IEEE Communications Surveys and Tutorials最新文献

{"title":"Reliability and Availability in Virtualized Networks: A Survey on Standards, Modeling Approaches, and Research Challenges","authors":"Mario Di Mauro;Walter Cerroni;Fabio Postiglione;Massimo Tornatore;Kishor S. Trivedi","doi":"10.1109/COMST.2026.3670039","DOIUrl":"10.1109/COMST.2026.3670039","url":null,"abstract":"Virtualized networks are built on the principle of replacing bulky and rather static hardware-based functions with software-based, virtualized instances of those functions, enabling more agile and cost-effective communication infrastructures. However, this shift brings new challenges for ensuring reliability and availability due to increased dependencies among system components introduced by virtualization technologies. Reliability, i.e., the ability of a system to perform regularly under specified conditions, and availability, i.e., the probability of a system of being ready to use, are critical requirements that must be guaranteed to maintain seamless network operations. Accurate modeling of these aspects is crucial for designing robust, fault-tolerant virtualized systems that can withstand service disruptions, ensuring continuous user access. Accordingly, this survey focuses on reliability and availability attributes of virtualized networks from a modeling perspective. We first introduce the Network Function Virtualization (NFV) architecture and relevant definitions, followed by a review of the European Telecommunications Standards Institute (ETSI) standardization efforts. We then explore key modeling formalisms and illustrate their use in characterizing failure and repair behaviors. A survey of related literature and supporting software tools is provided, along with a discussion on lessons learned and open research challenges to guide future work in designing fault-tolerant NFV systems.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"5121-5158"},"PeriodicalIF":34.4,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11418773","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147350568","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":"Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey","authors":"Jing Liu;Yao Du;Kun Yang;Jiaqi Wu;Yan Wang;Xiping Hu;Zehua Wang;Yang Liu;Peng Sun;Azzedine Boukerche;Victor C. M. Leung","doi":"10.1109/COMST.2026.3669216","DOIUrl":"10.1109/COMST.2026.3669216","url":null,"abstract":"Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications, integrating cloud resources with edge devices to enable efficient, low-latency processing across distributed communication networks. Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these networked systems, yet introduce significant challenges in model deployment, network resource management, and cross-layer optimization. In this survey, we comprehensively examine the intersection of distributed intelligence and model optimization within edge-cloud environments, providing a structured tutorial on fundamental architectures, communication protocols, and network-aware computing frameworks. Additionally, we systematically analyze model optimization approaches, including compression, adaptation, and neural architecture search, alongside AI-driven resource management strategies that balance performance, energy efficiency, and communication overhead across heterogeneous networks. We further explore critical aspects of privacy protection and security enhancement within ECCC systems and examine practical deployments through diverse networked applications, spanning autonomous driving, healthcare, and industrial automation. Performance analysis and benchmarking techniques are also thoroughly explored to establish evaluation standards for these complex distributed systems. Furthermore, the review identifies critical research directions including LLMs deployment, 6G integration, neuromorphic computing, and quantum computing, offering a roadmap for addressing persistent challenges in heterogeneity management, real-time processing, and scalability. By bridging theoretical advancements in communications with practical deployments, this survey offers researchers and practitioners a holistic perspective on leveraging AI to optimize distributed computing environments over next-generation communication networks, fostering innovation in intelligent networked systems.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"5049-5080"},"PeriodicalIF":34.4,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147350840","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":"Security and Privacy in O-RAN for 6G: A Comprehensive Review of Threats and Mitigation Approaches","authors":"Lujia Liang;Lei Zhang","doi":"10.1109/COMST.2026.3669743","DOIUrl":"10.1109/COMST.2026.3669743","url":null,"abstract":"Open Radio Access Network (O-RAN) is a major advancement in the telecommunications field, providing standardized interfaces that promote interoperability between different vendors’ technologies, thereby enhancing network flexibility and reducing operational expenses. By leveraging cutting-edge developments in network virtualization and artificial intelligence, O-RAN enhances operational efficiency and stimulates innovation within an open ecosystem. In the context of 6G, the potential capabilities of O-RAN have been significantly expanded, enabling ultra-reliable low-latency communication, terabit-level data rates, and seamless integration of terrestrial and non-terrestrial networks. Despite these benefits, its open architecture paradigm also brings critical security and privacy challenges, which, if not addressed, could compromise network integrity and data confidentiality. This paper conducts a comprehensive investigation into the security vulnerabilities and privacy issues associated with the O-RAN architecture in the context of the evolving 6G landscape, systematically categorizing fundamental vulnerabilities, meticulously examining potential attack vectors, and assessing current and future threats. In addition, this study also examines the existing and emerging security mechanisms of O-RAN and reviews the ongoing standardization activities aimed at strengthening the O-RAN security framework.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"5159-5195"},"PeriodicalIF":34.4,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147350839","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":"Integrated Radio Sensing Capabilities for 6G Networks: AI/ML Perspective","authors":"Victor Shatov;Steffen Schieler;Charlotte Muth;José Miguel Mateos-Ramos;Ivo Bizon;Florian Euchner;Sebastian Semper;Stephan ten Brink;Gerhard Fettweis;Christian Häger;Henk Wymeersch;Laurent Schmalen;Reiner S. Thomä;Norman Franchi","doi":"10.1109/COMST.2026.3668458","DOIUrl":"10.1109/COMST.2026.3668458","url":null,"abstract":"The sixth-generation wireless communications (6G) is often labeled as “connected intelligence”. Radio sensing, aligned with machine learning (ML) and artificial intelligence (AI), promises, among other benefits, breakthroughs in the system’s ability to perceive the environment and effectively utilize this awareness. This article offers a panoramic view of radio sensing by unifying physical object sensing and spectrum sensing. To this end, while staying in the framework of integrated sensing and communication (ISAC), we expand the term “sensing” from radar, via spectrum sensing, to miscellaneous applications of radio sensing like non-cooperative transmitter localization. We formulate the problems, explain the state-of-the-art approaches, and detail AI-based techniques to tackle various objectives in the context of wireless sensing. Finally, we discuss the potential integration of various radio sensing functions into a common AI-enhanced framework, emphasizing the possible benefits and the challenges to overcome. In addition to the tutorial-style core of this work based on direct authors’ involvement in 6G research problems, we review the related literature, and provide both a good start for those entering this field of research, and a topical overview for a general reader with a background in wireless communications.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"5081-5120"},"PeriodicalIF":34.4,"publicationDate":"2026-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147319625","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":"A Tutorial on AI-Empowered Integrated Sensing and Communications","authors":"Mojtaba Vaezi;Gayan Amarasuriya Aruma Baduge;Esa Ollila;Sergiy A. Vorobyov","doi":"10.1109/COMST.2026.3665143","DOIUrl":"10.1109/COMST.2026.3665143","url":null,"abstract":"Integrating sensing and communication (ISAC) can help overcome the challenges of limited spectrum and expensive hardware, leading to improved energy and cost efficiency. While full cooperation between sensing and communication can result in significant performance gains, achieving optimal performance requires efficient designs of unified waveforms and beamformers for joint sensing and communication. Sophisticated statistical signal processing and multi-objective optimization techniques are necessary to balance the competing design requirements of joint sensing and communication tasks. As model-based approaches can be suboptimal or too complex, deep learning offers a powerful data-driven alternative, especially when optimal algorithms are unknown or impractical for real-time use. Unified waveform and beamformer design problems for ISAC fall into this category, where fundamental design trade-offs exist between sensing and communication performance metrics, and the underlying models may be inadequate or incomplete. This tutorial paper explores the application of artificial intelligence (AI) to enhance efficiency or reduce complexity in ISAC designs. We emphasize the integration benefits through AI-driven ISAC designs, prioritizing the development of unified waveforms, constellations, and beamforming strategies for both sensing and communication. To illustrate the practical potential of AI-driven ISAC, we present three case studies on waveform, beamforming, and constellation design, demonstrating how unsupervised learning and neural network–based optimization can effectively balance performance, complexity, and implementation constraints.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"4980-5013"},"PeriodicalIF":34.4,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147279895","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":"From Data Mirror to Smart Copilot: A Survey on NextG Semantic Communication for Propelling Digital Twin World Into Cognitive Stage","authors":"Fang Zhu;Jiayuan Chen;Junjie Wen;Yuye Yang;Changyan Yi;Yun Tie;Peng Zhang;Jun Cai;Dusit Niyato;Mohsen Guizani","doi":"10.1109/COMST.2026.3665395","DOIUrl":"10.1109/COMST.2026.3665395","url":null,"abstract":"The revolution of information technologies is blurring the boundary between physical and virtual worlds, with digital twin (DT) at the forefront of this transformation. Meanwhile, DT is evolving from offering simple data mirrors to smart copilots, capable of understanding users’ intents and proactively providing profound insights. This ushers in the pivotal cognitive stage of the DT, where humans or embodied artificial intelligence agents (EAIs) can obtain cognitive perceptions within the DT world. Among various issues, in the cognitive stage, establishing strong interactions between the physical and DT worlds is critical. This paper thus explores the Next-Generation Semantic Communication (NextG-SemCom), well-suited for this issue, to fully enable the DT world with human-like cognitive capabilities. NextG-SemCom is envisioned as a cognitive-native paradigm that leverages Large AI Models (LAMs) as its core engine to perform intent understanding, contextual planning, and intent-oriented generative extraction. NextG-SemCom establishes a closed cognitive loop that shifts communication from data-driven to cognitive-driven, thereby not only reducing the traffic overhead, but also achieving cognitive comprehension of the transmitted information. This paper provides the first survey of the NextG-SemCom driven DT world, focusing on the advancement, trend and vision. We start by tracing the evolution of the DT from the initial virtual mapping stage to the current cognitive stage, and detail the distinguishing features of NextG-SemCom. We then present a holistic framework of cognitive interactions in the DT world, analyzing its core components, including the NextG-SemCom codec and network management, along with its design requirements and challenges. Furthermore, we discuss potential applications across a spectrum of human-human, human-EAI, and EAI-EAI interactive scenarios. Finally, we outline future research directions to provide a roadmap and inspire further studies in this promising field.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"4915-4947"},"PeriodicalIF":34.4,"publicationDate":"2026-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146204872","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":"Federated Learning for 6G Security: A Survey on Threats, Solutions, and Research Directions","authors":"Chamitha de Alwis;Ons Aouedi;Jiaming Xu;Shen Wang;Yushan Siriwardhana;Tharaka Hewa;Engin Zeydan;Chamara Sandeepa;Madhusanka Liyanage","doi":"10.1109/COMST.2026.3663434","DOIUrl":"10.1109/COMST.2026.3663434","url":null,"abstract":"The Sixth-Generation (6G) are already in the horizon, owing to advents of communication technologies towards enabling intelligent applications and services. Federated Learning (FL) is a distributed Artificial Intelligence (AI) technology that underpins 6G communication technologies and applications. Interestingly, FL is also a promising contender to enhance 6G security. This paper presents a comprehensive and up-to-date review of FL-enabled 6G security. The paper explores security threats in FL for 6G, threats in FL for 6G, and threats shared across FL and 6G. Subsequently, how FL can be utilized to strengthen 6G security in the Radio Access Network (RAN), Open RAN (O-RAN), network edge, and network orchestration and core is presented. In addition, FL is for 6G application and service security across various emerging applications, ranging from Connected Autonomous Vehicles (CAVs) to the envisaged metaverse applications. The paper then consolidates lessons learned, projects, and proposes future research directions to establish the role of FL in strengthening 6G security.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"4883-4914"},"PeriodicalIF":34.4,"publicationDate":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11389802","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146161386","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":"A Survey on Reconfigurable and Movable Antennas for Wireless Communications and Sensing","authors":"Wenyan Ma;Lipeng Zhu;Yanhua Tan;Beixiong Zheng;Yujie Zhang;Yuchen Zhang;Keke Ying;Zhen Gao;He Sun;Xiaodan Shao;Zhenyu Xiao;Dusit Niyato;Rui Zhang","doi":"10.1109/COMST.2026.3663021","DOIUrl":"10.1109/COMST.2026.3663021","url":null,"abstract":"Reconfigurable antennas (RAs) and movable antennas (MAs) have been recognized as promising technologies to enhance the performance of wireless communication and sensing systems by introducing additional degrees of freedom (DoFs) in tuning antenna radiation and/or placement. This paradigm shift from conventional non-reconfigurable/movable antennas offers tremendous new opportunities for realizing multi-functional, more adaptive, and efficient next-generation wireless networks. In this paper, we provide a comprehensive survey on the fundamentals, architectures, and applications of these two emerging antenna technologies. First, we provide a chronological overview of the parallel historical development of both RA and MA technologies. Next, we review and classify the state-of-the-art hardware architectures for implementing RAs and MAs, followed by a detailed comparison of their distinct mechanisms, performance metrics, and functionalities. Subsequently, we focus on various applications of RAs and MAs in wireless communication systems, analyzing their respective performance advantages and key design considerations such as mode selection, movement optimization, and channel acquisition. We also explore the significant roles of RAs and MAs in advancing wireless sensing and integrated sensing and communication (ISAC). Furthermore, we present numerical performance comparisons to illustrate the distinct characteristics and complementary advantages of RA and MA systems. Finally, we outline key challenges and identify promising future research directions to inspire further innovations in this burgeoning field.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"4842-4882"},"PeriodicalIF":34.4,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146161316","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":"Multi-Satellite Cooperative Communications for 6G: Fundamentals, System Design, and Applications","authors":"Bodong Shang;Xinyi Huang;Huayuan Wang;Xiangyu Li;Meixia Tao;Haijun Zhang;Pingzhi Fan","doi":"10.1109/COMST.2026.3660335","DOIUrl":"10.1109/COMST.2026.3660335","url":null,"abstract":"Recent advances in satellite technologies and non-terrestrial networks have made multi-satellite cooperative communications a promising approach for enhancing quality of service in satellite networks. To meet the ever-increasing demands for global coverage, ultra-reliable connectivity, and low-latency services, cooperation among multiple satellites is a key enabler for overcoming the limitations of single-satellite service systems. With its growing importance, both applications and opportunities are simultaneously manifesting. This paper presents a comprehensive survey of research on multi-satellite cooperative communications. In particular, we first introduce architectures for multi-satellite cooperative networks, including intra-layer, inter-layer, and cross-layer architectures. Subsequently, we comprehensively analyze and discuss their operating modes, including cooperative relaying, coordinated joint transmission, cooperative reception, beamforming, and cooperative interference management. Additionally, optical inter-satellite links are being considered for cooperative communications to enhance satellite interoperability. We then introduce the challenges and corresponding solutions for implementing multi-satellite cooperative communications. Furthermore, we explore enabling technologies for multi-satellite networks, including multi-connectivity, cooperative satellite beam-hopping, multi-satellite beamforming, traffic offloading, and management of satellite topology and constellation graphs. To further enhance the evaluation of the multi-satellite network, we discuss performance metrics, analysis tools, optimization methods, and validation. Finally, we outline several compelling research directions for future multi-satellite cooperative communication networks.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"4690-4730"},"PeriodicalIF":34.4,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110361","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":"ML-Enabled Open RAN: A Comprehensive Survey of Architectures, Challenges, and Opportunities","authors":"Mira Chandra Kirana;Patatchona Keyela;Fatemeh Rostamian;Deemah H. Tashman;Soumaya Cherkaoui","doi":"10.1109/COMST.2026.3660649","DOIUrl":"10.1109/COMST.2026.3660649","url":null,"abstract":"As wireless communication systems become more advanced, Open Radio Access Networks (O-RAN) stand out as a notable framework that promotes interoperability and cost-effectiveness. An examination of the progression of RAN architectures, as well as O-RAN’s underlying principles, reveals the importance of machine learning (ML) in addressing various challenges, including spectrum management, resource allocation, and security. Hence, this survey provides a comprehensive overview of the integration of ML within O-RAN, highlighting its transformative potential in enhancing network performance and efficiency. This survey aims to describe the current status of ML applications in O-RAN while indicating possible directions for future research by analyzing existing literature. The findings aim to assist researchers and stakeholders in formulating optimal service strategies and advancing the understanding of intelligent wireless networks.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"4948-4979"},"PeriodicalIF":34.4,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110358","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}