准确、高效的业务流识别与分类是保障多媒体通信端到端QoS(Quality of Service)、执行相关网络操作的前提。但多媒体通信业务构成复杂、具有较严格的QoS约束,且在包/流水平统计特征多样性,业务统计特征有效选取直接关系到识别和分类方...准确、高效的业务流识别与分类是保障多媒体通信端到端QoS(Quality of Service)、执行相关网络操作的前提。但多媒体通信业务构成复杂、具有较严格的QoS约束,且在包/流水平统计特征多样性,业务统计特征有效选取直接关系到识别和分类方法的有效性。在介绍相关研究成果的基础上,文中从业务特征角度对现有技术进行分类,进而对比各类方法的性能,同时在探讨当前业务流识别方法存在对新业务识别准确度不高、实时性不足等问题的基础上,结合跨域QoS类映射弹性需求的特点,给出跨域QoS类映射中多媒体业务识别架构。整个架构的目标是准确、高效地识别多媒体流,为聚集流的形成做好前期准备,为保障高效的端到端QoS提供技术支撑。最后,总结了发展趋势和面临的挑战。展开更多
This study explores the integration of predictive analytics in strategic corporate communications, with a specific focus on stakeholder engagement and crisis management. Our mixed-methods approach, which combines a co...This study explores the integration of predictive analytics in strategic corporate communications, with a specific focus on stakeholder engagement and crisis management. Our mixed-methods approach, which combines a comprehensive literature review with case studies of five multinational corporations, allows us to investigate the applications, challenges, and ethical implications of leveraging predictive models in communication strategies. While our findings reveal significant potential for enhancing personalized content delivery, real-time sentiment analysis, and proactive crisis management, we stress the need for careful consideration of challenges such as data privacy concerns and algorithmic bias. This emphasis on ethical implementation is crucial in navigating the complex landscape of predictive analytics in corporate communications. To address these issues, we propose a framework that prioritizes ethical considerations. Furthermore, we identify key areas for future research, thereby contributing to the evolving field of data-driven communication management.展开更多
This comprehensive study investigates the multifaceted impact of AI-powered personalization on strategic communications, delving deeply into its opportunities, challenges, and future directions. Employing a rigorous m...This comprehensive study investigates the multifaceted impact of AI-powered personalization on strategic communications, delving deeply into its opportunities, challenges, and future directions. Employing a rigorous mixed-methods approach, we conduct an in-depth analysis of the effects of AI-driven personalization on audience engagement, brand perception, and conversion rates across various industries and communication channels. Our findings reveal that while AI-powered personalization significantly enhances communication effectiveness and offers unprecedented opportunities for audience connection, it also raises critical ethical considerations and implementation challenges. The study contributes substantially to the growing body of literature on AI in communications, offering both theoretical insights and practical guidelines for professionals navigating this rapidly evolving landscape. Furthermore, we propose a novel framework for ethical AI implementation in strategic communications and outline a robust agenda for future research in this dynamic field.展开更多
In the realm of satellite communication,where the importance of efficient spectrum utilization is growing day by day due to the increasing significance of this technology,dynamic resource management has emerged as a p...In the realm of satellite communication,where the importance of efficient spectrum utilization is growing day by day due to the increasing significance of this technology,dynamic resource management has emerged as a pivotal consideration in the design of contemporary multi-beam satellites,facilitating the flexible allocation of resources based on user demand.This research paper delves into the pivotal role played by machine learning and artificial intelligence within the domain of satellite communication,particularly focusing on spot beam satellites.The study encompasses an evaluation of machine learning’s application,whereby an extensive dataset capturing user demand across a specific geographical area is subjected to analysis.This analysis involves determining the optimal number of beams/clusters,achieved through the utilization of the knee-elbow method predicated on within-cluster sum of squares.Subsequently,the demand data are equitably segmented employing the weighted k-means clustering technique.The proposed solution introduces a straightforward yet efficient model for bandwidth allocation,contrasting with conventional fixed beam illumination models.This approach not only enhances spectrum utilization but also leads to noteworthy power savings,thereby addressing the growing importance of efficient resource management in satellite communication.展开更多
The applications of artificial intelligence(AI)and machine learning(ML)technologies in wireless communications have drawn significant attention recently.AI has demonstrated real success in speech understanding,image i...The applications of artificial intelligence(AI)and machine learning(ML)technologies in wireless communications have drawn significant attention recently.AI has demonstrated real success in speech understanding,image identification,and natural language processing domains,thus exhibiting its great potential in solving problems that cannot be easily modeled.AI techniques have become an enabler in wireless communications to fulfill the increasing and diverse requirements across a large range of application scenarios.In this paper,we elaborate on several typical wireless scenarios,such as channel modeling,channel decoding and signal detection,and channel coding design,in which AI plays an important role in wireless communications.Then,AI and information theory are discussed from the viewpoint of the information bottleneck.Finally,we discuss some ideas about how AI techniques can be deeply integrated with wireless communication systems.展开更多
Nowadays,the emerging paradigm of semantic communications seems to offer an attractive opportunity to improve the transmission reliability and efficiency in new generation communication systems.In particular,focusing ...Nowadays,the emerging paradigm of semantic communications seems to offer an attractive opportunity to improve the transmission reliability and efficiency in new generation communication systems.In particular,focusing on spectrum scarcity,expected to afflict the upcoming sixth generation(6G)networks,this paper analyses the semantic communications behavior in the context of a cell-dense scenario,in which users belonging to different small base station areas may be allocated on a same channel giving rise to a non-negligible interference that severely affects the communications reliability.In such a context,artificial intelligence methodologies are of paramount importance in order to speed up the switch from traditional communication to the novel semantic communication paradigm.As a consequence,a deep-convolution neural networks based encoder-decoder architecture has been exploited here in the definition of the proposed semantic communications framework.Finally,extensive numerical simulations have been performed to test the advantages of the proposed framework in different interfering scenarios and in comparison with different traditional or semantic alternatives.展开更多
With a ten-year horizon from concept to reality, it is time now to start thinking about what will the sixth-generation(6G) mobile communications be on the eve of the fifth-generation(5G) deployment. To pave the way fo...With a ten-year horizon from concept to reality, it is time now to start thinking about what will the sixth-generation(6G) mobile communications be on the eve of the fifth-generation(5G) deployment. To pave the way for the development of 6G and beyond, we provide 6G visions in this paper. We first introduce the state-of-the-art technologies in 5G and indicate the necessity to study 6G. By taking the current and emerging development of wireless communications into consideration, we envision 6G to include three major aspects, namely, mobile ultra-broadband, super Internet-of-Things(IoT), and artificial intelligence(AI). Then, we review key technologies to realize each aspect. In particular, teraherz(THz) communications can be used to support mobile ultra-broadband, symbiotic radio and satellite-assisted communications can be used to achieve super IoT, and machine learning techniques are promising candidates for AI. For each technology, we provide the basic principle, key challenges, and state-of-the-art approaches and solutions.展开更多
文摘准确、高效的业务流识别与分类是保障多媒体通信端到端QoS(Quality of Service)、执行相关网络操作的前提。但多媒体通信业务构成复杂、具有较严格的QoS约束,且在包/流水平统计特征多样性,业务统计特征有效选取直接关系到识别和分类方法的有效性。在介绍相关研究成果的基础上,文中从业务特征角度对现有技术进行分类,进而对比各类方法的性能,同时在探讨当前业务流识别方法存在对新业务识别准确度不高、实时性不足等问题的基础上,结合跨域QoS类映射弹性需求的特点,给出跨域QoS类映射中多媒体业务识别架构。整个架构的目标是准确、高效地识别多媒体流,为聚集流的形成做好前期准备,为保障高效的端到端QoS提供技术支撑。最后,总结了发展趋势和面临的挑战。
文摘This study explores the integration of predictive analytics in strategic corporate communications, with a specific focus on stakeholder engagement and crisis management. Our mixed-methods approach, which combines a comprehensive literature review with case studies of five multinational corporations, allows us to investigate the applications, challenges, and ethical implications of leveraging predictive models in communication strategies. While our findings reveal significant potential for enhancing personalized content delivery, real-time sentiment analysis, and proactive crisis management, we stress the need for careful consideration of challenges such as data privacy concerns and algorithmic bias. This emphasis on ethical implementation is crucial in navigating the complex landscape of predictive analytics in corporate communications. To address these issues, we propose a framework that prioritizes ethical considerations. Furthermore, we identify key areas for future research, thereby contributing to the evolving field of data-driven communication management.
文摘This comprehensive study investigates the multifaceted impact of AI-powered personalization on strategic communications, delving deeply into its opportunities, challenges, and future directions. Employing a rigorous mixed-methods approach, we conduct an in-depth analysis of the effects of AI-driven personalization on audience engagement, brand perception, and conversion rates across various industries and communication channels. Our findings reveal that while AI-powered personalization significantly enhances communication effectiveness and offers unprecedented opportunities for audience connection, it also raises critical ethical considerations and implementation challenges. The study contributes substantially to the growing body of literature on AI in communications, offering both theoretical insights and practical guidelines for professionals navigating this rapidly evolving landscape. Furthermore, we propose a novel framework for ethical AI implementation in strategic communications and outline a robust agenda for future research in this dynamic field.
文摘In the realm of satellite communication,where the importance of efficient spectrum utilization is growing day by day due to the increasing significance of this technology,dynamic resource management has emerged as a pivotal consideration in the design of contemporary multi-beam satellites,facilitating the flexible allocation of resources based on user demand.This research paper delves into the pivotal role played by machine learning and artificial intelligence within the domain of satellite communication,particularly focusing on spot beam satellites.The study encompasses an evaluation of machine learning’s application,whereby an extensive dataset capturing user demand across a specific geographical area is subjected to analysis.This analysis involves determining the optimal number of beams/clusters,achieved through the utilization of the knee-elbow method predicated on within-cluster sum of squares.Subsequently,the demand data are equitably segmented employing the weighted k-means clustering technique.The proposed solution introduces a straightforward yet efficient model for bandwidth allocation,contrasting with conventional fixed beam illumination models.This approach not only enhances spectrum utilization but also leads to noteworthy power savings,thereby addressing the growing importance of efficient resource management in satellite communication.
文摘The applications of artificial intelligence(AI)and machine learning(ML)technologies in wireless communications have drawn significant attention recently.AI has demonstrated real success in speech understanding,image identification,and natural language processing domains,thus exhibiting its great potential in solving problems that cannot be easily modeled.AI techniques have become an enabler in wireless communications to fulfill the increasing and diverse requirements across a large range of application scenarios.In this paper,we elaborate on several typical wireless scenarios,such as channel modeling,channel decoding and signal detection,and channel coding design,in which AI plays an important role in wireless communications.Then,AI and information theory are discussed from the viewpoint of the information bottleneck.Finally,we discuss some ideas about how AI techniques can be deeply integrated with wireless communication systems.
基金This work was supported by the PNRR-Mission 4-Next Generation EU 1.3-contract PE0000001-research and innovation on future telecommunications systems and networks,to make Italy more smart.
文摘Nowadays,the emerging paradigm of semantic communications seems to offer an attractive opportunity to improve the transmission reliability and efficiency in new generation communication systems.In particular,focusing on spectrum scarcity,expected to afflict the upcoming sixth generation(6G)networks,this paper analyses the semantic communications behavior in the context of a cell-dense scenario,in which users belonging to different small base station areas may be allocated on a same channel giving rise to a non-negligible interference that severely affects the communications reliability.In such a context,artificial intelligence methodologies are of paramount importance in order to speed up the switch from traditional communication to the novel semantic communication paradigm.As a consequence,a deep-convolution neural networks based encoder-decoder architecture has been exploited here in the definition of the proposed semantic communications framework.Finally,extensive numerical simulations have been performed to test the advantages of the proposed framework in different interfering scenarios and in comparison with different traditional or semantic alternatives.
基金supported in part by National Natural Science Foundation of China under Grants 61631005, 61801101, U1801261, and 61571100
文摘With a ten-year horizon from concept to reality, it is time now to start thinking about what will the sixth-generation(6G) mobile communications be on the eve of the fifth-generation(5G) deployment. To pave the way for the development of 6G and beyond, we provide 6G visions in this paper. We first introduce the state-of-the-art technologies in 5G and indicate the necessity to study 6G. By taking the current and emerging development of wireless communications into consideration, we envision 6G to include three major aspects, namely, mobile ultra-broadband, super Internet-of-Things(IoT), and artificial intelligence(AI). Then, we review key technologies to realize each aspect. In particular, teraherz(THz) communications can be used to support mobile ultra-broadband, symbiotic radio and satellite-assisted communications can be used to achieve super IoT, and machine learning techniques are promising candidates for AI. For each technology, we provide the basic principle, key challenges, and state-of-the-art approaches and solutions.
