Pub Date : 2024-11-21DOI: 10.1109/COMST.2024.3464708
Dusit Niyato
I welcome you to the fourth issue of the IEEE Communications Surveys and Tutorials in 2024. This issue includes 19 papers covering different aspects of communication networks. In particular, these articles survey and tutor various issues in “Wireless Communications”, “Cyber Security”, “IoT and M2M”, “Vehicular and Sensor Communications”, “Internet Technologies”, “Network and Service Management and Green Communications”, “Network Virtualization”, “Optical Communications”, and “Multimedia Communications”. A brief account of each of these papers is given below.
{"title":"Editorial: Fourth Quarter 2024 IEEE Communications Surveys and Tutorials","authors":"Dusit Niyato","doi":"10.1109/COMST.2024.3464708","DOIUrl":"https://doi.org/10.1109/COMST.2024.3464708","url":null,"abstract":"I welcome you to the fourth issue of the IEEE Communications Surveys and Tutorials in 2024. This issue includes 19 papers covering different aspects of communication networks. In particular, these articles survey and tutor various issues in “Wireless Communications”, “Cyber Security”, “IoT and M2M”, “Vehicular and Sensor Communications”, “Internet Technologies”, “Network and Service Management and Green Communications”, “Network Virtualization”, “Optical Communications”, and “Multimedia Communications”. A brief account of each of these papers is given below.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"26 4","pages":"i-vi"},"PeriodicalIF":34.4,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10762798","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679407","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}
Frequency synchronization is essential to achieving the intended performance for single and multicarrier wireless systems. Blind techniques, which don’t require prior channel knowledge or pilot symbols, are crucial for dynamic environments where self-adaptive synchronization is needed. A key objective of this paper is to provide the readers as well as the industry’s professionals with a comprehensive understanding of the carrier frequency offset (CFO) problem in multicarrier communication systems like orthogonal frequency division multiplexing (OFDM), single carrier-frequency division multiple access (SC-FDMA), multiple input multiple output (MIMO)-OFDM, and MIMO-SC-FDMA. These waveforms are used in today’s and future wireless communication systems such as wireless-fidelity (Wi-Fi), fifth-generation, and sixth-generation. Moreover, this paper also develops a taxonomy of the available solutions to address the CFO issue. We study blind techniques for CFO estimation presented in the recent literature and give potential future directions. We summarize various statistical methods and deep learning algorithms for CFO estimation and emphasize their advantages and limitations. We also incorporate the CFO impact on next-generation wireless systems such as orthogonal time frequency space and reconfigurable intelligent surface-assisted communication systems and provide a broader and deeper knowledge of the area. We provide simulation results of some existing estimators and their performance comparison in terms of mean square error for better understanding. Therefore, this paper is perfectly adapted to provide a comprehensive information source on blind CFO estimation techniques.
{"title":"Blind Carrier Frequency Offset Estimation Techniques for Next-Generation Multicarrier Communication Systems: Challenges, Comparative Analysis, and Future Prospects","authors":"Shivani Singh;Sushant Kumar;Sudhan Majhi;Udit Satija;Chau Yuen","doi":"10.1109/COMST.2024.3472109","DOIUrl":"10.1109/COMST.2024.3472109","url":null,"abstract":"Frequency synchronization is essential to achieving the intended performance for single and multicarrier wireless systems. Blind techniques, which don’t require prior channel knowledge or pilot symbols, are crucial for dynamic environments where self-adaptive synchronization is needed. A key objective of this paper is to provide the readers as well as the industry’s professionals with a comprehensive understanding of the carrier frequency offset (CFO) problem in multicarrier communication systems like orthogonal frequency division multiplexing (OFDM), single carrier-frequency division multiple access (SC-FDMA), multiple input multiple output (MIMO)-OFDM, and MIMO-SC-FDMA. These waveforms are used in today’s and future wireless communication systems such as wireless-fidelity (Wi-Fi), fifth-generation, and sixth-generation. Moreover, this paper also develops a taxonomy of the available solutions to address the CFO issue. We study blind techniques for CFO estimation presented in the recent literature and give potential future directions. We summarize various statistical methods and deep learning algorithms for CFO estimation and emphasize their advantages and limitations. We also incorporate the CFO impact on next-generation wireless systems such as orthogonal time frequency space and reconfigurable intelligent surface-assisted communication systems and provide a broader and deeper knowledge of the area. We provide simulation results of some existing estimators and their performance comparison in terms of mean square error for better understanding. Therefore, this paper is perfectly adapted to provide a comprehensive information source on blind CFO estimation techniques.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"27 1","pages":"1-36"},"PeriodicalIF":34.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142362839","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 : 2024-08-22DOI: 10.1109/COMST.2024.3430588
Dusit Niyato
I welcome you to the third issue of the IEEE Communications Surveys and Tutorials in 2024. This issue includes 19 papers covering different aspects of communication networks. In particular, these articles survey and tutor various issues in “Wireless Communications”, “Cyber Security”, “Network Virtualization”, “Vehicular and Sensor Communications”, “Multimedia Communications”, “Network and Service Management and Green Communications”, and “Internet Technologies”. A brief account of each of these papers is given below.
{"title":"Editorial: Third Quarter 2024 IEEE Communications Surveys and Tutorials","authors":"Dusit Niyato","doi":"10.1109/COMST.2024.3430588","DOIUrl":"https://doi.org/10.1109/COMST.2024.3430588","url":null,"abstract":"I welcome you to the third issue of the IEEE Communications Surveys and Tutorials in 2024. This issue includes 19 papers covering different aspects of communication networks. In particular, these articles survey and tutor various issues in “Wireless Communications”, “Cyber Security”, “Network Virtualization”, “Vehicular and Sensor Communications”, “Multimedia Communications”, “Network and Service Management and Green Communications”, and “Internet Technologies”. A brief account of each of these papers is given below.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"26 3","pages":"i-vii"},"PeriodicalIF":34.4,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10643728","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142041438","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 : 2024-07-03DOI: 10.1109/COMST.2024.3422613
Jorge Sasiain;David Franco;Asier Atutxa;Jasone Astorga;Eduardo Jacob
Communication networks are in constant evolution to adapt to the ever-changing demands of modern services and applications. On the one hand, technologies within the framework of the fifth generation of communication networks (5G), currently in a steady course towards 6G, are being designed with unprecedented performance and flexibility capabilities to empower the digital transformation of heterogeneous vertical industries. On the other hand, Time-Sensitive Networking (TSN) is being developed to enable converged network infrastructures able to satisfy the most stringent communication requirements in regards to bounded low latency and reliability. In verticals such as manufacturing, a joint usage of 5G and TSN is very promising to enable revolutionary use cases that coexist with the constraints imposed by industrial communications and systems. In this paper, we exhaustively survey the technology landscape concerning the integration between 5G and TSN technologies, including the perspectives of both industry and academia. In the first part of this paper, we provide a comprehensive review of relevant standards and outcomes from industry initiatives. We first examine the capabilities provided by TSN and their application to industrial communications. Likewise, we contextualize the 5G architecture and its key enabling technologies, and later provide a comprehensive review of the architecture and mechanisms to enable the integration between TSN and 5G. In the second part of this paper, we extensively survey the state of the art from academic literature concerning proposals that contribute to enabling, improving, and/or demonstrating different aspects of the integration or interworking between 5G and TSN technologies.
{"title":"Toward the Integration and Convergence Between 5G and TSN Technologies and Architectures for Industrial Communications: A Survey","authors":"Jorge Sasiain;David Franco;Asier Atutxa;Jasone Astorga;Eduardo Jacob","doi":"10.1109/COMST.2024.3422613","DOIUrl":"10.1109/COMST.2024.3422613","url":null,"abstract":"Communication networks are in constant evolution to adapt to the ever-changing demands of modern services and applications. On the one hand, technologies within the framework of the fifth generation of communication networks (5G), currently in a steady course towards 6G, are being designed with unprecedented performance and flexibility capabilities to empower the digital transformation of heterogeneous vertical industries. On the other hand, Time-Sensitive Networking (TSN) is being developed to enable converged network infrastructures able to satisfy the most stringent communication requirements in regards to bounded low latency and reliability. In verticals such as manufacturing, a joint usage of 5G and TSN is very promising to enable revolutionary use cases that coexist with the constraints imposed by industrial communications and systems. In this paper, we exhaustively survey the technology landscape concerning the integration between 5G and TSN technologies, including the perspectives of both industry and academia. In the first part of this paper, we provide a comprehensive review of relevant standards and outcomes from industry initiatives. We first examine the capabilities provided by TSN and their application to industrial communications. Likewise, we contextualize the 5G architecture and its key enabling technologies, and later provide a comprehensive review of the architecture and mechanisms to enable the integration between TSN and 5G. In the second part of this paper, we extensively survey the state of the art from academic literature concerning proposals that contribute to enabling, improving, and/or demonstrating different aspects of the integration or interworking between 5G and TSN technologies.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"27 1","pages":"259-321"},"PeriodicalIF":34.4,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10584103","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141546037","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 : 2024-06-19DOI: 10.1109/COMST.2024.3417336
Hazem Sallouha;Sharief Saleh;Sibren De Bast;Zhuangzhuang Cui;Sofie Pollin;Henk Wymeersch
The inherent limitations in scaling up ground infrastructure for future wireless networks, combined with decreasing operational costs of aerial and space networks, are driving considerable research interest in multisegment ground-air-space (GAS) networks. In GAS networks, where ground and aerial users share network resources, ubiquitous and accurate user localization becomes indispensable, not only as an end-user service but also as an enabler for location-aware communications. This breaks the convention of having localization as a byproduct in networks primarily designed for communications. To address these imperative localization needs, the design and utilization of ground, aerial, and space anchors require thorough investigation. In this tutorial, we provide an in-depth systemic analysis of the radio localization problem in GAS networks, considering ground and aerial users as targets to be localized. Starting from a survey of the most relevant works, we then define the key characteristics of anchors and targets in GAS networks. Subsequently, we detail localization fundamentals in GAS networks, considering 3D positions, orientations, and velocities. Afterward, we thoroughly analyze radio localization systems in GAS networks, detailing the system model, design aspects, and considerations for each of the three GAS anchors. Preliminary results are presented to provide a quantifiable perspective on key design aspects in GAS-based localization scenarios. We then identify the vital roles 6G enablers are expected to play in radio localization in GAS networks.
{"title":"On the Ground and in the Sky: A Tutorial on Radio Localization in Ground-Air-Space Networks","authors":"Hazem Sallouha;Sharief Saleh;Sibren De Bast;Zhuangzhuang Cui;Sofie Pollin;Henk Wymeersch","doi":"10.1109/COMST.2024.3417336","DOIUrl":"10.1109/COMST.2024.3417336","url":null,"abstract":"The inherent limitations in scaling up ground infrastructure for future wireless networks, combined with decreasing operational costs of aerial and space networks, are driving considerable research interest in multisegment ground-air-space (GAS) networks. In GAS networks, where ground and aerial users share network resources, ubiquitous and accurate user localization becomes indispensable, not only as an end-user service but also as an enabler for location-aware communications. This breaks the convention of having localization as a byproduct in networks primarily designed for communications. To address these imperative localization needs, the design and utilization of ground, aerial, and space anchors require thorough investigation. In this tutorial, we provide an in-depth systemic analysis of the radio localization problem in GAS networks, considering ground and aerial users as targets to be localized. Starting from a survey of the most relevant works, we then define the key characteristics of anchors and targets in GAS networks. Subsequently, we detail localization fundamentals in GAS networks, considering 3D positions, orientations, and velocities. Afterward, we thoroughly analyze radio localization systems in GAS networks, detailing the system model, design aspects, and considerations for each of the three GAS anchors. Preliminary results are presented to provide a quantifiable perspective on key design aspects in GAS-based localization scenarios. We then identify the vital roles 6G enablers are expected to play in radio localization in GAS networks.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"27 1","pages":"218-258"},"PeriodicalIF":34.4,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141944597","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}
In the coming sixth generation (6G) communication era, to provide seamless and ubiquitous connections, the space-air-ground-sea integrated network (SAGSIN) is envisioned to address the challenges of communication coverage in areas with difficult conditions, such as the forest, desert, and sea. Considering the fundamental limitations of the SAGSIN including large-scale scenarios, highly dynamic channels, and limited device capabilities, traditional communications based on Shannon information theory cannot satisfy the communication demands. Moreover, bit-level reconstruction is usually redundant for many human-to-machine or machine-to-machine applications in the SAGSIN. Therefore, it is imperative to consider high-level communications towards semantics exchange, called semantic communications. In this survey, according to the interpretations of the term “semantics”, including “significance”, “meaning”, and “effectiveness-related information”, we review state-of-the-art works on semantic communications from three perspectives, which are 1) significance representation and protection, 2) meaning similarity measurement and meaning enhancement, and 3) ultimate effectiveness and effectiveness yielding. Sequentially, three types of semantic communication systems can be correspondingly introduced, namely the significance-oriented, meaning-oriented, and effectiveness/task-oriented semantic communication systems. Implementation of the above three types of systems in the SAGSIN necessitates a new perception-communication-computing-actuation-integrated paradigm (PCCAIP), where all the available perception, computing, and actuation techniques jointly facilitate significance-oriented sampling & transmission, semantic extraction & reconstruction, and task decision. Finally, we point out some future challenges on semantic communications in the SAGSIN. This survey provides a comprehensive review on the future semantic communications in the SAGSIN, and elaborates on the performance metrics and techniques related to semantic communications for references and further in-depth investigations.
{"title":"Semantics-Empowered Space-Air-Ground-Sea Integrated Network: New Paradigm, Frameworks, and Challenges","authors":"Siqi Meng;Shaohua Wu;Jiaming Zhang;Junlan Cheng;Haibo Zhou;Qinyu Zhang","doi":"10.1109/COMST.2024.3416309","DOIUrl":"10.1109/COMST.2024.3416309","url":null,"abstract":"In the coming sixth generation (6G) communication era, to provide seamless and ubiquitous connections, the space-air-ground-sea integrated network (SAGSIN) is envisioned to address the challenges of communication coverage in areas with difficult conditions, such as the forest, desert, and sea. Considering the fundamental limitations of the SAGSIN including large-scale scenarios, highly dynamic channels, and limited device capabilities, traditional communications based on Shannon information theory cannot satisfy the communication demands. Moreover, bit-level reconstruction is usually redundant for many human-to-machine or machine-to-machine applications in the SAGSIN. Therefore, it is imperative to consider high-level communications towards semantics exchange, called semantic communications. In this survey, according to the interpretations of the term “semantics”, including “significance”, “meaning”, and “effectiveness-related information”, we review state-of-the-art works on semantic communications from three perspectives, which are 1) significance representation and protection, 2) meaning similarity measurement and meaning enhancement, and 3) ultimate effectiveness and effectiveness yielding. Sequentially, three types of semantic communication systems can be correspondingly introduced, namely the significance-oriented, meaning-oriented, and effectiveness/task-oriented semantic communication systems. Implementation of the above three types of systems in the SAGSIN necessitates a new perception-communication-computing-actuation-integrated paradigm (PCCAIP), where all the available perception, computing, and actuation techniques jointly facilitate significance-oriented sampling & transmission, semantic extraction & reconstruction, and task decision. Finally, we point out some future challenges on semantic communications in the SAGSIN. This survey provides a comprehensive review on the future semantic communications in the SAGSIN, and elaborates on the performance metrics and techniques related to semantic communications for references and further in-depth investigations.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"27 1","pages":"140-183"},"PeriodicalIF":34.4,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141944598","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 : 2024-06-11DOI: 10.1109/COMST.2024.3409556
Hao Kong;Cheng Huang;Jiadi Yu;Xuemin Shen
Sensing technology plays a crucial role in bridging the physical and digital worlds. By transforming a multitude of physical phenomena into digital data, it significantly enhances our understanding of the environment and is instrumental in a wide range of applications. Given the wide bandwidth and short wavelength characteristics, millimeter wave (mmWave) radar sensing is considered one of the most promising sensing techniques beyond mmWave communication. In this paper, we provide a comprehensive survey of mmWave radar-based sensing techniques and applications in autonomous vehicles, smart homes, and industry. Specifically, we first review widely exploited mmWave radar techniques and signal processing techniques from the perspective of dedicated radars and communication integration, which are the basis of mmWave radar sensing. Then, we introduce mainstream machine learning techniques, especially the latest deep learning techniques for designing applications with mmWave signals. Related hardware devices, available public datasets, and evaluation metrics are also presented. Afterward, we provide a taxonomy of emerging mmWave radar sensing applications, and review the developments in object detection, ego-motion estimation, simultaneous localization and mapping, activity recognition, pose estimation, gesture recognition, speech recognition, vital sign monitoring, user authentication, indoor positioning, industrial imaging, industrial measurement, environmental monitoring, etc. We conclude the paper by discussing challenges and potential future research directions.
{"title":"A Survey of mmWave Radar-Based Sensing in Autonomous Vehicles, Smart Homes and Industry","authors":"Hao Kong;Cheng Huang;Jiadi Yu;Xuemin Shen","doi":"10.1109/COMST.2024.3409556","DOIUrl":"10.1109/COMST.2024.3409556","url":null,"abstract":"Sensing technology plays a crucial role in bridging the physical and digital worlds. By transforming a multitude of physical phenomena into digital data, it significantly enhances our understanding of the environment and is instrumental in a wide range of applications. Given the wide bandwidth and short wavelength characteristics, millimeter wave (mmWave) radar sensing is considered one of the most promising sensing techniques beyond mmWave communication. In this paper, we provide a comprehensive survey of mmWave radar-based sensing techniques and applications in autonomous vehicles, smart homes, and industry. Specifically, we first review widely exploited mmWave radar techniques and signal processing techniques from the perspective of dedicated radars and communication integration, which are the basis of mmWave radar sensing. Then, we introduce mainstream machine learning techniques, especially the latest deep learning techniques for designing applications with mmWave signals. Related hardware devices, available public datasets, and evaluation metrics are also presented. Afterward, we provide a taxonomy of emerging mmWave radar sensing applications, and review the developments in object detection, ego-motion estimation, simultaneous localization and mapping, activity recognition, pose estimation, gesture recognition, speech recognition, vital sign monitoring, user authentication, indoor positioning, industrial imaging, industrial measurement, environmental monitoring, etc. We conclude the paper by discussing challenges and potential future research directions.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"27 1","pages":"463-508"},"PeriodicalIF":34.4,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141944600","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 : 2024-06-11DOI: 10.1109/COMST.2024.3412852
Christina Chaccour;Walid Saad;Mérouane Debbah;Zhu Han;H. Vincent Poor
Semantic communication is viewed as a revolutionary paradigm that can potentially transform how we design and operate wireless communication systems. However, despite a recent surge of research activities in this area, remarkably, the research landscape is still limited in at least three ways. First and foremost, the definition of a “semantic communication system” is ambiguous and varies widely between different studies. This lack of consensus makes it challenging to develop rigorous and scalable frameworks for building semantic communication networks. Secondly, current approaches to building semantic communication networks are limited by their reliance on data-driven and information-driven AI-augmented networks. These networks remain “tied” to the data, which limits their ability to perform versatile logic. In contrast, knowledge-driven and reasoning-driven AI-native networks would allow for more flexible and powerful communication capabilities. However, there is currently a lack of technical foundations to support such networks. Thirdly, the concept of “semantic representation” is not well understood yet, and its role in embedding meaning and structure in data transferred across wireless network is still a subject of active research. The development of semantic representations that are minimalist, generalizable, and efficient is critical to enabling the transmitter and receiver to generate content via a minimally semantic representation. To address these limitations, in this tutorial, we propose the first rigorous and holistic vision of an end-to-end semantic communication network that is founded on novel concepts from artificial intelligence (AI), causal reasoning, transfer learning, and minimum description length theory. We first discuss how the design of semantic communication networks requires a move from data-driven AI-augmented networks, in which wireless networks remain “tied” to data, towards reasoning-driven AI-native networks which can perform versatile logic and generalizable intelligence. We then distinguish the concept of semantic communications from several other approaches that have been conflated with it. We opine that building effective and efficient semantic communication systems necessitates surpassing the creation of new encoder and decoder types at the transmitter/receiver side, or developing an “AI for wireless” framework that only extracts application features or fine-tunes wireless protocols/algorithms. Then, we identify the main tenets that are needed to build an end-to-end semantic communication network. Among those building blocks of a semantic communication network, we highlight the necessity of creating semantic representations of data that satisfy the key properties of minimalism, generalizability, and efficiency so as to faithfully represent the data and enable the transmitter and receiver to do more with less. We then explain how those representations can form the basis of a so-called semantic language that will
{"title":"Less Data, More Knowledge: Building Next-Generation Semantic Communication Networks","authors":"Christina Chaccour;Walid Saad;Mérouane Debbah;Zhu Han;H. Vincent Poor","doi":"10.1109/COMST.2024.3412852","DOIUrl":"10.1109/COMST.2024.3412852","url":null,"abstract":"Semantic communication is viewed as a revolutionary paradigm that can potentially transform how we design and operate wireless communication systems. However, despite a recent surge of research activities in this area, remarkably, the research landscape is still limited in at least three ways. First and foremost, the definition of a “semantic communication system” is ambiguous and varies widely between different studies. This lack of consensus makes it challenging to develop rigorous and scalable frameworks for building semantic communication networks. Secondly, current approaches to building semantic communication networks are limited by their reliance on data-driven and information-driven AI-augmented networks. These networks remain “tied” to the data, which limits their ability to perform versatile logic. In contrast, knowledge-driven and reasoning-driven AI-native networks would allow for more flexible and powerful communication capabilities. However, there is currently a lack of technical foundations to support such networks. Thirdly, the concept of “semantic representation” is not well understood yet, and its role in embedding meaning and structure in data transferred across wireless network is still a subject of active research. The development of semantic representations that are minimalist, generalizable, and efficient is critical to enabling the transmitter and receiver to generate content via a minimally semantic representation. To address these limitations, in this tutorial, we propose the first rigorous and holistic vision of an end-to-end semantic communication network that is founded on novel concepts from artificial intelligence (AI), causal reasoning, transfer learning, and minimum description length theory. We first discuss how the design of semantic communication networks requires a move from data-driven AI-augmented networks, in which wireless networks remain “tied” to data, towards reasoning-driven AI-native networks which can perform versatile logic and generalizable intelligence. We then distinguish the concept of semantic communications from several other approaches that have been conflated with it. We opine that building effective and efficient semantic communication systems necessitates surpassing the creation of new encoder and decoder types at the transmitter/receiver side, or developing an “AI for wireless” framework that only extracts application features or fine-tunes wireless protocols/algorithms. Then, we identify the main tenets that are needed to build an end-to-end semantic communication network. Among those building blocks of a semantic communication network, we highlight the necessity of creating semantic representations of data that satisfy the key properties of minimalism, generalizability, and efficiency so as to faithfully represent the data and enable the transmitter and receiver to do more with less. We then explain how those representations can form the basis of a so-called semantic language that will ","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"27 1","pages":"37-76"},"PeriodicalIF":34.4,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10554663","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141944602","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 : 2024-06-06DOI: 10.1109/COMST.2024.3410295
Wajid Rafique;Joyeeta Rani Barai;Abraham O. Fapojuwo;Diwakar Krishnamurthy
Beyond fifth generation (B5G) is expected to tremendously improve network capabilities by using a higher frequency band compared to 5G, capable of delivering higher network capacity with much lower latency. It is expected that there will be around 30 billion connected objects by 2030, approximately 3.5 times the population then which underscores the pressing need for advanced network capabilities to support diverse applications ranging from smart transportation and energy management to healthcare and public safety. Network slicing enables sharing of network resources by transforming the physical network into logically independent networks, each specifically tailored to meet the requirements of heterogeneous services (e.g., Internet of Things applications, gaming services, holographic communication). Each slice is an end-to-end logical network comprising network, compute, and storage resources. Softwarization and virtualization are the main drivers for innovation in B5G, enabling network developers and operators to develop network-aware applications to match customer demands. Smart cities vertical offers unique service characteristics, performance requirements, and technical challenges in B5G network slicing. Therefore, this paper provides a comprehensive survey on B5G network slicing use cases, synergies, practical implementations and applications based on their quality of service parameters for smart cities applications. The paper gives a detailed taxonomy of the B5G network slicing framework requirements, design, dynamic intra-slice and inter-slice resource allocation techniques, management and orchestration, artificial intelligence/machine learning-empowered network slicing designs, implementation testbeds, 3GPP specifications and projects/standards for B5G network slicing. Furthermore, the paper provides a thorough discussion on the technical challenges that can arise when implementing B5G network slicing for smart cities applications and offers potential solutions. Finally, the paper discusses B5G network slicing current and future research directions for smart cities applications.
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