Pub Date : 2025-12-22DOI: 10.1109/COMST.2025.3647003
Hongyu Li;Matteo Nerini;Shanpu Shen;Bruno Clerckx
Written by its inventors, this first tutorial on Beyond-Diagonal Reconfigurable Intelligent Surfaces (BD-RISs) provides the readers with the basics and fundamental tools necessary to appreciate, understand, and contribute to this emerging and disruptive technology. Conventional (Diagonal) RISs (D-RISs) are characterized by a diagonal scattering matrix <inline-formula> <tex-math>$boldsymbol {Theta }$ </tex-math></inline-formula> (commonly denoted as phase shift matrix in the literature). Since a very small percentage of the entries of that matrix, namely only the phases of its diagonal entries (in its passive form), are tunable, the wave manipulation flexibility of D-RIS is extremely limited. In contrast, BD-RIS refers to a novel and general framework for RIS where its scattering matrix is not limited to be diagonal (hence, the “beyond-diagonal” terminology) and consequently, all entries of <inline-formula> <tex-math>$boldsymbol {Theta }$ </tex-math></inline-formula> can potentially help shaping waves for much higher manipulation flexibility. In its passive form, <inline-formula> <tex-math>$boldsymbol {Theta }$ </tex-math></inline-formula> satisfies the unitary property <inline-formula> <tex-math>$boldsymbol {Theta }^{textsf {H}}boldsymbol {Theta }=mathbf {I}$ </tex-math></inline-formula> (for energy conservation in lossless ideal surfaces) and be either symmetric <inline-formula> <tex-math>$boldsymbol {Theta }=boldsymbol {Theta }^{textsf {T}}$ </tex-math></inline-formula> or asymmetric <inline-formula> <tex-math>$boldsymbol {Theta }neq boldsymbol {Theta }^{textsf {T}}$ </tex-math></inline-formula> hence leading to reciprocal or non-reciprocal BD-RIS. Such scattering matrix properties correspondingly translate into novel passive (lossless) and reciprocal/non-reciprocal circuitry where each RIS element is not only connected to its own tunable impedance but also to other elements through reconfigurable components. This physically means that BD-RIS can artificially engineer and reconfigure coupling across elements of the surface thanks to inter-element reconfigurable components which allow waves absorbed by one element to flow through other elements. This offers an extra degree of freedom for reconfigurable surfaces that provides new opportunities and flexibility for manipulating, modulating, processing, and computing signals and waves in the analog domain. Consequently, BD-RIS opens the door to more general and versatile intelligent surfaces that subsumes existing RIS architectures as special cases. In this tutorial, we share all the secret sauce to model, design, and optimize BD-RIS and make BD-RIS transformative in many different applications. Topics discussed include physics-consistent and multi-port network-aided modeling; transmitting, reflecting, hybrid, and multi-sector mode analysis; reciprocal and non-reciprocal architecture designs and optimal performance-complexity Pareto frontier of BD-RIS; signal processing, optimization, and channel es
{"title":"A Tutorial on Beyond-Diagonal Reconfigurable Intelligent Surfaces: Modeling, Architectures, System Design and Optimization, and Applications","authors":"Hongyu Li;Matteo Nerini;Shanpu Shen;Bruno Clerckx","doi":"10.1109/COMST.2025.3647003","DOIUrl":"10.1109/COMST.2025.3647003","url":null,"abstract":"Written by its inventors, this first tutorial on Beyond-Diagonal Reconfigurable Intelligent Surfaces (BD-RISs) provides the readers with the basics and fundamental tools necessary to appreciate, understand, and contribute to this emerging and disruptive technology. Conventional (Diagonal) RISs (D-RISs) are characterized by a diagonal scattering matrix <inline-formula> <tex-math>$boldsymbol {Theta }$ </tex-math></inline-formula> (commonly denoted as phase shift matrix in the literature). Since a very small percentage of the entries of that matrix, namely only the phases of its diagonal entries (in its passive form), are tunable, the wave manipulation flexibility of D-RIS is extremely limited. In contrast, BD-RIS refers to a novel and general framework for RIS where its scattering matrix is not limited to be diagonal (hence, the “beyond-diagonal” terminology) and consequently, all entries of <inline-formula> <tex-math>$boldsymbol {Theta }$ </tex-math></inline-formula> can potentially help shaping waves for much higher manipulation flexibility. In its passive form, <inline-formula> <tex-math>$boldsymbol {Theta }$ </tex-math></inline-formula> satisfies the unitary property <inline-formula> <tex-math>$boldsymbol {Theta }^{textsf {H}}boldsymbol {Theta }=mathbf {I}$ </tex-math></inline-formula> (for energy conservation in lossless ideal surfaces) and be either symmetric <inline-formula> <tex-math>$boldsymbol {Theta }=boldsymbol {Theta }^{textsf {T}}$ </tex-math></inline-formula> or asymmetric <inline-formula> <tex-math>$boldsymbol {Theta }neq boldsymbol {Theta }^{textsf {T}}$ </tex-math></inline-formula> hence leading to reciprocal or non-reciprocal BD-RIS. Such scattering matrix properties correspondingly translate into novel passive (lossless) and reciprocal/non-reciprocal circuitry where each RIS element is not only connected to its own tunable impedance but also to other elements through reconfigurable components. This physically means that BD-RIS can artificially engineer and reconfigure coupling across elements of the surface thanks to inter-element reconfigurable components which allow waves absorbed by one element to flow through other elements. This offers an extra degree of freedom for reconfigurable surfaces that provides new opportunities and flexibility for manipulating, modulating, processing, and computing signals and waves in the analog domain. Consequently, BD-RIS opens the door to more general and versatile intelligent surfaces that subsumes existing RIS architectures as special cases. In this tutorial, we share all the secret sauce to model, design, and optimize BD-RIS and make BD-RIS transformative in many different applications. Topics discussed include physics-consistent and multi-port network-aided modeling; transmitting, reflecting, hybrid, and multi-sector mode analysis; reciprocal and non-reciprocal architecture designs and optimal performance-complexity Pareto frontier of BD-RIS; signal processing, optimization, and channel es","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"4086-4126"},"PeriodicalIF":34.4,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145807805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-22DOI: 10.1109/COMST.2025.3646700
Chenbo Hu;Ruichen Zhang;Bo Li;Xu Jiang;Nan Zhao;Marco Di Renzo;Dusit Niyato;Arumugam Nallanathan;George K. Karagiannidis
Space-air-ground integrated networks (SAGINs) face unprecedented security challenges due to their inherent characteristics, such as multidimensional heterogeneity and dynamic topologies. These characteristics fundamentally undermine conventional security methods and traditional artificial intelligence (AI)-driven solutions. Generative AI (GAI) is a transformative approach that can safeguard SAGIN security by synthesizing data, understanding semantics, and making autonomous decisions. This survey fills existing review gaps by examining GAI-empowered secure communications across SAGINs, with a focus on core models such as generative adversarial networks (GANs), variational autoencoders (VAEs), generative diffusion models (GDMs), and large language models (LLMs). First, we introduce secured SAGINs and highlight GAI’s advantages over traditional AI for SAGIN security defenses. Then, we explain how GAI mitigates failures of authenticity, breaches of confidentiality, tampering of integrity, and disruptions of availability across the physical, data link, and network layers of SAGINs. We present three step-by-step tutorials to discuss how to apply GAI to solve specific security problems across different layers and segments of SAGINs utilizing concrete methods, emphasizing its generative paradigm beyond traditional AI. Finally, we outline open issues and future research directions, including lightweight deployment, adversarial robustness, cross-domain governance, and heterogeneous compatibility, to provide major insights into GAI’s role in shaping next-generation SAGIN security.
{"title":"Generative AI-Empowered Secure Communications in Space–Air–Ground Integrated Networks: A Survey and Tutorial","authors":"Chenbo Hu;Ruichen Zhang;Bo Li;Xu Jiang;Nan Zhao;Marco Di Renzo;Dusit Niyato;Arumugam Nallanathan;George K. Karagiannidis","doi":"10.1109/COMST.2025.3646700","DOIUrl":"10.1109/COMST.2025.3646700","url":null,"abstract":"Space-air-ground integrated networks (SAGINs) face unprecedented security challenges due to their inherent characteristics, such as multidimensional heterogeneity and dynamic topologies. These characteristics fundamentally undermine conventional security methods and traditional artificial intelligence (AI)-driven solutions. Generative AI (GAI) is a transformative approach that can safeguard SAGIN security by synthesizing data, understanding semantics, and making autonomous decisions. This survey fills existing review gaps by examining GAI-empowered secure communications across SAGINs, with a focus on core models such as generative adversarial networks (GANs), variational autoencoders (VAEs), generative diffusion models (GDMs), and large language models (LLMs). First, we introduce secured SAGINs and highlight GAI’s advantages over traditional AI for SAGIN security defenses. Then, we explain how GAI mitigates failures of authenticity, breaches of confidentiality, tampering of integrity, and disruptions of availability across the physical, data link, and network layers of SAGINs. We present three step-by-step tutorials to discuss how to apply GAI to solve specific security problems across different layers and segments of SAGINs utilizing concrete methods, emphasizing its generative paradigm beyond traditional AI. Finally, we outline open issues and future research directions, including lightweight deployment, adversarial robustness, cross-domain governance, and heterogeneous compatibility, to provide major insights into GAI’s role in shaping next-generation SAGIN security.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"4156-4194"},"PeriodicalIF":34.4,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145807806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-19DOI: 10.1109/COMST.2025.3646200
Wangzhong Ning;Fengxiao Tang;Ming Zhao;Nei Kato
Although Wi-Fi 7 is able to achieve a maximum throughput of up to 30 Gbps, reliability shortfalls, high latency, and low signal transmission efficiency still exist in frontier domains such as the Industrial Internet, the Metaverse, and autonomous driving. To address these issues, the IEEE 802.11 working group initiated a new standard study, IEEE 802.11bn, also known as Ultra-High Reliability (UHR). This paper systematically surveys the UHR Task Group’s research progress on the Physical (PHY) and Medium Access Control (MAC) layers, introducing specific technical enhancements including Distributed Resource Unit (DRU), Modulation and Coding Scheme (MCS), $2times $ Low-Density Parity-Check ($2times $ LDPC), Cooperative Spatial Reuse (Co-SR), Cooperative Beamforming (Co-BF), Seamless Mobility Domain (SMD), High Priority Enhanced Distributed Channel Access (P-EDCA), Non-Primary Channel Access (NPCA), and Dynamic Subband Operation (DSO). Furthermore, through a survey of the IEEE 802.11 TIG Task Group and relevant literature, this paper also explores the potential use cases of AI/ML technology in the Wi-Fi standardization process. Finally, this paper proposes the challenges, open issues, and future directions for UHR standardization, contributing to the advancement of Wi-Fi.
{"title":"A Survey on IEEE 802.11bn Wi-Fi 8: Advantages of Ultra High Reliability for Next-Generation Wireless LANs","authors":"Wangzhong Ning;Fengxiao Tang;Ming Zhao;Nei Kato","doi":"10.1109/COMST.2025.3646200","DOIUrl":"10.1109/COMST.2025.3646200","url":null,"abstract":"Although Wi-Fi 7 is able to achieve a maximum throughput of up to 30 Gbps, reliability shortfalls, high latency, and low signal transmission efficiency still exist in frontier domains such as the Industrial Internet, the Metaverse, and autonomous driving. To address these issues, the IEEE 802.11 working group initiated a new standard study, IEEE 802.11bn, also known as Ultra-High Reliability (UHR). This paper systematically surveys the UHR Task Group’s research progress on the Physical (PHY) and Medium Access Control (MAC) layers, introducing specific technical enhancements including Distributed Resource Unit (DRU), Modulation and Coding Scheme (MCS), <inline-formula> <tex-math>$2times $ </tex-math></inline-formula>Low-Density Parity-Check (<inline-formula> <tex-math>$2times $ </tex-math></inline-formula>LDPC), Cooperative Spatial Reuse (Co-SR), Cooperative Beamforming (Co-BF), Seamless Mobility Domain (SMD), High Priority Enhanced Distributed Channel Access (P-EDCA), Non-Primary Channel Access (NPCA), and Dynamic Subband Operation (DSO). Furthermore, through a survey of the IEEE 802.11 TIG Task Group and relevant literature, this paper also explores the potential use cases of AI/ML technology in the Wi-Fi standardization process. Finally, this paper proposes the challenges, open issues, and future directions for UHR standardization, contributing to the advancement of Wi-Fi.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"4215-4247"},"PeriodicalIF":34.4,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-16DOI: 10.1109/COMST.2025.3645053
Junlong Wang;Qing Wang;Quan Tao;Xiaomei Fu
Underwater information networks (UINs) serve as an effective solution for exploring and utilizing ocean resources. Communication and sonar are the two fundamental functions of UINs. Traditional discrete design between sonar and communication increases the size, power consumption and cost of the system, and reduce the system compatibility. The integrated design of sonar and communication enables them to share hardware platforms and signal processing units, thus overcoming the above drawbacks, and has received extensive attention from both academia and industry. Although integrated sonar and communication (ISC) systems have attracted increasing attention in recent years, research in this area remains relatively limited compared with the decades of development in integrated radar and communication (IRC). Several key issues remain to be addressed in aspects such as channel modeling, interference management, waveform design, and receiver signal processing. This article presents an overview of state-of-the-art research for ISC systems. We analyzes the shortcomings and challenges of these research in light of the characteristics of underwater acoustic (UWA) channels Furthermore, this paper discusses open problems and highlights promising research directions that could guide the development of more robust and efficient ISC systems in future underwater applications.
{"title":"Integrated Sonar and Communication: A Survey","authors":"Junlong Wang;Qing Wang;Quan Tao;Xiaomei Fu","doi":"10.1109/COMST.2025.3645053","DOIUrl":"10.1109/COMST.2025.3645053","url":null,"abstract":"Underwater information networks (UINs) serve as an effective solution for exploring and utilizing ocean resources. Communication and sonar are the two fundamental functions of UINs. Traditional discrete design between sonar and communication increases the size, power consumption and cost of the system, and reduce the system compatibility. The integrated design of sonar and communication enables them to share hardware platforms and signal processing units, thus overcoming the above drawbacks, and has received extensive attention from both academia and industry. Although integrated sonar and communication (ISC) systems have attracted increasing attention in recent years, research in this area remains relatively limited compared with the decades of development in integrated radar and communication (IRC). Several key issues remain to be addressed in aspects such as channel modeling, interference management, waveform design, and receiver signal processing. This article presents an overview of state-of-the-art research for ISC systems. We analyzes the shortcomings and challenges of these research in light of the characteristics of underwater acoustic (UWA) channels Furthermore, this paper discusses open problems and highlights promising research directions that could guide the development of more robust and efficient ISC systems in future underwater applications.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"4127-4155"},"PeriodicalIF":34.4,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145770980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-15DOI: 10.1109/COMST.2025.3644750
Md Raihan Uddin;Shaba Shaon;Ratun Rahman;Dinh C. Nguyen;Octavia A. Dobre;Dusit Niyato
Quantum federated learning (QFL) is a combination of distributed quantum computing and federated machine learning, integrating the strengths of both to enable privacy-preserving decentralized learning with quantum-enhanced capabilities. It appears as a promising approach for addressing challenges in efficient and secure model training across distributed quantum systems. This paper presents a comprehensive survey on QFL, exploring its key concepts, fundamentals, applications, and emerging challenges in this rapidly developing field. Specifically, we begin with an introduction to the recent advancements of QFL, followed by discussion on its market opportunity and background knowledge. We then discuss the motivation behind the integration of quantum computing and federated learning, highlighting its working principle. Moreover, we review the fundamentals of QFL and its taxonomy. Particularly, we explore federation architecture, networking topology, communication schemes, optimization techniques, and security mechanisms within QFL frameworks. Furthermore, we investigate applications of QFL across several domains which include vehicular networks, healthcare networks, satellite networks, metaverse, and network security. Additionally, we analyze frameworks and platforms related to QFL, delving into its prototype implementations, and provide a detailed case study. Key insights and lessons learned from this review of QFL are also highlighted. We complete the survey by identifying current challenges and outlining potential avenues for future research in this rapidly advancing field.
{"title":"Quantum Federated Learning: A Comprehensive Survey","authors":"Md Raihan Uddin;Shaba Shaon;Ratun Rahman;Dinh C. Nguyen;Octavia A. Dobre;Dusit Niyato","doi":"10.1109/COMST.2025.3644750","DOIUrl":"10.1109/COMST.2025.3644750","url":null,"abstract":"Quantum federated learning (QFL) is a combination of distributed quantum computing and federated machine learning, integrating the strengths of both to enable privacy-preserving decentralized learning with quantum-enhanced capabilities. It appears as a promising approach for addressing challenges in efficient and secure model training across distributed quantum systems. This paper presents a comprehensive survey on QFL, exploring its key concepts, fundamentals, applications, and emerging challenges in this rapidly developing field. Specifically, we begin with an introduction to the recent advancements of QFL, followed by discussion on its market opportunity and background knowledge. We then discuss the motivation behind the integration of quantum computing and federated learning, highlighting its working principle. Moreover, we review the fundamentals of QFL and its taxonomy. Particularly, we explore federation architecture, networking topology, communication schemes, optimization techniques, and security mechanisms within QFL frameworks. Furthermore, we investigate applications of QFL across several domains which include vehicular networks, healthcare networks, satellite networks, metaverse, and network security. Additionally, we analyze frameworks and platforms related to QFL, delving into its prototype implementations, and provide a detailed case study. Key insights and lessons learned from this review of QFL are also highlighted. We complete the survey by identifying current challenges and outlining potential avenues for future research in this rapidly advancing field.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"3942-3975"},"PeriodicalIF":34.4,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-08DOI: 10.1109/COMST.2025.3641591
Yunting Xu;Jiacheng Wang;Ruichen Zhang;Changyuan Zhao;Dusit Niyato;Jiawen Kang;Zehui Xiong;Bo Qian;Haibo Zhou;Shiwen Mao;Abbas Jamalipour;Xuemin Shen;Dong In Kim
Mixture of Experts (MoE) has emerged as a promising paradigm for scaling model capacity while preserving computational efficiency, particularly in large-scale machine learning architectures such as large language models (LLMs). Recent advances in MoE have facilitated its adoption in wireless networks to address the increasing complexity and heterogeneity of modern communication systems. This paper presents a comprehensive survey of the MoE framework in wireless networks, highlighting its potential in optimizing resource efficiency, improving scalability, and enhancing adaptability across diverse network tasks. We first introduce the fundamental concepts of MoE, including various gating mechanisms and the integration with generative AI (GenAI) and reinforcement learning (RL). Subsequently, we discuss the extensive applications of MoE across critical wireless communication scenarios, such as vehicular networks, unmanned aerial vehicles (UAVs), satellite communications, heterogeneous networks, integrated sensing and communication (ISAC), and mobile edge networks. Furthermore, key applications in channel prediction, physical layer signal processing, radio resource management, network optimization, and security are thoroughly examined. Additionally, we present a detailed overview of open-source datasets that are widely used in MoE-based models to support diverse machine learning tasks. Finally, this survey identifies crucial future research directions for MoE, emphasizing the importance of advanced training techniques, resource-aware gating strategies, and deeper integration with emerging 6G technologies.
{"title":"Mixture of Experts for Decentralized Generative AI and Reinforcement Learning in Wireless Networks: A Comprehensive Survey","authors":"Yunting Xu;Jiacheng Wang;Ruichen Zhang;Changyuan Zhao;Dusit Niyato;Jiawen Kang;Zehui Xiong;Bo Qian;Haibo Zhou;Shiwen Mao;Abbas Jamalipour;Xuemin Shen;Dong In Kim","doi":"10.1109/COMST.2025.3641591","DOIUrl":"10.1109/COMST.2025.3641591","url":null,"abstract":"Mixture of Experts (MoE) has emerged as a promising paradigm for scaling model capacity while preserving computational efficiency, particularly in large-scale machine learning architectures such as large language models (LLMs). Recent advances in MoE have facilitated its adoption in wireless networks to address the increasing complexity and heterogeneity of modern communication systems. This paper presents a comprehensive survey of the MoE framework in wireless networks, highlighting its potential in optimizing resource efficiency, improving scalability, and enhancing adaptability across diverse network tasks. We first introduce the fundamental concepts of MoE, including various gating mechanisms and the integration with generative AI (GenAI) and reinforcement learning (RL). Subsequently, we discuss the extensive applications of MoE across critical wireless communication scenarios, such as vehicular networks, unmanned aerial vehicles (UAVs), satellite communications, heterogeneous networks, integrated sensing and communication (ISAC), and mobile edge networks. Furthermore, key applications in channel prediction, physical layer signal processing, radio resource management, network optimization, and security are thoroughly examined. Additionally, we present a detailed overview of open-source datasets that are widely used in MoE-based models to support diverse machine learning tasks. Finally, this survey identifies crucial future research directions for MoE, emphasizing the importance of advanced training techniques, resource-aware gating strategies, and deeper integration with emerging 6G technologies.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"4051-4085"},"PeriodicalIF":34.4,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145704097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-25DOI: 10.1109/COMST.2025.3636503
Lashmi Kondoth;Rajan Shankaran;Quan Z. Sheng;Endrowednes Kuantama;Wei Ni
As semiconductor Network-on-Chip (NoC) architectures continue to evolve, there is a growing need for adaptive routing strategies that dynamically respond to changing network conditions. This paper reviews and categorizes adaptive routing methods for 2D NoC architectures, focusing on data traffic management, latency, and energy efficiency in multi-core systems. We present a novel taxonomy, classifying strategies as reactive, proactive, or application-specific, and examine key aspects such as fault tolerance, power efficiency, and congestion management. By comparing existing approaches, we highlight their strengths and limitations, offering insights for future NoC design. We also propose future research directions, including integrating machine learning and developing 2.5D NoC-specific routing algorithms to further improve adaptability and performance.
{"title":"A Survey of Adaptive Routing Techniques in Two-Dimensional Network-on-Chip Architectures","authors":"Lashmi Kondoth;Rajan Shankaran;Quan Z. Sheng;Endrowednes Kuantama;Wei Ni","doi":"10.1109/COMST.2025.3636503","DOIUrl":"10.1109/COMST.2025.3636503","url":null,"abstract":"As semiconductor Network-on-Chip (NoC) architectures continue to evolve, there is a growing need for adaptive routing strategies that dynamically respond to changing network conditions. This paper reviews and categorizes adaptive routing methods for 2D NoC architectures, focusing on data traffic management, latency, and energy efficiency in multi-core systems. We present a novel taxonomy, classifying strategies as reactive, proactive, or application-specific, and examine key aspects such as fault tolerance, power efficiency, and congestion management. By comparing existing approaches, we highlight their strengths and limitations, offering insights for future NoC design. We also propose future research directions, including integrating machine learning and developing 2.5D NoC-specific routing algorithms to further improve adaptability and performance.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"3195-3234"},"PeriodicalIF":34.4,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145599510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-14DOI: 10.1109/COMST.2025.3632908
Annisa Anggun Puspitasari;Byung Moo Lee
The increasing demands for ultra-reliable low-latency communication (URLLC), high-capacity, connectivity, and mobility in next-generation wireless networks have driven the integration of unmanned aerial vehicles (UAVs) as flexible and adaptive communication nodes. UAV-assisted networks offer enhanced coverage, dynamic deployment, and cost-effective infrastructure for the sixth generation (6G) and beyond. However, optimizing UAV trajectory planning, resource allocation, and interference management is particularly challenging, especially in dynamic and large-scale environments. Classical reinforcement learning (RL) techniques, while effective in adaptive optimization, suffer from computational inefficiencies and slow convergence in high-dimensional state-action spaces. To address these constraints, quantum reinforcement learning (QRL) emerges as a novel paradigm that leverages quantum computing principles, such as superposition and entanglement, to enhance decision-making efficiency. This study provides a comprehensive overview of QRL algorithms and investigates their transformative potential in UAV-assisted wireless communication networks. We analyze UAV deployment strategies, network architectures, and topologies, highlighting their advantages as well as inherent challenges. Furthermore, we examine the limitations of classical RL algorithms and assess how QRL overcomes them while improving computational efficiency and exploration capabilities. In addition, we present a detailed review of existing QRL algorithms applicable to next-generation UAV-enhanced wireless networks, identifying key challenges that must be addressed for practical deployment. Finally, we identify open research challenges and offer future directions for integrating QRL into UAV-assisted communication, paving the way for scalable, intelligent, and high-performance wireless systems.
{"title":"Quantum Reinforcement Learning for UAV-Enhanced Next-Generation Wireless Communications","authors":"Annisa Anggun Puspitasari;Byung Moo Lee","doi":"10.1109/COMST.2025.3632908","DOIUrl":"10.1109/COMST.2025.3632908","url":null,"abstract":"The increasing demands for ultra-reliable low-latency communication (URLLC), high-capacity, connectivity, and mobility in next-generation wireless networks have driven the integration of unmanned aerial vehicles (UAVs) as flexible and adaptive communication nodes. UAV-assisted networks offer enhanced coverage, dynamic deployment, and cost-effective infrastructure for the sixth generation (6G) and beyond. However, optimizing UAV trajectory planning, resource allocation, and interference management is particularly challenging, especially in dynamic and large-scale environments. Classical reinforcement learning (RL) techniques, while effective in adaptive optimization, suffer from computational inefficiencies and slow convergence in high-dimensional state-action spaces. To address these constraints, quantum reinforcement learning (QRL) emerges as a novel paradigm that leverages quantum computing principles, such as superposition and entanglement, to enhance decision-making efficiency. This study provides a comprehensive overview of QRL algorithms and investigates their transformative potential in UAV-assisted wireless communication networks. We analyze UAV deployment strategies, network architectures, and topologies, highlighting their advantages as well as inherent challenges. Furthermore, we examine the limitations of classical RL algorithms and assess how QRL overcomes them while improving computational efficiency and exploration capabilities. In addition, we present a detailed review of existing QRL algorithms applicable to next-generation UAV-enhanced wireless networks, identifying key challenges that must be addressed for practical deployment. Finally, we identify open research challenges and offer future directions for integrating QRL into UAV-assisted communication, paving the way for scalable, intelligent, and high-performance wireless systems.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"3089-3124"},"PeriodicalIF":34.4,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145515733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-13DOI: 10.1109/COMST.2025.3632286
Muhammad Adil;Tie Qiu;Xiaobo Zhou;Danish Javeed;Zhenrui Cao;Dapeng Oliver Wu
The convergence of 5G and Time-Sensitive Networking (TSN) offers a powerful foundation for enabling ultra-reliable, low-latency, and deterministic communication across a wide spectrum of emerging applications. While prior surveys primarily concentrate on industrial automation, this work presents, to the best of our knowledge, the first comprehensive survey that systematically investigates the potential of integrated 5G-TSN in four critical domains: industrial automation, intelligent transportation systems, smart energy systems, and digital health. We begin by providing a detailed background and comparative analysis of 5G and TSN technologies, highlighting recent advancements and complementary capabilities. The paper then presents a deep dive into an integrated 5G-TSN system architecture, with particular focus on time synchronization, traffic scheduling, QoS mapping, and cross-domain resource coordination. Building on this technical foundation, we introduce a structured, application-specific analysis that maps the communication requirements, challenges, and domain-specific integration strategies to the corresponding enabling 5G-TSN mechanisms. Finally, we synthesize key technical challenges such as interoperability, end-to-end synchronization, heterogeneous QoS alignment, and unified security, and propose targeted research directions to support the practical, scalable deployment of integrated 5G-TSN for emerging applications.
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