5G is a new generation of mobile networking that aims to achieve unparalleled speed and performance. To accomplish this, three technologies, Device-to-Device communication (D2D), multi-access edge computing (MEC) and ...5G is a new generation of mobile networking that aims to achieve unparalleled speed and performance. To accomplish this, three technologies, Device-to-Device communication (D2D), multi-access edge computing (MEC) and network function virtualization (NFV) with ClickOS, have been a significant part of 5G, and this paper mainly discusses them. D2D enables direct communication between devices without the relay of base station. In 5G, a two-tier cellular network composed of traditional cellular network system and D2D is an efficient method for realizing high-speed communication. MEC unloads work from end devices and clouds platforms to widespread nodes, and connects the nodes together with outside devices and third-party providers, in order to diminish the overloading effect on any device caused by enormous applications and improve users’ quality of experience (QoE). There is also a NFV method in order to fulfill the 5G requirements. In this part, an optimized virtual machine for middle-boxes named ClickOS is introduced, and it is evaluated in several aspects. Some middle boxes are being implemented in the ClickOS and proved to have outstanding performances.展开更多
将机器人采集到的温度、湿度和位置等数据信息传送至云平台服务器,便于云端数据分析、机器人数据共享以及人机信息交互等。本文应用第4代移动通信技术(the 4th generation mobile communication technology,4G)无线传输技术,设计的机器...将机器人采集到的温度、湿度和位置等数据信息传送至云平台服务器,便于云端数据分析、机器人数据共享以及人机信息交互等。本文应用第4代移动通信技术(the 4th generation mobile communication technology,4G)无线传输技术,设计的机器人与云平台无线数据传输系统主要包括硬件和软件实现2个部分,硬件方面机器人通过32位微控制器(STMicroelectronics32,STM32)嵌入式系统与4G模块组合,将传感器采集到的数据信息,通过Internet网络传输到云平台服务器中,软件部分运用C语言编程通过网络透传模式(transmission control protocol/user datagram protocol, TCP/UDP)实现无线通信过程,最终实验验证了机器人与云平台服务器之间的双向无线数据传输功能。展开更多
As one of the critical technologies for the 6 th generation mobile communication system(6 G) mobile communication systems, artificial intelligence(AI) technology will provide complete automation for connecting the vir...As one of the critical technologies for the 6 th generation mobile communication system(6 G) mobile communication systems, artificial intelligence(AI) technology will provide complete automation for connecting the virtual and physical worlds. In order to construct the future ubiquitous intelligent network, people are beginning to rethink how mobile communication systems transmit and exploit intelligent information. This paper proposes a new communication paradigm, called the Intellicise communication system: model-driven semantic communication. Intellicise communication system is built on top of the traditional communication system and innovatively adds a new feature dimension on top of the traditional source coding, which enables the communication system to evolve from the traditional transmission of bit to the transmission of "model". Like the semantic base(Seb) for semantic communication, the model is considered as the new feature obtained from the joint source-channel coding. The sink node can re-construct the original signal based on the received model and the encoded sequence. In addition, the performance evaluation metrics and the implementation details of the Intellicise communication system are discussed in this paper. Finally, preliminary results of model-driven image transmission in the Intellicise communication system are presented.展开更多
The application of the artificial intelligence(AI) technology in the 5 th generation mobile communication system(5 G) networks promotes the development of the mobile communication network and its application in vertic...The application of the artificial intelligence(AI) technology in the 5 th generation mobile communication system(5 G) networks promotes the development of the mobile communication network and its application in vertical industries, however, the application models of "patching" and "plug-in" have hindered the effect of AI applications. Meanwhile, the application of AI in all walks of life puts forward requirements for new capabilities of the future network, such as distributed training, real-time collaborative inference, local data processing, etc., which require "native intelligence design" in future networks. This paper discusses the requirements of native intelligence in the 6 th generation mobile communication system(6 G) networks from the perspectives of 5 G intelligent network challenges and the "ubiquitous intelligence" vision of 6 G, and analyzes the technical challenges of the AI workflows in its lifecycle and the AI as a service(AIaaS) in cloud network. The progress and deficiencies of the current research on AI functional architecture in various industry organizations are summarized. The end-to-end functional architecture for native AI for 6 G network and its three key technical characteristics are proposed: quality of AI services(QoAIS) based AI service orchestration for its full lifecycle, deep integration of native AI computing and communication, and integration of native AI and digital twin network. The directions of future research are also prospected.展开更多
It has been an exciting journey since the mobile communications and artificial intelligence(AI)were conceived in 1983 and 1956.While both fields evolved independently and profoundly changed communications and computin...It has been an exciting journey since the mobile communications and artificial intelligence(AI)were conceived in 1983 and 1956.While both fields evolved independently and profoundly changed communications and computing industries,the rapid convergence of 5th generation mobile communication technology(5G)and AI is beginning to significantly transform the core communication infrastructure,network management,and vertical applications.The paper first outlined the individual roadmaps of mobile communications and AI in the early stage,with a concentration to review the era from 3rd generation mobile communication technology(3G)to 5G when AI and mobile communications started to converge.With regard to telecommunications AI,the progress of AI in the ecosystem of mobile communications was further introduced in detail,including network infrastructure,network operation and management,business operation and management,intelligent applications towards business supporting system(BSS)&operation supporting system(OSS)convergence,verticals and private networks,etc.Then the classifications of AI in telecommunication ecosystems were summarized along with its evolution paths specified by various international telecommunications standardization organizations.Towards the next decade,the prospective roadmap of telecommunications AI was forecasted.In line with 3rd generation partnership project(3GPP)and International Telecommunication Union Radiocommunication Sector(ITU-R)timeline of 5G&6th generation mobile communication technology(6G),the network intelligence following 3GPP and open radio access network(O-RAN)routes,experience and intent-based network management and operation,network AI signaling system,intelligent middle-office based BSS,intelligent customer experience management and policy control driven by BSS&OSS convergence,evolution from service level agreement(SLA)to experience level agreement(ELA),and intelligent private network for verticals were further explored.The paper is concluded with the vision that AI will reshape the future beyond 5G(B5G)/6G landscape,and we need pivot our research and development(R&D),standardizations,and ecosystem to fully take the unprecedented opportunities.展开更多
In the 6 th generation mobile communication system(6 G) era, a large number of delay-sensitive and computation-intensive applications impose great pressure on resource-constrained Internet of things(IoT) devices. Aeri...In the 6 th generation mobile communication system(6 G) era, a large number of delay-sensitive and computation-intensive applications impose great pressure on resource-constrained Internet of things(IoT) devices. Aerial edge computing is envisioned as a promising and cost-effective solution, especially in hostile environments without terrestrial infrastructures. Therefore, this paper focuses on integrating aerial edge computing into 6 G for providing ubiquitous computing services for IoT devices. This paper first presents the layered network architecture of aerial edge computing for 6 G. The benefits, potential applications, and design challenges are also discussed in detail. Next, several key techniques like unmanned aerial vehicle(UAV) deployment, operation mode, offloading mode, caching policy, and resource management are highlighted to present how to integrated aerial edge computing into 6 G. Then, the joint UAV deployment optimization and computation offloading method is designed to minimize the computing delay for a typical aerial edge computing network. Numerical results reveal the significant delay reduction of the proposed method compared with the other benchmark methods. Finally, several open issues for aerial edge computing in 6 G are elaborated to provide some guidance for future research.展开更多
文摘5G is a new generation of mobile networking that aims to achieve unparalleled speed and performance. To accomplish this, three technologies, Device-to-Device communication (D2D), multi-access edge computing (MEC) and network function virtualization (NFV) with ClickOS, have been a significant part of 5G, and this paper mainly discusses them. D2D enables direct communication between devices without the relay of base station. In 5G, a two-tier cellular network composed of traditional cellular network system and D2D is an efficient method for realizing high-speed communication. MEC unloads work from end devices and clouds platforms to widespread nodes, and connects the nodes together with outside devices and third-party providers, in order to diminish the overloading effect on any device caused by enormous applications and improve users’ quality of experience (QoE). There is also a NFV method in order to fulfill the 5G requirements. In this part, an optimized virtual machine for middle-boxes named ClickOS is introduced, and it is evaluated in several aspects. Some middle boxes are being implemented in the ClickOS and proved to have outstanding performances.
文摘将机器人采集到的温度、湿度和位置等数据信息传送至云平台服务器,便于云端数据分析、机器人数据共享以及人机信息交互等。本文应用第4代移动通信技术(the 4th generation mobile communication technology,4G)无线传输技术,设计的机器人与云平台无线数据传输系统主要包括硬件和软件实现2个部分,硬件方面机器人通过32位微控制器(STMicroelectronics32,STM32)嵌入式系统与4G模块组合,将传感器采集到的数据信息,通过Internet网络传输到云平台服务器中,软件部分运用C语言编程通过网络透传模式(transmission control protocol/user datagram protocol, TCP/UDP)实现无线通信过程,最终实验验证了机器人与云平台服务器之间的双向无线数据传输功能。
基金supported by the National Natural Science Foundation of China (61871045)。
文摘As one of the critical technologies for the 6 th generation mobile communication system(6 G) mobile communication systems, artificial intelligence(AI) technology will provide complete automation for connecting the virtual and physical worlds. In order to construct the future ubiquitous intelligent network, people are beginning to rethink how mobile communication systems transmit and exploit intelligent information. This paper proposes a new communication paradigm, called the Intellicise communication system: model-driven semantic communication. Intellicise communication system is built on top of the traditional communication system and innovatively adds a new feature dimension on top of the traditional source coding, which enables the communication system to evolve from the traditional transmission of bit to the transmission of "model". Like the semantic base(Seb) for semantic communication, the model is considered as the new feature obtained from the joint source-channel coding. The sink node can re-construct the original signal based on the received model and the encoded sequence. In addition, the performance evaluation metrics and the implementation details of the Intellicise communication system are discussed in this paper. Finally, preliminary results of model-driven image transmission in the Intellicise communication system are presented.
基金supported by the National Key R&D Program of China (2020YFB1806800)。
文摘The application of the artificial intelligence(AI) technology in the 5 th generation mobile communication system(5 G) networks promotes the development of the mobile communication network and its application in vertical industries, however, the application models of "patching" and "plug-in" have hindered the effect of AI applications. Meanwhile, the application of AI in all walks of life puts forward requirements for new capabilities of the future network, such as distributed training, real-time collaborative inference, local data processing, etc., which require "native intelligence design" in future networks. This paper discusses the requirements of native intelligence in the 6 th generation mobile communication system(6 G) networks from the perspectives of 5 G intelligent network challenges and the "ubiquitous intelligence" vision of 6 G, and analyzes the technical challenges of the AI workflows in its lifecycle and the AI as a service(AIaaS) in cloud network. The progress and deficiencies of the current research on AI functional architecture in various industry organizations are summarized. The end-to-end functional architecture for native AI for 6 G network and its three key technical characteristics are proposed: quality of AI services(QoAIS) based AI service orchestration for its full lifecycle, deep integration of native AI computing and communication, and integration of native AI and digital twin network. The directions of future research are also prospected.
文摘It has been an exciting journey since the mobile communications and artificial intelligence(AI)were conceived in 1983 and 1956.While both fields evolved independently and profoundly changed communications and computing industries,the rapid convergence of 5th generation mobile communication technology(5G)and AI is beginning to significantly transform the core communication infrastructure,network management,and vertical applications.The paper first outlined the individual roadmaps of mobile communications and AI in the early stage,with a concentration to review the era from 3rd generation mobile communication technology(3G)to 5G when AI and mobile communications started to converge.With regard to telecommunications AI,the progress of AI in the ecosystem of mobile communications was further introduced in detail,including network infrastructure,network operation and management,business operation and management,intelligent applications towards business supporting system(BSS)&operation supporting system(OSS)convergence,verticals and private networks,etc.Then the classifications of AI in telecommunication ecosystems were summarized along with its evolution paths specified by various international telecommunications standardization organizations.Towards the next decade,the prospective roadmap of telecommunications AI was forecasted.In line with 3rd generation partnership project(3GPP)and International Telecommunication Union Radiocommunication Sector(ITU-R)timeline of 5G&6th generation mobile communication technology(6G),the network intelligence following 3GPP and open radio access network(O-RAN)routes,experience and intent-based network management and operation,network AI signaling system,intelligent middle-office based BSS,intelligent customer experience management and policy control driven by BSS&OSS convergence,evolution from service level agreement(SLA)to experience level agreement(ELA),and intelligent private network for verticals were further explored.The paper is concluded with the vision that AI will reshape the future beyond 5G(B5G)/6G landscape,and we need pivot our research and development(R&D),standardizations,and ecosystem to fully take the unprecedented opportunities.
文摘In the 6 th generation mobile communication system(6 G) era, a large number of delay-sensitive and computation-intensive applications impose great pressure on resource-constrained Internet of things(IoT) devices. Aerial edge computing is envisioned as a promising and cost-effective solution, especially in hostile environments without terrestrial infrastructures. Therefore, this paper focuses on integrating aerial edge computing into 6 G for providing ubiquitous computing services for IoT devices. This paper first presents the layered network architecture of aerial edge computing for 6 G. The benefits, potential applications, and design challenges are also discussed in detail. Next, several key techniques like unmanned aerial vehicle(UAV) deployment, operation mode, offloading mode, caching policy, and resource management are highlighted to present how to integrated aerial edge computing into 6 G. Then, the joint UAV deployment optimization and computation offloading method is designed to minimize the computing delay for a typical aerial edge computing network. Numerical results reveal the significant delay reduction of the proposed method compared with the other benchmark methods. Finally, several open issues for aerial edge computing in 6 G are elaborated to provide some guidance for future research.