On-device large language models (LLMs), referring to running LLMs on edge devices, have raised considerable interest since they are more cost-effective, latency-efficient, and privacy-preserving compared with the cloud LLM paradigm. Nonetheless unlike cloud LLMs, the performance of on-device LLMs is intrinsically constrained by resource limitations on edge devices. Sitting between cloud and on-device AI, mobile edge intelligence (MEI) may address this dilemma by provisioning AI capabilities at the edge of mobile networks, e.g., on base stations. This article provides a contemporary survey on harnessing MEI for LLM deployment. We begin by illustrating several killer applications to demonstrate the urgent need for deploying LLMs at the network edge. Next, we present the preliminaries of LLMs, MEI, and resource-efficient LLM techniques. We then provide an architectural overview of MEI for LLMs (MEI4LLM), outlining its core components and how it supports LLM deployment. Subsequently, we delve into various aspects of MEI4LLM, extensively covering edge LLM caching and delivery, edge LLM training, and edge LLM inference. Finally, we identify future research opportunities. We hope this article inspires researchers in the field to leverage mobile edge computing to facilitate LLM deployment, thereby unleashing the potential of LLMs across various privacy- and delay-sensitive applications.
{"title":"Mobile Edge Intelligence for Large Language Models: A Contemporary Survey","authors":"Guanqiao Qu;Qiyuan Chen;Wei Wei;Zheng Lin;Xianhao Chen;Kaibin Huang","doi":"10.1109/COMST.2025.3527641","DOIUrl":"10.1109/COMST.2025.3527641","url":null,"abstract":"On-device large language models (LLMs), referring to running LLMs on edge devices, have raised considerable interest since they are more cost-effective, latency-efficient, and privacy-preserving compared with the cloud LLM paradigm. Nonetheless unlike cloud LLMs, the performance of on-device LLMs is intrinsically constrained by resource limitations on edge devices. Sitting between cloud and on-device AI, mobile edge intelligence (MEI) may address this dilemma by provisioning AI capabilities at the edge of mobile networks, e.g., on base stations. This article provides a contemporary survey on harnessing MEI for LLM deployment. We begin by illustrating several killer applications to demonstrate the urgent need for deploying LLMs at the network edge. Next, we present the preliminaries of LLMs, MEI, and resource-efficient LLM techniques. We then provide an architectural overview of MEI for LLMs (MEI4LLM), outlining its core components and how it supports LLM deployment. Subsequently, we delve into various aspects of MEI4LLM, extensively covering edge LLM caching and delivery, edge LLM training, and edge LLM inference. Finally, we identify future research opportunities. We hope this article inspires researchers in the field to leverage mobile edge computing to facilitate LLM deployment, thereby unleashing the potential of LLMs across various privacy- and delay-sensitive applications.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"27 6","pages":"3820-3860"},"PeriodicalIF":34.4,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142940129","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}
Recent years have witnessed a thriving growth of computing facilities connected at the network edge, cultivating edge networks as a fundamental infrastructure for supporting miscellaneous intelligent services. Meanwhile, Artificial Intelligence (AI) frontiers have extrapolated to the graph domain and promoted Graph Intelligence (GI). Given the inherent relation between graphs and networks, the interdiscipline of graph learning and edge networks, i.e., Edge GI or EGI, has revealed a novel interplay between them – GI aids in optimizing edge networks, while edge networks facilitate GI model deployment. Driven by this delicate closed-loop, EGI is recognized as a promising solution to fully unleash the potential of edge computing power and is garnering growing attention. Nevertheless, research on EGI remains nascent, and there is a soaring demand within both the communications and AI communities for a dedicated venue to share recent advancements. To this end, this paper promotes the concept of EGI, explores its scope and core principles, and conducts a comprehensive survey concerning recent research efforts on this emerging field. Specifically, this paper introduces and discusses: 1) fundamentals of edge computing and graph learning, 2) emerging techniques centering on the closed loop between graph intelligence and edge networks, and 3) open challenges and research opportunities of future EGI. By bridging the gap across communication, networking, and graph learning areas, we believe that this survey can garner increased attention, foster meaningful discussions, and inspire further research ideas in EGI.
{"title":"Edge Graph Intelligence: Reciprocally Empowering Edge Networks With Graph Intelligence","authors":"Liekang Zeng;Shengyuan Ye;Xu Chen;Xiaoxi Zhang;Ju Ren;Jian Tang;Yang Yang;Xuemin Shen","doi":"10.1109/COMST.2025.3527561","DOIUrl":"10.1109/COMST.2025.3527561","url":null,"abstract":"Recent years have witnessed a thriving growth of computing facilities connected at the network edge, cultivating edge networks as a fundamental infrastructure for supporting miscellaneous intelligent services. Meanwhile, Artificial Intelligence (AI) frontiers have extrapolated to the graph domain and promoted Graph Intelligence (GI). Given the inherent relation between graphs and networks, the interdiscipline of graph learning and edge networks, i.e., Edge GI or EGI, has revealed a novel interplay between them – GI aids in optimizing edge networks, while edge networks facilitate GI model deployment. Driven by this delicate closed-loop, EGI is recognized as a promising solution to fully unleash the potential of edge computing power and is garnering growing attention. Nevertheless, research on EGI remains nascent, and there is a soaring demand within both the communications and AI communities for a dedicated venue to share recent advancements. To this end, this paper promotes the concept of EGI, explores its scope and core principles, and conducts a comprehensive survey concerning recent research efforts on this emerging field. Specifically, this paper introduces and discusses: 1) fundamentals of edge computing and graph learning, 2) emerging techniques centering on the closed loop between graph intelligence and edge networks, and 3) open challenges and research opportunities of future EGI. By bridging the gap across communication, networking, and graph learning areas, we believe that this survey can garner increased attention, foster meaningful discussions, and inspire further research ideas in EGI.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"27 6","pages":"3417-3454"},"PeriodicalIF":34.4,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142940131","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 : 2025-01-08DOI: 10.1109/COMST.2024.3519785
Anton Tishchenko;Mohsen Khalily;Arman Shojaeifard;Fraser Burton;Emil Björnson;Marco Di Renzo;Rahim Tafazolli
Reconfigurable intelligent surfaces (RIS) are positioned as one of the key enabling technologies for 6G networks as they can provide ubiquitous coverage for areas with blocked line-of-sight (LOS) links. However, to be successfully integrated into functional networks such structures will require the addition of sensors and other radio network elements, thereby resulting in a multi-functional RIS (MF-RIS). These structures are expected to be deployed for integrated sensing and communications (ISAC) and radar and communication coexistence (RCC) in 6G, which will enhance the performance of radio communication and enable a smart wireless environment (SWE) that is programmable and self-reconfigurable. This survey provides an up-to-date summary of the state of the art. It considers applications for MF-RISs and the challenges associated with their deployment.
{"title":"The Emergence of Multi-Functional and Hybrid Reconfigurable Intelligent Surfaces for Integrated Sensing and Communications - A Survey","authors":"Anton Tishchenko;Mohsen Khalily;Arman Shojaeifard;Fraser Burton;Emil Björnson;Marco Di Renzo;Rahim Tafazolli","doi":"10.1109/COMST.2024.3519785","DOIUrl":"10.1109/COMST.2024.3519785","url":null,"abstract":"Reconfigurable intelligent surfaces (RIS) are positioned as one of the key enabling technologies for 6G networks as they can provide ubiquitous coverage for areas with blocked line-of-sight (LOS) links. However, to be successfully integrated into functional networks such structures will require the addition of sensors and other radio network elements, thereby resulting in a multi-functional RIS (MF-RIS). These structures are expected to be deployed for integrated sensing and communications (ISAC) and radar and communication coexistence (RCC) in 6G, which will enhance the performance of radio communication and enable a smart wireless environment (SWE) that is programmable and self-reconfigurable. This survey provides an up-to-date summary of the state of the art. It considers applications for MF-RISs and the challenges associated with their deployment.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"27 5","pages":"2895-2936"},"PeriodicalIF":34.4,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10833623","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142937474","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}
As Large Language Models (LLMs) have achieved significant success in handling multi-modal tasks such as text, images, videos, and sounds, particularly showcasing emergent capabilities in natural language tasks, they hold great potential for network operations that similarly involve vast amounts of text data, fault data, and log files. This paper focuses on the development of LLMs, detailing their fundamental principles and application scenarios across different domains. It highlights the remarkable capabilities of LLMs in tasks such as fault diagnosis, causal inference, and intelligent question answering, and applies these abilities to the field of network operations. Moreover, the paper reviews some of the key issues and technical barriers faced by intelligent networks, such as efficiently monitoring networks in real-time and providing timely alerts when necessary. In addition to examining the utilization of LLM in network operations, this paper introduces a framework for intelligent network operations and performance optimization, leveraging LLM. The objective of this framework is to bolster network robustness and furnish users with exceptional, personalized network services. Ultimately, we conclude by delineating the challenges encountered in LLM-based intelligent network operations and performance optimization, while presenting potential solutions to overcome these hurdles and propel the comprehensive deployment of LLM-driven network intelligence.
{"title":"A Survey on Intelligent Network Operations and Performance Optimization Based on Large Language Models","authors":"Sifan Long;Jingjing Tan;Bomin Mao;Fengxiao Tang;Yangfan Li;Ming Zhao;Nei Kato","doi":"10.1109/COMST.2025.3526606","DOIUrl":"10.1109/COMST.2025.3526606","url":null,"abstract":"As Large Language Models (LLMs) have achieved significant success in handling multi-modal tasks such as text, images, videos, and sounds, particularly showcasing emergent capabilities in natural language tasks, they hold great potential for network operations that similarly involve vast amounts of text data, fault data, and log files. This paper focuses on the development of LLMs, detailing their fundamental principles and application scenarios across different domains. It highlights the remarkable capabilities of LLMs in tasks such as fault diagnosis, causal inference, and intelligent question answering, and applies these abilities to the field of network operations. Moreover, the paper reviews some of the key issues and technical barriers faced by intelligent networks, such as efficiently monitoring networks in real-time and providing timely alerts when necessary. In addition to examining the utilization of LLM in network operations, this paper introduces a framework for intelligent network operations and performance optimization, leveraging LLM. The objective of this framework is to bolster network robustness and furnish users with exceptional, personalized network services. Ultimately, we conclude by delineating the challenges encountered in LLM-based intelligent network operations and performance optimization, while presenting potential solutions to overcome these hurdles and propel the comprehensive deployment of LLM-driven network intelligence.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"27 6","pages":"3915-3949"},"PeriodicalIF":34.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142936172","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 : 2025-01-06DOI: 10.1109/COMST.2025.3526605
Jemin Ahn;Rasheed Hussain;Kyungtae Kang;Junggab Son
Cryptographic network protocols play a crucial role in enabling secure data exchange over insecure media in modern network environments. However, even minor vulnerabilities can make protocols an easy target for cyber attackers. Therefore, it is essential to investigate the threats and vulnerabilities stemming from the cryptographic network protocols. Furthermore, it is necessary to comprehensively investigate the weaknesses of network protocols that use cryptographic primitives to inform users and developers about potential attack points. This comprehensive survey examines the relationship between encryption schemes and network protocols and presents an in-depth review of associated threats and vulnerabilities. Given that most cryptographic protocols operate in the Transport and Application layers of the Transmission Control Protocol/Internet Protocol (TCP/IP) protocol stack, our investigation primarily centers around encryption algorithms used by representative and notable cryptographic network protocols such as Transport Layer Security (TLS) and Secure Shell (SSH). Furthermore, we delve into the attackers’ methods to exploit the already identified and existing vulnerabilities, seeking to understand the mechanisms employed to compromise these protocols. Through this survey, we aim to provide the readership with an in-depth understanding of the existing and new vulnerabilities associated with modern cryptographic protocols and provide valuable insights into securing them effectively. We also discuss the existing challenges and future research directions in this domain.
{"title":"Exploring Encryption Algorithms and Network Protocols: A Comprehensive Survey of Threats and Vulnerabilities","authors":"Jemin Ahn;Rasheed Hussain;Kyungtae Kang;Junggab Son","doi":"10.1109/COMST.2025.3526605","DOIUrl":"10.1109/COMST.2025.3526605","url":null,"abstract":"Cryptographic network protocols play a crucial role in enabling secure data exchange over insecure media in modern network environments. However, even minor vulnerabilities can make protocols an easy target for cyber attackers. Therefore, it is essential to investigate the threats and vulnerabilities stemming from the cryptographic network protocols. Furthermore, it is necessary to comprehensively investigate the weaknesses of network protocols that use cryptographic primitives to inform users and developers about potential attack points. This comprehensive survey examines the relationship between encryption schemes and network protocols and presents an in-depth review of associated threats and vulnerabilities. Given that most cryptographic protocols operate in the Transport and Application layers of the Transmission Control Protocol/Internet Protocol (TCP/IP) protocol stack, our investigation primarily centers around encryption algorithms used by representative and notable cryptographic network protocols such as Transport Layer Security (TLS) and Secure Shell (SSH). Furthermore, we delve into the attackers’ methods to exploit the already identified and existing vulnerabilities, seeking to understand the mechanisms employed to compromise these protocols. Through this survey, we aim to provide the readership with an in-depth understanding of the existing and new vulnerabilities associated with modern cryptographic protocols and provide valuable insights into securing them effectively. We also discuss the existing challenges and future research directions in this domain.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"27 6","pages":"3587-3614"},"PeriodicalIF":34.4,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10829860","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142934436","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 : 2025-01-06DOI: 10.1109/COMST.2025.3526251
Zhaolong Ning;Tengfeng Li;Yu Wu;Xiaojie Wang;Qingqing Wu;Fei Richard Yu;Song Guo
With the continuous development of Intelligent Reflecting Surfaces (IRSs) and autonomous aerial vehicles (AAVs), their combination has become foundational technologies to complement the terrestrial network by providing communication enhancement services for large-scale users. This article provides a comprehensive overview of IRS-assisted AAV communications for 6th-Generation (6G) networks. First, the applications supported by IRS-assisted AAV communications for 6G networks are introduced, and key issues originated from applications supported by IRSs and AAVs for 6G networks are summarized and analyzed. Then, prototypes and main technologies related to the integration of IRSs and AAVs are introduced. Driven by applications and technologies of IRS-assisted AAV communications, existing solutions in the realms of energy-constrained communications, secure communications, and enhanced communications are summarized, and corresponding empirical lessons are provided. Finally, we discuss some research challenges and open issues in IRS-assisted AAV communications, offering directions for the future development.
{"title":"6G Communication New Paradigm: The Integration of Autonomous Aerial Vehicles and Intelligent Reflecting Surfaces","authors":"Zhaolong Ning;Tengfeng Li;Yu Wu;Xiaojie Wang;Qingqing Wu;Fei Richard Yu;Song Guo","doi":"10.1109/COMST.2025.3526251","DOIUrl":"10.1109/COMST.2025.3526251","url":null,"abstract":"With the continuous development of Intelligent Reflecting Surfaces (IRSs) and autonomous aerial vehicles (AAVs), their combination has become foundational technologies to complement the terrestrial network by providing communication enhancement services for large-scale users. This article provides a comprehensive overview of IRS-assisted AAV communications for 6th-Generation (6G) networks. First, the applications supported by IRS-assisted AAV communications for 6G networks are introduced, and key issues originated from applications supported by IRSs and AAVs for 6G networks are summarized and analyzed. Then, prototypes and main technologies related to the integration of IRSs and AAVs are introduced. Driven by applications and technologies of IRS-assisted AAV communications, existing solutions in the realms of energy-constrained communications, secure communications, and enhanced communications are summarized, and corresponding empirical lessons are provided. Finally, we discuss some research challenges and open issues in IRS-assisted AAV communications, offering directions for the future development.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"27 6","pages":"3382-3416"},"PeriodicalIF":34.4,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142934676","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}
Artificial intelligence generated content (AIGC) relies on advanced AI algorithms supported by extensive datasets and substantial computing power to generate precise and pertinent content. Federated learning (FL) enables the aggregation of large volumes of data and computing resources from various sources, all while safeguarding privacy. As a result, FL has emerged as a critical enabler in the realm of AIGC. This survey paper provides a comprehensive overview of the integration of FL and AIGC, namely federated AIGC models. First, we introduce the fundamental concepts of FL and AIGC. Next, we summarize four typical types of federated AIGC models. Subsequently, We highlight the threats to centralized federated AIGC models regarding data confidentiality, integrity, and availability and discuss the unique advantages of blockchain technology in decentralized federated AIGC models in addressing these issues. Finally, we look at potential emerging application scenarios and explore open issues and future directions for federated AIGC models.
{"title":"Integration of Federated Learning and AI-Generated Content: A Survey of Overview, Opportunities, Challenges, and Solutions","authors":"Ying Liu;Jianhui Yin;Weiting Zhang;Changming An;Yu Xia;Hongke Zhang","doi":"10.1109/COMST.2024.3523350","DOIUrl":"10.1109/COMST.2024.3523350","url":null,"abstract":"Artificial intelligence generated content (AIGC) relies on advanced AI algorithms supported by extensive datasets and substantial computing power to generate precise and pertinent content. Federated learning (FL) enables the aggregation of large volumes of data and computing resources from various sources, all while safeguarding privacy. As a result, FL has emerged as a critical enabler in the realm of AIGC. This survey paper provides a comprehensive overview of the integration of FL and AIGC, namely federated AIGC models. First, we introduce the fundamental concepts of FL and AIGC. Next, we summarize four typical types of federated AIGC models. Subsequently, We highlight the threats to centralized federated AIGC models regarding data confidentiality, integrity, and availability and discuss the unique advantages of blockchain technology in decentralized federated AIGC models in addressing these issues. Finally, we look at potential emerging application scenarios and explore open issues and future directions for federated AIGC models.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"27 5","pages":"3308-3338"},"PeriodicalIF":34.4,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142888782","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 : 2024-12-25DOI: 10.1109/COMST.2024.3521647
Adeel Ahmed;Xingfu Wang;Ammar Hawbani;Weijie Yuan;Hina Tabassum;Yuanwei Liu;Muhammad Umar Farooq Qaisar;Zhiguo Ding;Naofal Al-Dhahir;Arumugam Nallanathan;Derrick Wing Kwan Ng
The revolutionary sixth-generation wireless communications technologies and applications, notably digital twin networks (DTN), connected autonomous vehicles (CAVs), space-air-ground integrated networks (SAGINs), zero-touch networks, industry 5.0, healthcare 5.0, agriculture 5.0, and more, are driving the evolution of next-generation wireless networks (NGWNs). These innovative technologies and groundbreaking innovative applications will generate a sheer volume of data that requires the swift transmission of massive data across wireless networks and the capability to connect trillions of devices, thereby fueling the use of sophisticated next-generation multiple access (NGMA) schemes. In particular, NGMA strives to cater to the massive connectivity in the 6G era, enabling the smooth and optimized operations of NGWNs compared to existing multiple access (MA) schemes. This survey showcases non-orthogonal multiple access (NOMA) as the frontrunner for NGMA, spotlighting its novel contributions within the existing literature in terms of “What has NOMA delivered?”, “What is NOMA currently providing?” and “What lies ahead for NOMA?”. We present different variants of NOMA in this comprehensive survey, detailing their fundamental operations. In addition, this survey highlights NOMA’s applicability in a broad range of wireless communications technologies such as multi-antenna systems, machine learning, reconfigurable intelligent surfaces (RIS), cognitive radio networks (CRN), integrated sensing and communications (ISAC), terahertz networks, autonomous aerial vehicles (AAVs), etc. This survey delves deeper by providing a comprehensive literature review of NOMA’s interplay with various state-of-the-art wireless technologies. Furthermore, despite the numerous perks and advantages of NOMA, we also highlight several technical challenges of NOMA, which NOMA-assisted NGWNs may encounter. Next, we unveil the research trends of NOMA in the 6G era, offering reliable, robust, and swift communications. Finally, we offer design recommendations and insights along with the future perspectives of NOMA as the leading choice for NGMA within the realm of NGWNs.
{"title":"Unveiling the Potential of NOMA: A Journey to Next-Generation Multiple Access","authors":"Adeel Ahmed;Xingfu Wang;Ammar Hawbani;Weijie Yuan;Hina Tabassum;Yuanwei Liu;Muhammad Umar Farooq Qaisar;Zhiguo Ding;Naofal Al-Dhahir;Arumugam Nallanathan;Derrick Wing Kwan Ng","doi":"10.1109/COMST.2024.3521647","DOIUrl":"10.1109/COMST.2024.3521647","url":null,"abstract":"The revolutionary sixth-generation wireless communications technologies and applications, notably digital twin networks (DTN), connected autonomous vehicles (CAVs), space-air-ground integrated networks (SAGINs), zero-touch networks, industry 5.0, healthcare 5.0, agriculture 5.0, and more, are driving the evolution of next-generation wireless networks (NGWNs). These innovative technologies and groundbreaking innovative applications will generate a sheer volume of data that requires the swift transmission of massive data across wireless networks and the capability to connect trillions of devices, thereby fueling the use of sophisticated next-generation multiple access (NGMA) schemes. In particular, NGMA strives to cater to the massive connectivity in the 6G era, enabling the smooth and optimized operations of NGWNs compared to existing multiple access (MA) schemes. This survey showcases non-orthogonal multiple access (NOMA) as the frontrunner for NGMA, spotlighting its novel contributions within the existing literature in terms of “What has NOMA delivered?”, “What is NOMA currently providing?” and “What lies ahead for NOMA?”. We present different variants of NOMA in this comprehensive survey, detailing their fundamental operations. In addition, this survey highlights NOMA’s applicability in a broad range of wireless communications technologies such as multi-antenna systems, machine learning, reconfigurable intelligent surfaces (RIS), cognitive radio networks (CRN), integrated sensing and communications (ISAC), terahertz networks, autonomous aerial vehicles (AAVs), etc. This survey delves deeper by providing a comprehensive literature review of NOMA’s interplay with various state-of-the-art wireless technologies. Furthermore, despite the numerous perks and advantages of NOMA, we also highlight several technical challenges of NOMA, which NOMA-assisted NGWNs may encounter. Next, we unveil the research trends of NOMA in the 6G era, offering reliable, robust, and swift communications. Finally, we offer design recommendations and insights along with the future perspectives of NOMA as the leading choice for NGMA within the realm of NGWNs.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"27 5","pages":"3099-3164"},"PeriodicalIF":34.4,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142888810","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 : 2024-12-23DOI: 10.1109/COMST.2024.3521498
Dingzhu Wen;Yong Zhou;Xiaoyang Li;Yuanming Shi;Kaibin Huang;Khaled B. Letaief
The forthcoming generation of wireless technology, 6G, promises a revolutionary leap beyond traditional data-centric services. It aims to usher in an era of ubiquitous intelligent services, where everything is interconnected and intelligent. This vision requires the seamless integration of three fundamental modules: Sensing for information acquisition, communication for information sharing, and computation for information processing and decision-making. These modules are intricately linked, especially in complex tasks such as edge learning and inference. However, the performance of these modules is interdependent, creating a resource competition for time, energy, and bandwidth. Existing techniques like integrated communication and computation (ICC), integrated sensing and computation (ISC), and integrated sensing and communication (ISAC) have made partial strides in addressing this challenge, but they fall short of meeting the extreme performance requirements. To overcome these limitations, it is essential to develop new techniques that comprehensively integrate sensing, communication, and computation. This integrated approach, known as Integrated Sensing, Communication, and Computation (ISCC), offers a systematic perspective for enhancing task performance. This paper begins with a comprehensive survey of historic and related techniques such as ICC, ISC, and ISAC, highlighting their strengths and limitations. It then discusses the benefits, functions, and challenges of ISCC. Subsequently, the state-of-the-art signal designs for ISCC, along with network resource management strategies specifically tailored for ISCC are explored. Furthermore, this paper discusses the exciting research opportunities that lie ahead for implementing ISCC in future advanced networks, and the unresolved issues requiring further investigation. ISCC is expected to unlock the full potential of intelligent connectivity, paving the way for groundbreaking applications and services.
{"title":"A Survey on Integrated Sensing, Communication, and Computation","authors":"Dingzhu Wen;Yong Zhou;Xiaoyang Li;Yuanming Shi;Kaibin Huang;Khaled B. Letaief","doi":"10.1109/COMST.2024.3521498","DOIUrl":"10.1109/COMST.2024.3521498","url":null,"abstract":"The forthcoming generation of wireless technology, 6G, promises a revolutionary leap beyond traditional data-centric services. It aims to usher in an era of ubiquitous intelligent services, where everything is interconnected and intelligent. This vision requires the seamless integration of three fundamental modules: Sensing for information acquisition, communication for information sharing, and computation for information processing and decision-making. These modules are intricately linked, especially in complex tasks such as edge learning and inference. However, the performance of these modules is interdependent, creating a resource competition for time, energy, and bandwidth. Existing techniques like integrated communication and computation (ICC), integrated sensing and computation (ISC), and integrated sensing and communication (ISAC) have made partial strides in addressing this challenge, but they fall short of meeting the extreme performance requirements. To overcome these limitations, it is essential to develop new techniques that comprehensively integrate sensing, communication, and computation. This integrated approach, known as Integrated Sensing, Communication, and Computation (ISCC), offers a systematic perspective for enhancing task performance. This paper begins with a comprehensive survey of historic and related techniques such as ICC, ISC, and ISAC, highlighting their strengths and limitations. It then discusses the benefits, functions, and challenges of ISCC. Subsequently, the state-of-the-art signal designs for ISCC, along with network resource management strategies specifically tailored for ISCC are explored. Furthermore, this paper discusses the exciting research opportunities that lie ahead for implementing ISCC in future advanced networks, and the unresolved issues requiring further investigation. ISCC is expected to unlock the full potential of intelligent connectivity, paving the way for groundbreaking applications and services.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"27 5","pages":"3058-3098"},"PeriodicalIF":34.4,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10812728","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142879804","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 : 2024-12-18DOI: 10.1109/COMST.2024.3519865
Muhammad Omair Butt;Nazar Waheed;Trung Q. Duong;Waleed Ejaz
The Internet of Things (IoT) drives an exponential surge in computing and communication devices. Consequently, it triggers capacity, coverage, interference, latency, and security issues in the existing communication networks. The forthcoming sixth-generation (6G) networks will address these issues by providing comprehensive solutions. In particular, quantum communication technology can potentially address the challenges of 6G networks. However, its implementation requires substantial infrastructure upgrades. Therefore, the quantum-inspired (QI) techniques offer an intermediate resort due to their ability to utilize the classical communication infrastructure for design and implementation. Hence, we review QI techniques in this survey that address radio resource optimization challenges across various communication aspects, including channel assignment, reconfigurable intelligent surfaces, spectrum sensing, uncrewed aerial vehicle-assisted networks, and related areas. The analysis explores diverse aspects, including objectives, constraints, problem characterization, proposed solutions, and lessons learnt. Research indicates that QI techniques offer advantages such as faster convergence and reduced complexity, providing promising solutions to complex optimization problems in communication networks. Furthermore, we identify the future directions, research gaps, and ongoing challenges from the QI radio resource optimization dataset.
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