Pub Date : 2024-03-28DOI: 10.1109/COMST.2024.3382470
Asif Siddiqui;Bhaskar P. Rimal;Martin Reisslein;Yong Wang
Home networks increasingly support important networked applications with limited professional network administration support, while sophisticated attacks pose enormous security risks for networked applications. A Unified Threat Management (UTM) system strives to comprehensively protect a network by providing firewall, intrusion detection and prevention, as well as antibot protection in an integrated, easy-to-configure manner. Previous surveys have extensively covered the individual components of a UTM system, i.e., there is extensive literature on firewall surveys, intrusion detection and prevention surveys, and antibot protection surveys. Importantly, the previous surveys covered these protection services separately, without considering their integration (however, this integration is critical for comprehensive home network protection). In contrast, the present survey covers for the first time home network UTM systems, i.e., the integrated network security services provided by a UTM system for a home network. This UTM survey is organized according to the UTM components, i.e., we comprehensively survey the firewall methods, the intrusion detection and prevention methods, as well as the antibot protection methods that are suitable for a UTM system for a home network. Throughout, we view these methods from the perspective of integration into a UTM system with limited computational resources and limited network administration support. Our survey includes the protection capabilities, as well as the design and deployment aspects and software/hardware limitations of available off-the-shelf and open-source UTM systems. We find that effective integrated home network protection where the UTM system components synergistically support each other while operating with limited computational resources and network administration support still requires extensive future research and development.
{"title":"Survey on Unified Threat Management (UTM) Systems for Home Networks","authors":"Asif Siddiqui;Bhaskar P. Rimal;Martin Reisslein;Yong Wang","doi":"10.1109/COMST.2024.3382470","DOIUrl":"10.1109/COMST.2024.3382470","url":null,"abstract":"Home networks increasingly support important networked applications with limited professional network administration support, while sophisticated attacks pose enormous security risks for networked applications. A Unified Threat Management (UTM) system strives to comprehensively protect a network by providing firewall, intrusion detection and prevention, as well as antibot protection in an integrated, easy-to-configure manner. Previous surveys have extensively covered the individual components of a UTM system, i.e., there is extensive literature on firewall surveys, intrusion detection and prevention surveys, and antibot protection surveys. Importantly, the previous surveys covered these protection services separately, without considering their integration (however, this integration is critical for comprehensive home network protection). In contrast, the present survey covers for the first time home network UTM systems, i.e., the integrated network security services provided by a UTM system for a home network. This UTM survey is organized according to the UTM components, i.e., we comprehensively survey the firewall methods, the intrusion detection and prevention methods, as well as the antibot protection methods that are suitable for a UTM system for a home network. Throughout, we view these methods from the perspective of integration into a UTM system with limited computational resources and limited network administration support. Our survey includes the protection capabilities, as well as the design and deployment aspects and software/hardware limitations of available off-the-shelf and open-source UTM systems. We find that effective integrated home network protection where the UTM system components synergistically support each other while operating with limited computational resources and network administration support still requires extensive future research and development.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"26 4","pages":"2459-2509"},"PeriodicalIF":34.4,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140321893","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 : 2024-03-27DOI: 10.1109/COMST.2024.3381669
Klaus I. Pedersen;Roberto Maldonado;Guillermo Pocovi;Enric Juan;Mads Lauridsen;István Z. Kovács;Mads Brix;Jeroen Wigard
In this tutorial we present recipes for dynamic system-level simulations (SLSs) of 5G and beyond cellular radio systems. A key ingredient for such SLSs is selection of proper models to make sure that the performance determining effects are properly reflected to ensure output of realistic radio performance results. We therefore present a significant number of SLS models and related methodologies for a variety of use cases. Our focus is on generally accepted models that are largely supported by academia and industrial players and adopted by 3GPP as being realistic. Among others, we touch on deployment models, traffic models, non-terrestrial cellular networks with satellites, SLS methodologies for Machine Learning (ML) enabled air-interface solutions, and many more. We also present several recommendations for best practices related to preparing and running detailed SLS campaigns, and agile software engineering considerations. Throughout the article we use the 3GPP defined 5G and 5G-Advanced systems to illustrate our points, extending it also into the 6G-era that is predicted to build on alike SLS methodologies and best practices.
{"title":"A Tutorial on Radio System-Level Simulations With Emphasis on 3GPP 5G-Advanced and Beyond","authors":"Klaus I. Pedersen;Roberto Maldonado;Guillermo Pocovi;Enric Juan;Mads Lauridsen;István Z. Kovács;Mads Brix;Jeroen Wigard","doi":"10.1109/COMST.2024.3381669","DOIUrl":"10.1109/COMST.2024.3381669","url":null,"abstract":"In this tutorial we present recipes for dynamic system-level simulations (SLSs) of 5G and beyond cellular radio systems. A key ingredient for such SLSs is selection of proper models to make sure that the performance determining effects are properly reflected to ensure output of realistic radio performance results. We therefore present a significant number of SLS models and related methodologies for a variety of use cases. Our focus is on generally accepted models that are largely supported by academia and industrial players and adopted by 3GPP as being realistic. Among others, we touch on deployment models, traffic models, non-terrestrial cellular networks with satellites, SLS methodologies for Machine Learning (ML) enabled air-interface solutions, and many more. We also present several recommendations for best practices related to preparing and running detailed SLS campaigns, and agile software engineering considerations. Throughout the article we use the 3GPP defined 5G and 5G-Advanced systems to illustrate our points, extending it also into the 6G-era that is predicted to build on alike SLS methodologies and best practices.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"26 4","pages":"2290-2325"},"PeriodicalIF":34.4,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140310527","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 : 2024-03-25DOI: 10.1109/COMST.2024.3380901
Yan F. Coutinho;ândrei Camponogara;Mateus de L. Filomeno;Marcello L. R. de Campos;Andrea M. Tonello;Moisés V. Ribeiro
In this paper, we review the current state-of-the-art of resource allocation for power line communication systems. The study systematically presents the advancements achieved in this field over the past decades. To achieve this, we adopt two complementary perspectives: a power line communication-centric view and an optimization-centric view. The former highlights resource allocation within the eye of the power line communication community, while the latter emphasizes optimization principles, focusing on the technical aspects of resource allocation. Subsequently, we meticulously present the progress made in both single- and multi-user scenarios, employing a chronological approach to provide readers with a coherent narrative of the evolution and important findings. Furthermore, we accentuate critical insights and valuable lessons gathered from these developments. In conclusion, we explore future trends and prospects within the scope of resource allocation for power line communication systems, intending to inspire research initiatives that push the boundaries beyond the existing state-of-the-art.
{"title":"Two Decades of Research Progress in Resource Allocation for PLC Systems: From Core Concepts to Frontiers","authors":"Yan F. Coutinho;ândrei Camponogara;Mateus de L. Filomeno;Marcello L. R. de Campos;Andrea M. Tonello;Moisés V. Ribeiro","doi":"10.1109/COMST.2024.3380901","DOIUrl":"10.1109/COMST.2024.3380901","url":null,"abstract":"In this paper, we review the current state-of-the-art of resource allocation for power line communication systems. The study systematically presents the advancements achieved in this field over the past decades. To achieve this, we adopt two complementary perspectives: a power line communication-centric view and an optimization-centric view. The former highlights resource allocation within the eye of the power line communication community, while the latter emphasizes optimization principles, focusing on the technical aspects of resource allocation. Subsequently, we meticulously present the progress made in both single- and multi-user scenarios, employing a chronological approach to provide readers with a coherent narrative of the evolution and important findings. Furthermore, we accentuate critical insights and valuable lessons gathered from these developments. In conclusion, we explore future trends and prospects within the scope of resource allocation for power line communication systems, intending to inspire research initiatives that push the boundaries beyond the existing state-of-the-art.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"26 3","pages":"1710-1747"},"PeriodicalIF":34.4,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140291467","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 : 2024-03-22DOI: 10.1109/COMST.2024.3396689
Dusit Niyato
I welcome you to the second issue of the IEEE Communications Surveys and Tutorials in 2024. This issue includes 18 articles 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 “Optical Communications”. A brief account for each of these articles is given below.
{"title":"Editorial: Second Quarter 2024 IEEE Communications Surveys and Tutorials","authors":"Dusit Niyato","doi":"10.1109/COMST.2024.3396689","DOIUrl":"https://doi.org/10.1109/COMST.2024.3396689","url":null,"abstract":"I welcome you to the second issue of the IEEE Communications Surveys and Tutorials in 2024. This issue includes 18 articles 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 “Optical Communications”. A brief account for each of these articles is given below.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"26 2","pages":"i-vi"},"PeriodicalIF":35.6,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10536696","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141084863","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}
Pub Date : 2024-03-21DOI: 10.1109/COMST.2024.3403873
Sarah Karmous;Nadia Adem;Mohammed Atiquzzaman;Sumudu Samarakoon
With a large number of deep space (DS) missions anticipated by the end of this decade, reliable and high-capacity DS communications are needed more than ever. Nevertheless, existing technologies are far from meeting such a goal. Improving current systems does not only require engineering leadership, but also, very crucially, investigating potential technologies that overcome the unique challenges of ultra-long DS links. To the best of our knowledge, there has not been any comprehensive surveys of DS communications technologies over the last decade. Free space optical (FSO) is an emerging DS technology, proven to acquire lower communications systems size weight and power (SWaP) and achieve a very high capacity compared to its counterpart radio frequency (RF), the currently used DS technology. In this survey, we discuss the pros and cons of deep space optical communications (DSOC) and review their physical and networking characteristics. Furthermore, we provide, for the first time, thoughtful discussions about implementing orbital angular momentum (OAM) and quantum communications (QC) for DS. We elaborate on how these technologies among other field advances including interplanetary network (IPN) and RF/FSO systems improve reliability, capacity, and security. This paper provides a holistic survey of DSOC technologies gathering 247 fragmented pieces of literature and including novel perspectives aiming to set the stage for more developments in the field.
{"title":"How Can Optical Communications Shape the Future of Deep Space Communications? A Survey","authors":"Sarah Karmous;Nadia Adem;Mohammed Atiquzzaman;Sumudu Samarakoon","doi":"10.1109/COMST.2024.3403873","DOIUrl":"10.1109/COMST.2024.3403873","url":null,"abstract":"With a large number of deep space (DS) missions anticipated by the end of this decade, reliable and high-capacity DS communications are needed more than ever. Nevertheless, existing technologies are far from meeting such a goal. Improving current systems does not only require engineering leadership, but also, very crucially, investigating potential technologies that overcome the unique challenges of ultra-long DS links. To the best of our knowledge, there has not been any comprehensive surveys of DS communications technologies over the last decade. Free space optical (FSO) is an emerging DS technology, proven to acquire lower communications systems size weight and power (SWaP) and achieve a very high capacity compared to its counterpart radio frequency (RF), the currently used DS technology. In this survey, we discuss the pros and cons of deep space optical communications (DSOC) and review their physical and networking characteristics. Furthermore, we provide, for the first time, thoughtful discussions about implementing orbital angular momentum (OAM) and quantum communications (QC) for DS. We elaborate on how these technologies among other field advances including interplanetary network (IPN) and RF/FSO systems improve reliability, capacity, and security. This paper provides a holistic survey of DSOC technologies gathering 247 fragmented pieces of literature and including novel perspectives aiming to set the stage for more developments in the field.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"27 1","pages":"725-747"},"PeriodicalIF":34.4,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141079062","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 : 2024-03-18DOI: 10.1109/COMST.2024.3376252
Gabriel Antonio F. Rebello;Gustavo F. Camilo;Lucas Airam C. de Souza;Maria Potop-Butucaru;Marcelo Dias de Amorim;Miguel Elias M. Campista;Luís Henrique M. K. Costa
Despite the great success of blockchain systems in recent years, blockchains still struggle to provide the same level of latency and throughput as centralized financial systems. The core of this problem lies in the inefficiency of consensus protocols. In this paper, we provide a survey on recent efforts to improve the scalability of blockchains. We focus on layer-two protocols, such as payment channel networks and transaction rollups, which process computations off-chain and only use consensus for dispute resolution. Layer-two protocols are expected to process microtransactions with sub-second latency and reduced fees, allowing blockchains to scale. Much of this work addresses the open challenges of payment channel networks, such as payment routing, channel rebalancing, network design strategies, security and privacy, payment scheduling, congestion control, simulators, and support for light nodes. We also dedicate a section to the existing implementations of smart-contract-based transaction rollups. Our work systematizes the state-of-the-art layer-two protocols, paving the way for future advances.
{"title":"A Survey on Blockchain Scalability: From Hardware to Layer-Two Protocols","authors":"Gabriel Antonio F. Rebello;Gustavo F. Camilo;Lucas Airam C. de Souza;Maria Potop-Butucaru;Marcelo Dias de Amorim;Miguel Elias M. Campista;Luís Henrique M. K. Costa","doi":"10.1109/COMST.2024.3376252","DOIUrl":"10.1109/COMST.2024.3376252","url":null,"abstract":"Despite the great success of blockchain systems in recent years, blockchains still struggle to provide the same level of latency and throughput as centralized financial systems. The core of this problem lies in the inefficiency of consensus protocols. In this paper, we provide a survey on recent efforts to improve the scalability of blockchains. We focus on layer-two protocols, such as payment channel networks and transaction rollups, which process computations off-chain and only use consensus for dispute resolution. Layer-two protocols are expected to process microtransactions with sub-second latency and reduced fees, allowing blockchains to scale. Much of this work addresses the open challenges of payment channel networks, such as payment routing, channel rebalancing, network design strategies, security and privacy, payment scheduling, congestion control, simulators, and support for light nodes. We also dedicate a section to the existing implementations of smart-contract-based transaction rollups. Our work systematizes the state-of-the-art layer-two protocols, paving the way for future advances.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"26 4","pages":"2411-2458"},"PeriodicalIF":34.4,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140161573","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 : 2024-03-14DOI: 10.1109/COMST.2024.3377531
Lorenzo Rosa;Luca Foschini;Antonio Corradi
Modern interactive and data-intensive applications must operate under demanding time constraints, prompting a shift toward the adoption of specialized software and hardware network acceleration technologies. This specialization, however, poses significant scalability, flexibility, security, and economic sustainability challenges for application developers. Cloud computing holds the potential to overcome these obstacles by offering the cost-effective option to access specialized acceleration technologies through standard cloud interfaces. Nevertheless, that integration is still challenging for cloud providers. In the cloud, physical resources are hidden behind a virtualization layer, whereas acceleration technologies make applications directly interact with the hardware. To bridge this gap, recent literature explores the possibility of empowering cloud platforms with accelerated networking as a commodity, thus offering the innovative option of Network Acceleration as a Service. This survey reviews these recent research efforts by adopting popular technologies like XDP, DPDK, and RDMA as a reference. To organize the surveyed research in a comprehensive framework, we identify four key aspects that pose critical problems to the integration of acceleration options in cloud computing - access interfaces, virtualization techniques, serviceability, and security - and systematically discuss the associated challenges. Then, we present the issues to be further addressed and outline the most promising research directions for the full integration of network acceleration within next-generation cloud computing platforms.
{"title":"Empowering Cloud Computing With Network Acceleration: A Survey","authors":"Lorenzo Rosa;Luca Foschini;Antonio Corradi","doi":"10.1109/COMST.2024.3377531","DOIUrl":"10.1109/COMST.2024.3377531","url":null,"abstract":"Modern interactive and data-intensive applications must operate under demanding time constraints, prompting a shift toward the adoption of specialized software and hardware network acceleration technologies. This specialization, however, poses significant scalability, flexibility, security, and economic sustainability challenges for application developers. Cloud computing holds the potential to overcome these obstacles by offering the cost-effective option to access specialized acceleration technologies through standard cloud interfaces. Nevertheless, that integration is still challenging for cloud providers. In the cloud, physical resources are hidden behind a virtualization layer, whereas acceleration technologies make applications directly interact with the hardware. To bridge this gap, recent literature explores the possibility of empowering cloud platforms with accelerated networking as a commodity, thus offering the innovative option of Network Acceleration as a Service. This survey reviews these recent research efforts by adopting popular technologies like XDP, DPDK, and RDMA as a reference. To organize the surveyed research in a comprehensive framework, we identify four key aspects that pose critical problems to the integration of acceleration options in cloud computing - access interfaces, virtualization techniques, serviceability, and security - and systematically discuss the associated challenges. Then, we present the issues to be further addressed and outline the most promising research directions for the full integration of network acceleration within next-generation cloud computing platforms.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"26 4","pages":"2729-2768"},"PeriodicalIF":34.4,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10472517","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140135628","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}
Pub Date : 2024-03-13DOI: 10.1109/COMST.2024.3377161
Mohammed Mahyoub;AbdulAziz AbdulGhaffar;Emmanuel Alalade;Ezekiel Ndubisi;Ashraf Matrawy
Interfaces play a crucial role in the Fifth Generation (5G) architecture as they interconnect Network Functions (NFs) to exchange data and services. Certain interfaces are particularly critical because they are involved in the exchange of sensitive data or are exposed externally. To the best of our knowledge, no literature work analyzes the 5G security from a network architecture or interface perspective. Therefore, existing research on 5G security may not be helpful when considering a certain component of the network or a specific interface. This paper reviews the security measures recommended by the selected Standardization Development Organizations (SDOs) for critical interfaces and classifies them based on security goals. It also identifies vulnerabilities and threats to these interfaces in the absence of security measures and categorizes them based on STRIDE model and impacted traffic types.
{"title":"Security Analysis of Critical 5G Interfaces","authors":"Mohammed Mahyoub;AbdulAziz AbdulGhaffar;Emmanuel Alalade;Ezekiel Ndubisi;Ashraf Matrawy","doi":"10.1109/COMST.2024.3377161","DOIUrl":"10.1109/COMST.2024.3377161","url":null,"abstract":"Interfaces play a crucial role in the Fifth Generation (5G) architecture as they interconnect Network Functions (NFs) to exchange data and services. Certain interfaces are particularly critical because they are involved in the exchange of sensitive data or are exposed externally. To the best of our knowledge, no literature work analyzes the 5G security from a network architecture or interface perspective. Therefore, existing research on 5G security may not be helpful when considering a certain component of the network or a specific interface. This paper reviews the security measures recommended by the selected Standardization Development Organizations (SDOs) for critical interfaces and classifies them based on security goals. It also identifies vulnerabilities and threats to these interfaces in the absence of security measures and categorizes them based on STRIDE model and impacted traffic types.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"26 4","pages":"2382-2410"},"PeriodicalIF":34.4,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140123866","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 : 2024-03-10DOI: 10.1109/COMST.2024.3400011
Hongyang Du;Ruichen Zhang;Yinqiu Liu;Jiacheng Wang;Yijing Lin;Zonghang Li;Dusit Niyato;Jiawen Kang;Zehui Xiong;Shuguang Cui;Bo Ai;Haibo Zhou;Dong In Kim
Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across various applications. The ability to model complex data distributions and generate high-quality samples has made GDMs particularly effective in tasks such as image generation and reinforcement learning. Furthermore, their iterative nature, which involves a series of noise addition and denoising steps, is a powerful and unique approach to learning and generating data. This paper serves as a comprehensive tutorial on applying GDMs in network optimization tasks. We delve into the strengths of GDMs, emphasizing their wide applicability across various domains, such as vision, text, and audio generation. We detail how GDMs can be effectively harnessed to solve complex optimization problems inherent in networks. The paper first provides a basic background of GDMs and their applications in network optimization. This is followed by a series of case studies, showcasing the integration of GDMs with Deep Reinforcement Learning (DRL), incentive mechanism design, Semantic Communications (SemCom), Internet of Vehicles (IoV) networks, etc. These case studies underscore the practicality and efficacy of GDMs in real-world scenarios, offering insights into network design. We conclude with a discussion on potential future directions for GDM research and applications, providing major insights into how they can continue to shape the future of network optimization.
{"title":"Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization","authors":"Hongyang Du;Ruichen Zhang;Yinqiu Liu;Jiacheng Wang;Yijing Lin;Zonghang Li;Dusit Niyato;Jiawen Kang;Zehui Xiong;Shuguang Cui;Bo Ai;Haibo Zhou;Dong In Kim","doi":"10.1109/COMST.2024.3400011","DOIUrl":"10.1109/COMST.2024.3400011","url":null,"abstract":"Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across various applications. The ability to model complex data distributions and generate high-quality samples has made GDMs particularly effective in tasks such as image generation and reinforcement learning. Furthermore, their iterative nature, which involves a series of noise addition and denoising steps, is a powerful and unique approach to learning and generating data. This paper serves as a comprehensive tutorial on applying GDMs in network optimization tasks. We delve into the strengths of GDMs, emphasizing their wide applicability across various domains, such as vision, text, and audio generation. We detail how GDMs can be effectively harnessed to solve complex optimization problems inherent in networks. The paper first provides a basic background of GDMs and their applications in network optimization. This is followed by a series of case studies, showcasing the integration of GDMs with Deep Reinforcement Learning (DRL), incentive mechanism design, Semantic Communications (SemCom), Internet of Vehicles (IoV) networks, etc. These case studies underscore the practicality and efficacy of GDMs in real-world scenarios, offering insights into network design. We conclude with a discussion on potential future directions for GDM research and applications, providing major insights into how they can continue to shape the future of network optimization.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"26 4","pages":"2611-2646"},"PeriodicalIF":34.4,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140907362","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 : 2024-03-10DOI: 10.1109/COMST.2024.3399612
Cheng Qiao;Mianjie Li;Yuan Liu;Zhihong Tian
Quantum Federated Learning (QFL) recently becomes a promising approach with the potential to revolutionize Machine Learning (ML). It merges the established strengths of classical Federated Learning (FL) with the exceptional parallel mechanism and exponential speed enhancements characteristic of quantum computing. While this innovative fusion has garnered considerable attention, a notable gap in current research is the tendency to consider traditional FL and its quantum-enhanced counterpart, QFL, in isolation. This approach often overlooks the critical role of Quantum Machine Learning (QML) in effectively bridging these two domains. Recognizing this, there emerges a pressing need for a comprehensive survey that encompasses the entire spectrum of FL paradigms, from foundational FL concepts to the cutting-edge developments in QFL. Our survey aims to address this need by providing an in-depth exploration of the various facets of FL paradigms, ultimately leading to a thorough understanding of Quantum Federated Learning. We start by emphasizing the driving factors and prevalent research topics related to FL. To develop a more efficient, robust, and precise computing paradigm, we investigate the current solutions that address the concerns of heterogeneity, privacy, security, and evaluation in FL. After that, we explore the possibility of improving the computational efficiency of ML methods by leveraging the computational capabilities of quantum computers. In particular, we discuss the frameworks, evaluation, and applications for QML. Following that, we detail the integration of quantum computing technologies with standard FL, aiming to bolster computational performance and mitigate security and privacy risks. The potential solutions to improve the efficiency (i.e., slimming mechanism) and respect the privacy and security (i.e., quantum key distribution) for QFL are explored. Finally, we outline some critical future directions towards unlocking the full potential of QFL in distributed machine learning.
{"title":"Transitioning From Federated Learning to Quantum Federated Learning in Internet of Things: A Comprehensive Survey","authors":"Cheng Qiao;Mianjie Li;Yuan Liu;Zhihong Tian","doi":"10.1109/COMST.2024.3399612","DOIUrl":"10.1109/COMST.2024.3399612","url":null,"abstract":"Quantum Federated Learning (QFL) recently becomes a promising approach with the potential to revolutionize Machine Learning (ML). It merges the established strengths of classical Federated Learning (FL) with the exceptional parallel mechanism and exponential speed enhancements characteristic of quantum computing. While this innovative fusion has garnered considerable attention, a notable gap in current research is the tendency to consider traditional FL and its quantum-enhanced counterpart, QFL, in isolation. This approach often overlooks the critical role of Quantum Machine Learning (QML) in effectively bridging these two domains. Recognizing this, there emerges a pressing need for a comprehensive survey that encompasses the entire spectrum of FL paradigms, from foundational FL concepts to the cutting-edge developments in QFL. Our survey aims to address this need by providing an in-depth exploration of the various facets of FL paradigms, ultimately leading to a thorough understanding of Quantum Federated Learning. We start by emphasizing the driving factors and prevalent research topics related to FL. To develop a more efficient, robust, and precise computing paradigm, we investigate the current solutions that address the concerns of heterogeneity, privacy, security, and evaluation in FL. After that, we explore the possibility of improving the computational efficiency of ML methods by leveraging the computational capabilities of quantum computers. In particular, we discuss the frameworks, evaluation, and applications for QML. Following that, we detail the integration of quantum computing technologies with standard FL, aiming to bolster computational performance and mitigate security and privacy risks. The potential solutions to improve the efficiency (i.e., slimming mechanism) and respect the privacy and security (i.e., quantum key distribution) for QFL are explored. Finally, we outline some critical future directions towards unlocking the full potential of QFL in distributed machine learning.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"27 1","pages":"509-545"},"PeriodicalIF":34.4,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140907365","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}
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