IEEE Communications Surveys and Tutorials最新文献

{"title":"Intelligent Reflecting Surfaces for Integrated Sensing and Communications: From System Coexistence to Networked Mutualism","authors":"Qingqing Wu;Qiaoyan Peng;Ziheng Zhang;Xiaodan Shao;Yang Liu;Yifan Jiang;Yapeng Zhao;Yanze Zhu;Yilong Chen;Zixiang Ren;Jie Xu;Wen Chen;Rui Zhang","doi":"10.1109/COMST.2026.3686910","DOIUrl":"10.1109/COMST.2026.3686910","url":null,"abstract":"The rapid development of sixth-generation (6G) wireless networks requires seamless integration of communication and sensing to support ubiquitous intelligence and real-time, high-reliability applications. Integrated sensing and communication (ISAC) has emerged as a key solution for achieving this convergence, offering joint utilization of spectral, hardware, and computing resources. However, realizing high-performance ISAC remains challenging due to environmental line-of-sight (LoS) blockage, limited spatial resolution, and the inherent coverage asymmetry and resource coupling between sensing and communication. Intelligent reflecting surfaces (IRSs), featuring low-cost, energy-efficient, and programmable electromagnetic reconfiguration, provide a promising solution to overcome these limitations. This paper presents a comprehensive overview of IRS-aided wireless sensing and ISAC technologies, including IRS architectures, target detection and estimation techniques, beamforming designs, and performance metrics. It further explores IRS-enabled new opportunities for more efficient performance balancing, coexistence, and networking in ISAC systems, focuses on current design bottlenecks, and outlines future research directions. This paper aims to offer a unified design framework that guides the development of practical and scalable IRS-aided ISAC systems for the next-generation wireless network.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"6057-6100"},"PeriodicalIF":34.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147735738","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 Survey on Reconfigurable Intelligent Surface-Assisted Optical Wireless Communications","authors":"Qingqing Hu;Murat Uysal","doi":"10.1109/COMST.2026.3672534","DOIUrl":"10.1109/COMST.2026.3672534","url":null,"abstract":"Reconfigurable intelligent surfaces (RISs) have attracted significant interest in recent years, primarily due to their ability to dynamically manipulate the propagation characteristics of the electromagnetic environment. By intelligently adjusting the phase, amplitude, or polarization of incident signals, RISs can enhance signal coverage, mitigate interference, and improve link reliability, offering a low-cost and energy-efficient solution for next-generation wireless communication systems. While RIS technologies have been widely explored in radio frequency bands such as sub-6 GHz, millimeter wave (mmWave), and terahertz (THz), their application to the optical domain—encompassing free-space optical communication (FSOC) and visible light communication (VLC)—is a relatively recent development. Optical wireless communication (OWC) leverages the unlicensed optical spectrum to deliver high-capacity wireless links. However, FSOC systems exhibit significant vulnerability to line-of-sight (LoS) blockage. Although VLC systems are more resilient to non-line-of-sight (NLoS) conditions, they still experience performance degradation in shadowed environments. As an emerging class of optical communication devices, optical reconfigurable intelligent surfaces (ORISs) have the ability to control essential characteristics of optical beams. and offer a promising approach to enhance signal robustness and coverage by enabling dynamic beam control and path reconfiguration. In this paper, we provide a comprehensive overview of the state-of-the-art in ORISs, categorizing them into mirror-based, meta-surface-based, and simultaneous transmission and reflection (STAR)-based structures. We highlight the distinct characteristics of ORIS deployment in key OWC subset technologies, i.e., FSOC and VLC. For both, we review classical channel models and discuss their extension to ORIS-assisted scenarios, identifying open challenges in channel modeling. We emphasize the pivotal role of transceiver architecture—whether based on intensity modulation/direct detection (IM/DD) or coherent modulation/coherent detection (CM/CD)—as it dictates the exploitable signal dimensions. Accordingly, we classify existing studies based on transceiver type and analyze how ORISs can be leveraged to enhance system performance. This taxonomy provides insight into the capabilities and limitations of ORIS integration across different OWC configurations. Finally, we outline several promising research directions and identify key open challenges for future work.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"5196-5226"},"PeriodicalIF":34.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440141","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 Comprehensive Survey on Synthetic Network Traffic Generation","authors":"Nirhoshan Sivaroopan;Kaushitha Silva;Chamara Madarasingha;Thilini Dahanayaka;Guillaume Jourjon;Anura Jayasumana;Kanchana Thilakarathna","doi":"10.1109/COMST.2026.3684111","DOIUrl":"10.1109/COMST.2026.3684111","url":null,"abstract":"Synthetic network traffic generation has emerged as a promising alternative for various data-driven applications in the networking domain. It enables the creation of synthetic data that preserves real-world characteristics while addressing key challenges such as data scarcity, privacy concerns, and purity constraints associated with real data. In this survey, we provide a comprehensive review of synthetic network traffic generation approaches, covering essential aspects such as data types and generation models. With the rapid advancements in Artificial Intelligence (AI) and Machine Learning (ML), we focus particularly on deep learning (DL)-based techniques while also providing a detailed discussion of statistical methods and their extensions, including commercially available tools. We present a comprehensive comparison of generation approaches and provide an AI tool to apply this comparison for any network traffic generation papers. Furthermore, we highlight open challenges in this domain and discuss potential future directions for further research and development. This survey serves as a foundational resource for researchers and practitioners, offering a structured analysis of existing methods, challenges, and opportunities in synthetic network traffic generation.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"5949-5983"},"PeriodicalIF":34.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147695779","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":"Modeling and Operation of Underwater Intelligent Communication Systems: A Survey of Multi-Agent Reinforcement Learning-Based Approaches","authors":"Guangjie Han;Shengchao Zhu;Chuan Lin;Jinfang Jiang;Yun Hou","doi":"10.1109/COMST.2026.3689477","DOIUrl":"https://doi.org/10.1109/COMST.2026.3689477","url":null,"abstract":"With the continuous evolution of the 6G sea-land-air integrated communication paradigm, underwater intelligent communication systems have emerged as a prominent research focus in recent years. Due to harsh environmental conditions, limited communication bandwidth, high propagation delays, and severe energy constraints, the modelling and operation of underwater intelligent communication systems face significant challenges in achieving intelligence, coordination, and sustainability. To meet the above challenges, multi-agent reinforcement learning (MARL), characterized by its decentralized and autonomous decision-making capabilities, has been regarded as a promising framework for intelligent, distributed, and adaptive coordination in such environments. Nevertheless, there is still a lack of comprehensive surveys on using MARL to optimize underwater communication networks. Therefore, this survey provides a comprehensive overview of recent advancements in applying MARL techniques to optimize underwater communication networks and bridges this gap. Specifically, we review the fundamental components of MARL and explain why it is particularly well suited for optimizing underwater networks. Then, we review MARL-based underwater applications in both static communication systems and dynamic mobile networks, including routing strategies, network security, resource allocation, MAC layer optimization, cooperative multi-robot formation, and target tracking. Furthermore, we summarize algorithmic innovations tailored to underwater communication optimization, focusing on improvements in training efficiency, robustness, and scalability. Building on these analyses, we further analyze open challenges and outline future research directions for advancing MARL-enabled underwater communication networks.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"6101-6135"},"PeriodicalIF":34.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147828712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RIS for 6G THz Networks: Advances in Modeling, Hardware, Signal Processing, and Standardization","authors":"Rithwik Premanand;Narendra Vishwakarma;Justin Jose;Ranjan Singh;A. S. Madhukumar","doi":"10.1109/COMST.2026.3687119","DOIUrl":"10.1109/COMST.2026.3687119","url":null,"abstract":"The integration of reconfigurable intelligent surfaces (RIS) with terahertz (THz) communication is emerging as a transformative enabler for sixth-generation (6G) wireless networks, offering unprecedented capacity, spectrum utilization, and propagation control. This paper presents a comprehensive survey of RIS-assisted THz systems, with a focus on their design, performance, and integration challenges. A unified taxonomy is introduced, organizing the discussion into key domains: system architectures, channel modeling, hardware implementation, signal processing, and experimental validation. The paper consolidates diverse technological perspectives by examining metasurface hardware, wideband and near-field propagation modeling, and signal processing tailored to THz-specific impairments. It further reviews advanced methods for beamforming, phase error mitigation, and channel estimation under practical hardware constraints. Beyond performance optimization, the work explores deployment paradigms including indoor coverage, autonomous aerial vehicles (AAV)-based relays, and RIS integration in joint communication and sensing frameworks. A major contribution of this survey is the detailed mapping between RIS design strategies and 6G use cases, offering deployment guidelines and highlighting trade-offs across different tuning mechanisms, fabrication techniques, and control architectures. The paper also addresses open research challenges such as hardware scalability and real-time reconfigurability, ultimately serving as a foundational reference for researchers and engineers developing next-generation RIS-enabled THz communication systems.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"6018-6056"},"PeriodicalIF":34.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147735737","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":"Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Art","authors":"Dayu Fan;Rui Meng;Xiaodong Xu;Yiming Liu;Guoshun Nan;Chenyuan Feng;Shujun Han;Song Gao;Bingxuan Xu;Dusit Niyato;Tony Q. S. Quek;Ping Zhang","doi":"10.1109/COMST.2026.3671110","DOIUrl":"10.1109/COMST.2026.3671110","url":null,"abstract":"With the rapid development of Generative Artificial Intelligence (GAI) technology, Generative Diffusion Models (GDMs) have shown significant empowerment potential in the field of wireless networks due to advantages, such as noise resistance, training stability, controllability, and multimodal generation. Although there have been multiple studies focusing on GDMs for wireless networks, there is still a lack of comprehensive reviews on their technological evolution. Motivated by this, we systematically explore the application of GDMs in wireless networks. Firstly, we identify the core challenges of wireless networks and argue why GDMs are uniquely suited to address them. We then introduce the mathematical principles of GDMs and representative models. Furthermore, we organize our comprehensive review through a structured taxonomy that categorizes GDM-based schemes into the sensing, transmission, and Applications, complemented by a security plane. For each representative scheme, we analyze its innovative points, the role of GDMs, strengths, and weaknesses. Ultimately, we extract key challenges and provide potential solutions, with the aim of providing directional guidance for future research in this field.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"5632-5677"},"PeriodicalIF":34.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147371456","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 Survey on Wi-Fi Sensing Generalizability: Taxonomy, Techniques, Datasets, and Future Research Prospects","authors":"Fei Wang;Tingting Zhang;Wei Xi;Han Ding;Ge Wang;Di Zhang;Yuanhao Cui;Fan Liu;Jinsong Han;Jie Xu;Tony Xiao Han","doi":"10.1109/COMST.2026.3670854","DOIUrl":"10.1109/COMST.2026.3670854","url":null,"abstract":"Wi-Fi sensing has emerged as a powerful non-intrusive technology for recognizing human activities, monitoring vital signs, and enabling context-aware applications using commercial wireless devices. However, the performance of Wi-Fi sensing often degrades when applied to new users, devices, or environments due to significant domain shifts. To address this challenge, researchers have proposed a wide range of generalization techniques aimed at enhancing the robustness and adaptability of Wi-Fi sensing systems. In this survey, we provide a comprehensive and structured review of over 200 papers published since 2015, categorizing them according to the Wi-Fi sensing pipeline: experimental setup, signal preprocessing, feature learning, and model deployment. We analyze key techniques, including signal preprocessing, domain adaptation, meta-learning, metric learning, data augmentation, cross-modal alignment, federated learning, and continual learning. Furthermore, we summarize publicly available datasets across various tasks, such as activity recognition, user identification, indoor localization, and pose estimation, and provide insights into their domain diversity. We also discuss emerging trends and future directions, including large-scale pretraining, integration with multimodal foundation models, and continual deployment. To foster community collaboration, we introduce the Sensing Dataset Platform (SDP) (<uri>http://www.sdp8.org/</uri>) for sharing datasets and models. This survey aims to serve as a valuable reference and practical guide for researchers and practitioners dedicated to improving the generalizability of Wi-Fi sensing systems. Notably, while this paper focuses on Wi-Fi sensing, it is important to emphasize that the methodologies discussed in the feature learning and model deployment stages, e.g., domain alignment, metric learning, meta-learning, federated learning, and continual learning, are equally applicable to a broader range of wireless sensing generalization challenges, such as those encountered in millimeter radar-based human sensing. Given the rapid evolution of this field, we will continuously maintain and update relevant resources at <uri>https://github.com/aiotgroup/awesome-wireless-sensing-generalization</uri>","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"5227-5266"},"PeriodicalIF":34.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147371455","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":"Vehicular Wireless Positioning: A Survey","authors":"Sharief Saleh;Satyam Dwivedi;Russ Whiton;Peter Hammarberg;Musa Furkan Keskin;Julia Equi;Hui Chen;Florent Munier;Olof Eriksson;Fredrik Gunnarsson;Fredrik Tufvesson;Henk Wymeersch","doi":"10.1109/COMST.2026.3674599","DOIUrl":"10.1109/COMST.2026.3674599","url":null,"abstract":"The rapid advancement of connected and autonomous vehicles has driven a growing demand for precise and reliable positioning systems capable of operating in complex environments. Meeting these demands requires an integrated approach that combines multiple positioning technologies, including wireless-based systems, perception-based technologies, and motion-based sensors. This paper presents a comprehensive survey of wireless-based positioning for vehicular applications, with a focus on satellite-based positioning (such as global navigation satellite systems (GNSS) and low-Earth-orbit (LEO) satellites), cellular-based positioning (5G and beyond), and IEEE-based technologies (including Wi-Fi, ultrawideband (UWB), Bluetooth, and vehicle-to-vehicle (V2V) communications). First, the survey reviews a wide range of vehicular positioning use cases, outlining their specific performance requirements. Next, it explores the historical development, standardization, and evolution of each wireless positioning technology, providing an in-depth categorization of existing positioning solutions and algorithms, and identifying open challenges and contemporary trends. Finally, the paper examines sensor fusion techniques that integrate these wireless systems with onboard perception and motion sensors to enhance positioning accuracy and resilience in real-world conditions. This survey thus offers a holistic perspective on the historical foundations, current advancements, and future directions of wireless-based positioning for vehicular applications, addressing a critical gap in the literature.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"5792-5832"},"PeriodicalIF":34.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11435359","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147471530","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":"Advancing Multi-Robot Networks via MLLM-Driven Sensing, Communication, and Computation: A Comprehensive Survey","authors":"Hyun Jong Yang;Howon Lee;Kyuhong Shim;Jeongho Kwak;Hyunsoo Kim;Donghoon Kim;Khoa Anh Ngo;Sehyun Ryu;Jaehyun Choi;Youbin Kim;Chanjun Moon;Michael Ryoo;Byonghyo Shim","doi":"10.1109/COMST.2026.3683120","DOIUrl":"10.1109/COMST.2026.3683120","url":null,"abstract":"Imagine a near future where advanced humanoid robots, powered by multimodal large language models (MLLMs), effortlessly interpret real-time sensing data, not just from their own sensors, but also from neighboring drones, autonomous vehicles, or underwater vehicles. Such robots for the physical AI are already taking shape in laboratories, hinting at imminent deployment across industries to tackle tasks like warehouse logistics, manufacturing, factory assembly, precision agriculture, public-space assistance, and on-site medical or safety rescue. While a single robot can demonstrate impressive local autonomy, realistic missions demand holistic coordination among multiple agents, compelling them to share and jointly interpret vast streams of sensor information. In these high-stakes scenarios, communication is indispensable, since without robust links, each robot remains blind to the broader mission context and cannot leverage the combined intelligence offered by a collective MLLM. Yet transmitting comprehensive sensor data from dozens or hundreds of robots, each with its own bandwidth and latency constraints, can overwhelm networks. This challenge is exacerbated when a system-level orchestrator or cloud-based MLLM needs to fuse multimodal inputs to generate holistic decisions, like route planning, anomaly detection, or real-time adjustments to complex collaborative tasks. Crucially, these tasks are often initiated by high-level natural language instructions (e.g., “Search for the yellow bin”). This text-based intent serves as a powerful filter for resource optimization: by understanding the specific goal via MLLMs, the system can selectively activate only the relevant sensing modalities, dynamically allocate communication bandwidth, and determine the optimal computation placement. This “intent-to-resource” mapping capability is a fundamental motivation for the proposed unified architecture. Moreover, many real deployments require open-vocabulary perception and language-grounded action (e.g., recognizing previously unseen objects or infrastructure outside the robot’s field-of-view), which is difficult to achieve with closed-set on-device perception alone. Viewed this way, R2X is fundamentally an intent-to-resource orchestration problem: given a high-level language command and system context, the network must jointly optimize sensing, wireless communication, and computation so that task-level success is maximized under resource constraints. This survey examines how integrated sensing, communication, and computation design paves the way for effective multi-robot coordination under MLLM guidance. We begin by reviewing state-of-the-art sensing modalities (from bounding-box LiDAR to hyperspectral imaging) and their interplay with semantic or partial-compression techniques. Next, we analyze communication strategies, including low latency protocols, edge-based orchestration, and adaptive resource allocation, to ensure scalable and timely data delivery at sca","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"5833-5871"},"PeriodicalIF":34.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147684423","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":"Generative AI-Enabled Semantic Communication: State-of-the-Art, Applications, and the Way Ahead","authors":"Chengyang Liang;Dong Li","doi":"10.1109/COMST.2025.3649707","DOIUrl":"10.1109/COMST.2025.3649707","url":null,"abstract":"The rapid advancement of generative artificial intelligence (GenAI) has introduced novel opportunities for semantic communication (SemCom) systems. This survey offers a comprehensive overview of GenAI-enabled SemCom, connecting theoretical foundations with practical applications. Initially, we introduce the fundamental concepts of SemCom and explore how generative models augment traditional communication paradigms. The paper systematically reviews state-of-the-art methodologies, including variational autoencoders, generative adversarial networks, diffusion models, and other GenAI frameworks within SemCom contexts. We classify GenAI in SemCom based on its GenAI architecture, communication modality, and application tasks. Additionally, we present detailed case studies that demonstrate real-world applications in smart healthcare, intelligent transportation systems, and smart agriculture. These case studies exemplify how generative SemCom can fulfill semantic tasks while preserving the communication fidelity. Finally, we identify emerging research directions and discuss open challenges that merit further investigation. This survey constitutes a valuable resource for researchers and practitioners aiming to comprehend and implement GenAI techniques in next-generation communication systems.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"3976-4015"},"PeriodicalIF":34.4,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894909","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}