Satellite communications, pivotal for global connectivity, are increasingly converging with cutting-edge mobile networks, notably 5G, B5G, and 6G. This amalgamation heralds the promise of universal, high-velocity comm...Satellite communications, pivotal for global connectivity, are increasingly converging with cutting-edge mobile networks, notably 5G, B5G, and 6G. This amalgamation heralds the promise of universal, high-velocity communication, yet it is not without its challenges. Paramount concerns encompass spectrum allocation, the harmonization of network architectures, and inherent latency issues in satellite transmissions. Potential mitigations, such as dynamic spectrum sharing and the deployment of edge computing, are explored as viable solutions. Looking ahead, the advent of quantum communications within satellite frameworks and the integration of AI spotlight promising research trajectories. These advancements aim to foster a seamless and synergistic coexistence between satellite communications and next-gen mobile networks.展开更多
The evolution of global mobile data over the past decades in broadcasting, Internet of Things (IoT), education, healthcare, commerce, and energy has put strong pressure on 3G/4G mobile networks to improve their servic...The evolution of global mobile data over the past decades in broadcasting, Internet of Things (IoT), education, healthcare, commerce, and energy has put strong pressure on 3G/4G mobile networks to improve their service offerings. These generations of mobile networks were initially invented to meet the requirements of the above-mentioned applications. However, as the requirements in these applications continue to increase, new mobile technologies such as 5G (fifth generation), 5G and beyond (B5G, beyond fifth generation), and 6G (sixth generation) are still progressing and being experimented. These networks are very heterogeneous generations of mobile networks that will have to offer very high throughput per user, good energy efficiency, better traffic capacity per area, improved spectral efficiency, very low latency, and high mobility. To meet these requirements, the radio interface of future mobile networks will have to be flexible and rationalized the available frequency resources. Therefore, new modulation methods, access techniques and waveforms capable of supporting these technological changes are proposed. This review presents brief descriptions of the types of 5G, B5G, and 6G waveforms. The 5G consists of OFDM including its transmission techniques: generalized frequency division multiplexing (GFDM), filter bank based multi-carrier (FBMC), universal filtered multi-carrier (UFMC), and index modulation (IM). Meanwhile, the 6G covers orthogonal time frequency space (OTFS), orthogonal chirp division multiplexing (OCDM) and orthogonal time sequence multiplexing (OTSM). The networks’ potentialities, advantages, disadvantages, and future directions are outlined.展开更多
Key challenges for 5G and Beyond networks relate with the requirements for exceptionally low latency, high reliability, and extremely high data rates. The Ultra-Reliable Low Latency Communication (URLLC) use case is t...Key challenges for 5G and Beyond networks relate with the requirements for exceptionally low latency, high reliability, and extremely high data rates. The Ultra-Reliable Low Latency Communication (URLLC) use case is the trickiest to support and current research is focused on physical or MAC layer solutions, while proposals focused on the network layer using Machine Learning (ML) and Artificial Intelligence (AI) algorithms running on base stations and User Equipment (UE) or Internet of Things (IoT) devices are in early stages. In this paper, we describe the operation rationale of the most recent relevant ML algorithms and techniques, and we propose and validate ML algorithms running on both cells (base stations/gNBs) and UEs or IoT devices to handle URLLC service control. One ML algorithm runs on base stations to evaluate latency demands and offload traffic in case of need, while another lightweight algorithm runs on UEs and IoT devices to rank cells with the best URLLC service in real-time to indicate the best one cell for a UE or IoT device to camp. We show that the interplay of these algorithms leads to good service control and eventually optimal load allocation, under slow load mobility. .展开更多
目前,各运营商仅在Sub-6 GHz频段建设基站,并未实现频率范围2(Frequency Range 2,FR2)的5G商业部署。相较于中国电信股份有限公司江苏分公司现网部署的3.5 GHz频段,毫米波频段较高,基站需部署更多天线来获得更多的波束增益,以弥补FR2频...目前,各运营商仅在Sub-6 GHz频段建设基站,并未实现频率范围2(Frequency Range 2,FR2)的5G商业部署。相较于中国电信股份有限公司江苏分公司现网部署的3.5 GHz频段,毫米波频段较高,基站需部署更多天线来获得更多的波束增益,以弥补FR2频段的高路径损耗。因为通信波束较窄,基站与终端只有使用最佳波束对通信才可获得较高的通信速率,所以高效的波束搜索方案必不可少。在后5G(Beyond 5G,B5G)场景下提出一种波束搜索方法,相较于遍历搜索,可在复杂度降低99.999%的同时获得较高的频谱效率。展开更多
With the help of network densification,network coverage as well as the throughput can be improved via ultra-dense networks(UDNs).In tandem,Unmanned Aerial Vehicle(UAV)communications have recently garnered much attenti...With the help of network densification,network coverage as well as the throughput can be improved via ultra-dense networks(UDNs).In tandem,Unmanned Aerial Vehicle(UAV)communications have recently garnered much attention because of their high agility as well as widespread applications.In this paper,a cognitive UAV is proposed for wireless nodes power pertaining to the IoT ground terminal.Further,the UAV is included in the IoT system as the source of power for the wireless nodes as well as for resource allocation.The quality of service(QoS)related to the cognitive node was considered as a utility function based on pricing scheme that was modelled as a non-cooperative game theory in order to maximise users’net utility function.Moreover,an energy efficiency non-cooperative game theory power allocation with pricing scheme(EE-NGPAP)is proposed to obtain an efficient power control within IoT wireless nodes.Further,uniqueness and existence of the Nash equilibrium have been demonstrated mathematically and through simulation.Simulation results show that the proposed energy harvest algorithm demonstrated considerable decrease in transmitted power consumption in terms of average power reduction,which is regarded to be apt with the 5Gnetworks’vision.Finally,the proposed algorithm requires around 4 iterations only to converge to NE which makes the algorithm more suitable in practical heterogeneous scenarios.展开更多
Joint radar and communication(JRC)technology has become important for civil and military applications for decades.This paper introduces the concepts,characteristics and advantages of JRC technology,presenting the typi...Joint radar and communication(JRC)technology has become important for civil and military applications for decades.This paper introduces the concepts,characteristics and advantages of JRC technology,presenting the typical applications that have benefited from JRC technology currently and in the future.This paper explores the state-of-the-art of JRC in the levels of coexistence,cooperation,co-design and collaboration.Compared to previous surveys,this paper reviews the entire trends that drive the development of radar sensing and wireless communication using JRC.Specifically,we explore an open research issue on radar and communication operating with mutual benefits based on collaboration,which represents the fourth stage of JRC evolution.This paper provides useful perspectives for future researches of JRC technology.展开更多
How to explore and exploit the full potential of artificial intelligence(AI)technologies in future wireless communications such as beyond 5G(B5G)and 6G is an extremely hot inter-disciplinary research topic around the ...How to explore and exploit the full potential of artificial intelligence(AI)technologies in future wireless communications such as beyond 5G(B5G)and 6G is an extremely hot inter-disciplinary research topic around the world.On the one hand,AI empowers intelligent resource management for wireless communications through powerful learning and automatic adaptation capabilities.On the other hand,embracing AI in wireless communication resource management calls for new network architecture and system models as well as standardized interfaces/protocols/data formats to facilitate the large-scale deployment of AI in future B5G/6G networks.This paper reviews the state-of-art AI-empowered resource management from the framework perspective down to the methodology perspective,not only considering the radio resource(e.g.,spectrum)management but also other types of resources such as computing and caching.We also discuss the challenges and opportunities for AI-based resource management to widely deploy AI in future wireless communication networks.展开更多
文摘Satellite communications, pivotal for global connectivity, are increasingly converging with cutting-edge mobile networks, notably 5G, B5G, and 6G. This amalgamation heralds the promise of universal, high-velocity communication, yet it is not without its challenges. Paramount concerns encompass spectrum allocation, the harmonization of network architectures, and inherent latency issues in satellite transmissions. Potential mitigations, such as dynamic spectrum sharing and the deployment of edge computing, are explored as viable solutions. Looking ahead, the advent of quantum communications within satellite frameworks and the integration of AI spotlight promising research trajectories. These advancements aim to foster a seamless and synergistic coexistence between satellite communications and next-gen mobile networks.
文摘The evolution of global mobile data over the past decades in broadcasting, Internet of Things (IoT), education, healthcare, commerce, and energy has put strong pressure on 3G/4G mobile networks to improve their service offerings. These generations of mobile networks were initially invented to meet the requirements of the above-mentioned applications. However, as the requirements in these applications continue to increase, new mobile technologies such as 5G (fifth generation), 5G and beyond (B5G, beyond fifth generation), and 6G (sixth generation) are still progressing and being experimented. These networks are very heterogeneous generations of mobile networks that will have to offer very high throughput per user, good energy efficiency, better traffic capacity per area, improved spectral efficiency, very low latency, and high mobility. To meet these requirements, the radio interface of future mobile networks will have to be flexible and rationalized the available frequency resources. Therefore, new modulation methods, access techniques and waveforms capable of supporting these technological changes are proposed. This review presents brief descriptions of the types of 5G, B5G, and 6G waveforms. The 5G consists of OFDM including its transmission techniques: generalized frequency division multiplexing (GFDM), filter bank based multi-carrier (FBMC), universal filtered multi-carrier (UFMC), and index modulation (IM). Meanwhile, the 6G covers orthogonal time frequency space (OTFS), orthogonal chirp division multiplexing (OCDM) and orthogonal time sequence multiplexing (OTSM). The networks’ potentialities, advantages, disadvantages, and future directions are outlined.
文摘Key challenges for 5G and Beyond networks relate with the requirements for exceptionally low latency, high reliability, and extremely high data rates. The Ultra-Reliable Low Latency Communication (URLLC) use case is the trickiest to support and current research is focused on physical or MAC layer solutions, while proposals focused on the network layer using Machine Learning (ML) and Artificial Intelligence (AI) algorithms running on base stations and User Equipment (UE) or Internet of Things (IoT) devices are in early stages. In this paper, we describe the operation rationale of the most recent relevant ML algorithms and techniques, and we propose and validate ML algorithms running on both cells (base stations/gNBs) and UEs or IoT devices to handle URLLC service control. One ML algorithm runs on base stations to evaluate latency demands and offload traffic in case of need, while another lightweight algorithm runs on UEs and IoT devices to rank cells with the best URLLC service in real-time to indicate the best one cell for a UE or IoT device to camp. We show that the interplay of these algorithms leads to good service control and eventually optimal load allocation, under slow load mobility. .
基金supported by the School of Computing,Faculty of Engineering,Universiti Teknologi Malaysia(UTM)and funded by the PRGS Project(Grant ID:R.J130000.7806.4L706).
文摘With the help of network densification,network coverage as well as the throughput can be improved via ultra-dense networks(UDNs).In tandem,Unmanned Aerial Vehicle(UAV)communications have recently garnered much attention because of their high agility as well as widespread applications.In this paper,a cognitive UAV is proposed for wireless nodes power pertaining to the IoT ground terminal.Further,the UAV is included in the IoT system as the source of power for the wireless nodes as well as for resource allocation.The quality of service(QoS)related to the cognitive node was considered as a utility function based on pricing scheme that was modelled as a non-cooperative game theory in order to maximise users’net utility function.Moreover,an energy efficiency non-cooperative game theory power allocation with pricing scheme(EE-NGPAP)is proposed to obtain an efficient power control within IoT wireless nodes.Further,uniqueness and existence of the Nash equilibrium have been demonstrated mathematically and through simulation.Simulation results show that the proposed energy harvest algorithm demonstrated considerable decrease in transmitted power consumption in terms of average power reduction,which is regarded to be apt with the 5Gnetworks’vision.Finally,the proposed algorithm requires around 4 iterations only to converge to NE which makes the algorithm more suitable in practical heterogeneous scenarios.
基金supported by the National Natural Science Foundation of China (No. 61631003, 61601055)the National Science Fund for Distinguished Young Scholars (No. 61525101)
文摘Joint radar and communication(JRC)technology has become important for civil and military applications for decades.This paper introduces the concepts,characteristics and advantages of JRC technology,presenting the typical applications that have benefited from JRC technology currently and in the future.This paper explores the state-of-the-art of JRC in the levels of coexistence,cooperation,co-design and collaboration.Compared to previous surveys,this paper reviews the entire trends that drive the development of radar sensing and wireless communication using JRC.Specifically,we explore an open research issue on radar and communication operating with mutual benefits based on collaboration,which represents the fourth stage of JRC evolution.This paper provides useful perspectives for future researches of JRC technology.
文摘How to explore and exploit the full potential of artificial intelligence(AI)technologies in future wireless communications such as beyond 5G(B5G)and 6G is an extremely hot inter-disciplinary research topic around the world.On the one hand,AI empowers intelligent resource management for wireless communications through powerful learning and automatic adaptation capabilities.On the other hand,embracing AI in wireless communication resource management calls for new network architecture and system models as well as standardized interfaces/protocols/data formats to facilitate the large-scale deployment of AI in future B5G/6G networks.This paper reviews the state-of-art AI-empowered resource management from the framework perspective down to the methodology perspective,not only considering the radio resource(e.g.,spectrum)management but also other types of resources such as computing and caching.We also discuss the challenges and opportunities for AI-based resource management to widely deploy AI in future wireless communication networks.
