanming Shi;Zixin Wang;Yong Zhou;Liqun Fu;Lin Bai;Jun Zhang;Wei Zhang
The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from “connected things” to “connected intelligence”, featured by ultra high density, large-scale, dynamic heterogeneity, diversified functional requirements, and machine learning capabilities, which leads to a growing need for highly efficient intelligent algorithms. The classic optimization-based algorithms usually require highly precise mathematical model of data links and suffer from poor performance with high computational cost in realistic 6G applications. Based on domain knowledge (e.g., optimization models and theoretical tools), machine learning (ML) stands out as a promising and viable methodology for many complex large-scale optimization problems in 6G, due to its superior performance, computational efficiency, scalability, and generalizability. In this paper, we systematically review the most representative “learning to optimize” techniques in diverse domains of 6G wireless networks by identifying the inherent feature of the underlying optimization problem and investigating the specifically designed ML frameworks from the perspective of optimization. In particular, we will cover algorithm unrolling, learning to branch-and-bound, graph neural network for structured optimization, deep reinforcement learning for stochastic optimization, end-to-end learning for semantic optimization, as well as wireless federated learning for distributed optimization, which are capable of addressing challenging large-scale problems arising from a variety of crucial wireless applications. Through the in-depth discussion, we shed light on the excellent performance of ML-based optimization algorithms with respect to the classical methods, and provide insightful guidance to develop advanced ML techniques in 6G networks. Neural network design, theoretical tools of different ML methods, implementation issues, as well as challenges and future research directions are also discussed to support the practical use of the ML model in 6G wireless networks.
第六代(6G)无线系统将实现从“物联网”到“物联网智能”的范式转变,其特点是超高密度、大规模、动态异构、多样化的功能需求和机器学习能力,这导致对高效智能算法的需求日益增长。经典的基于优化的算法通常需要高度精确的数据链路数学模型,在实际的6G应用中存在性能差、计算成本高的问题。基于领域知识(例如,优化模型和理论工具),机器学习(ML)因其优越的性能、计算效率、可扩展性和通用性而成为6G中许多复杂的大规模优化问题的一种有前途和可行的方法。本文通过识别底层优化问题的内在特征,并从优化的角度研究专门设计的ML框架,系统地回顾了6G无线网络不同领域中最具代表性的“学习优化”技术。特别是,我们将涵盖算法展开,分支定界学习,结构化优化的图神经网络,随机优化的深度强化学习,语义优化的端到端学习,以及分布式优化的无线联邦学习,这些都能够解决各种关键无线应用中出现的具有挑战性的大规模问题。通过深入讨论,我们揭示了基于机器学习的优化算法相对于经典方法的卓越性能,并为在6G网络中开发先进的机器学习技术提供了有洞察力的指导。本文还讨论了神经网络设计、不同机器学习方法的理论工具、实现问题、挑战和未来的研究方向,以支持机器学习模型在6G无线网络中的实际应用。
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引用次数: 7
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