The performance of multi-user code to direct spreading bi-phase shift keying （DS-BPSK） direct impulse ultra wideband （UWB） systems under indoor multi-user and multi-path environment is analyzed and simulated. The ...The performance of multi-user code to direct spreading bi-phase shift keying （DS-BPSK） direct impulse ultra wideband （UWB） systems under indoor multi-user and multi-path environment is analyzed and simulated. The system output signals with Rake receiver are derived, then a simple and practical code selection scheme is given; i. e., with a large occupation to empty ratio of the repeating pulses, directly choosing those random or pseudo-random user codes with enough length and good co-relative orthogonal features will make the performance of DS-BPSK approximate the optimum and, so there is no need to carefully design the code or its type. The system multi-access performances are simulated using Gold sequence and PN codes as multi-user codes under CMI-CM4 multi-path channels. Simulation results prove that the proposed scheme is feasible.展开更多
Multi-access Edge Computing(MEC)is one of the key technologies of the future 5G network.By deploying edge computing centers at the edge of wireless access network,the computation tasks can be offloaded to edge servers...Multi-access Edge Computing(MEC)is one of the key technologies of the future 5G network.By deploying edge computing centers at the edge of wireless access network,the computation tasks can be offloaded to edge servers rather than the remote cloud server to meet the requirements of 5G low-latency and high-reliability application scenarios.Meanwhile,with the development of IOV(Internet of Vehicles)technology,various delay-sensitive and compute-intensive in-vehicle applications continue to appear.Compared with traditional Internet business,these computation tasks have higher processing priority and lower delay requirements.In this paper,we design a 5G-based vehicle-aware Multi-access Edge Computing network(VAMECN)and propose a joint optimization problem of minimizing total system cost.In view of the problem,a deep reinforcement learningbased joint computation offloading and task migration optimization(JCOTM)algorithm is proposed,considering the influences of multiple factors such as concurrent multiple computation tasks,system computing resources distribution,and network communication bandwidth.And,the mixed integer nonlinear programming problem is described as a Markov Decision Process.Experiments show that our proposed algorithm can effectively reduce task processing delay and equipment energy consumption,optimize computing offloading and resource allocation schemes,and improve system resource utilization,compared with other computing offloading policies.展开更多
The main aim of future mobile networks is to provide secure,reliable,intelligent,and seamless connectivity.It also enables mobile network operators to ensure their customer’s a better quality of service(QoS).Nowadays...The main aim of future mobile networks is to provide secure,reliable,intelligent,and seamless connectivity.It also enables mobile network operators to ensure their customer’s a better quality of service(QoS).Nowadays,Unmanned Aerial Vehicles(UAVs)are a significant part of the mobile network due to their continuously growing use in various applications.For better coverage,cost-effective,and seamless service connectivity and provisioning,UAVs have emerged as the best choice for telco operators.UAVs can be used as flying base stations,edge servers,and relay nodes in mobile networks.On the other side,Multi-access EdgeComputing(MEC)technology also emerged in the 5G network to provide a better quality of experience(QoE)to users with different QoS requirements.However,UAVs in a mobile network for coverage enhancement and better QoS face several challenges such as trajectory designing,path planning,optimization,QoS assurance,mobilitymanagement,etc.The efficient and proactive path planning and optimization in a highly dynamic environment containing buildings and obstacles are challenging.So,an automated Artificial Intelligence(AI)enabled QoSaware solution is needed for trajectory planning and optimization.Therefore,this work introduces a well-designed AI and MEC-enabled architecture for a UAVs-assisted future network.It has an efficient Deep Reinforcement Learning(DRL)algorithm for real-time and proactive trajectory planning and optimization.It also fulfills QoS-aware service provisioning.A greedypolicy approach is used to maximize the long-term reward for serving more users withQoS.Simulation results reveal the superiority of the proposed DRL mechanism for energy-efficient and QoS-aware trajectory planning over the existing models.展开更多
In this paper,the security problem for the multi-access edge computing(MEC)network is researched,and an intelligent immunity-based security defense system is proposed to identify the unauthorized mobile users and to p...In this paper,the security problem for the multi-access edge computing(MEC)network is researched,and an intelligent immunity-based security defense system is proposed to identify the unauthorized mobile users and to protect the security of whole system.In the proposed security defense system,the security is protected by the intelligent immunity through three functions,identification function,learning function,and regulation function,respectively.Meanwhile,a three process-based intelligent algorithm is proposed for the intelligent immunity system.Numerical simulations are given to prove the effeteness of the proposed approach.展开更多
Blockchain and multi-access edge com-puting(MEC)are two emerging promising tech-nologies that have received extensive attention from academia and industry.As a brand-new information storage,dissemination and managemen...Blockchain and multi-access edge com-puting(MEC)are two emerging promising tech-nologies that have received extensive attention from academia and industry.As a brand-new information storage,dissemination and management mechanism,blockchain technology achieves the reliable transmis-sion of data and value.While as a new computing paradigm,multi-access edge computing enables the high-frequency interaction and real-time transmission of data.The integration of communication and com-puting in blockchain-enabled multi-access edge com-puting networks has been studied without a systemat-ical view.In the survey,we focus on the integration of communication and computing,explores the mu-tual empowerment and mutual promotion effects be-tween the blockchain and MEC,and introduces the resource integration architecture of blockchain and multi-access edge computing.Then,the paper sum-marizes the applications of the resource integration ar-chitecture,resource management,data sharing,incen-tive mechanism,and consensus mechanism,and ana-lyzes corresponding applications in real-world scenar-ios.Finally,future challenges and potentially promis-ing research directions are discussed and present in de-tail.展开更多
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
The Multi-access Edge Cloud(MEC) networks extend cloud computing services and capabilities to the edge of the networks. By bringing computation and storage capabilities closer to end-users and connected devices, MEC n...The Multi-access Edge Cloud(MEC) networks extend cloud computing services and capabilities to the edge of the networks. By bringing computation and storage capabilities closer to end-users and connected devices, MEC networks can support a wide range of applications. MEC networks can also leverage various types of resources, including computation resources, network resources, radio resources,and location-based resources, to provide multidimensional resources for intelligent applications in 5/6G.However, tasks generated by users often consist of multiple subtasks that require different types of resources. It is a challenging problem to offload multiresource task requests to the edge cloud aiming at maximizing benefits due to the heterogeneity of resources provided by devices. To address this issue,we mathematically model the task requests with multiple subtasks. Then, the problem of task offloading of multi-resource task requests is proved to be NP-hard. Furthermore, we propose a novel Dual-Agent Deep Reinforcement Learning algorithm with Node First and Link features(NF_L_DA_DRL) based on the policy network, to optimize the benefits generated by offloading multi-resource task requests in MEC networks. Finally, simulation results show that the proposed algorithm can effectively improve the benefit of task offloading with higher resource utilization compared with baseline algorithms.展开更多
The truncated binary exponential back-off algorithm is one of the most effective methods applied in collision resolution process of random multi-access channel.In this study,two new strategies are presented to improve...The truncated binary exponential back-off algorithm is one of the most effective methods applied in collision resolution process of random multi-access channel.In this study,two new strategies are presented to improve the capability of the truncated binary exponential back-off algorithm.In the new strategies,the sizes of the initial window size or the operating window sizes are adjusted dynamically,which always bring a significant improvement for the self-adaptability of the original algorithm.A series of experiments are simulated and the results verify that the new strategies can make the implementation more stable and effective than the original algorithm.展开更多
A higher quality of service （QoS） is provided for ad hoc networks through a multi-channel and slotted random multi-access （MSRM） protocol with two-dimensional probability. For this protocol, the system time is slo...A higher quality of service （QoS） is provided for ad hoc networks through a multi-channel and slotted random multi-access （MSRM） protocol with two-dimensional probability. For this protocol, the system time is slotted into a time slot with high channel utilization realized by the choice of two parameters p1 and p2, and the channel load equilibrium. The protocol analyzes the throughput of the MSRM protocol for a load equilibrium state and the throughput based on priority. Simulations agree with the theoretical analysis. The simulations also show that the slotted-time system is better than the continuous-time system.展开更多
张量转置(tensor transposition)作为基础张量运算原语,广泛应用于信号处理、科学计算以及深度学习等各种领域,在张量数据密集型应用及高性能计算中具有重要作用。随着能效指标在高性能计算系统中的重要性日益凸显,基于数字信号处理器(d...张量转置(tensor transposition)作为基础张量运算原语,广泛应用于信号处理、科学计算以及深度学习等各种领域,在张量数据密集型应用及高性能计算中具有重要作用。随着能效指标在高性能计算系统中的重要性日益凸显,基于数字信号处理器(digital signal processors,DSPs)的加速器已被集成至通用计算系统。然而,传统面向多核CPU和GPU的张量转置库因架构差异无法充分适配DSP架构。一方面,DSP架构的向量化计算潜力尚未得到充分挖掘;另一方面,其复杂的片上存储体系与多层次共享内存结构为张量并行程序设计带来了显著挑战。针对国产多核DSP的架构特点,提出ftmTT算法,并设计实现了一个面向多核DSP架构的通用张量转置库。ftmTT算法通过设计适配DSP架构的高效内存访问模式充分挖掘其并行化和向量化潜力,其核心创新包括:1)采用分块策略将高维张量转置转化为多核DSP平台所提供的矩阵转置内核操作;2)提出基于DMA点对点传输的张量数据块访存合并方案来降低数据搬运开销;3)通过双缓冲设计异步重叠转置计算与DMA传输实现计算通信隐藏,最终面向多核DSP实现高性能并行张量转置。在国产多核DSP平台FT-M7032的实验表明,ftmTT张量转置算法取得了最高达理论带宽75.96%的性能,达到FT-M7032平台STREAM带宽99.23%的性能。展开更多
高性能同轴电缆网络(High Performance Network Over Coax,HINOC)技术是一种光纤同轴混合接入技术,已发展至第3代。为了实现万兆以太网的接入速率,第3代HINOC引入了多信道绑定机制。但该机制在有效扩展HINOC网络信道带宽的同时易导致HIM...高性能同轴电缆网络(High Performance Network Over Coax,HINOC)技术是一种光纤同轴混合接入技术,已发展至第3代。为了实现万兆以太网的接入速率,第3代HINOC引入了多信道绑定机制。但该机制在有效扩展HINOC网络信道带宽的同时易导致HIMAC(HINOC Medium Access Control)拆帧端接收的数据流失序。针对该问题,文中提出了一种拆帧重排序方法。通过重排序队列缓存管理、入队逻辑地址计算、超时判断及清空以及出队判断等关键技术的设计和实现来解决多信道绑定机制引起的拆帧乱序问题,并对其关键功能点进行仿真验证和板级验证。实验结果表明,所提方法能够有效处理多信道绑定导致的乱序问题,并且能够确保系统在遇到错误情况时稳定运行,具有较强的鲁棒性,满足万兆同轴宽带接入HIMAC 3.0的功能和性能要求。展开更多
文摘The performance of multi-user code to direct spreading bi-phase shift keying （DS-BPSK） direct impulse ultra wideband （UWB） systems under indoor multi-user and multi-path environment is analyzed and simulated. The system output signals with Rake receiver are derived, then a simple and practical code selection scheme is given; i. e., with a large occupation to empty ratio of the repeating pulses, directly choosing those random or pseudo-random user codes with enough length and good co-relative orthogonal features will make the performance of DS-BPSK approximate the optimum and, so there is no need to carefully design the code or its type. The system multi-access performances are simulated using Gold sequence and PN codes as multi-user codes under CMI-CM4 multi-path channels. Simulation results prove that the proposed scheme is feasible.
基金supported in part by the National Key R&D Program of China under Grant 2018YFC0831502.
文摘Multi-access Edge Computing(MEC)is one of the key technologies of the future 5G network.By deploying edge computing centers at the edge of wireless access network,the computation tasks can be offloaded to edge servers rather than the remote cloud server to meet the requirements of 5G low-latency and high-reliability application scenarios.Meanwhile,with the development of IOV(Internet of Vehicles)technology,various delay-sensitive and compute-intensive in-vehicle applications continue to appear.Compared with traditional Internet business,these computation tasks have higher processing priority and lower delay requirements.In this paper,we design a 5G-based vehicle-aware Multi-access Edge Computing network(VAMECN)and propose a joint optimization problem of minimizing total system cost.In view of the problem,a deep reinforcement learningbased joint computation offloading and task migration optimization(JCOTM)algorithm is proposed,considering the influences of multiple factors such as concurrent multiple computation tasks,system computing resources distribution,and network communication bandwidth.And,the mixed integer nonlinear programming problem is described as a Markov Decision Process.Experiments show that our proposed algorithm can effectively reduce task processing delay and equipment energy consumption,optimize computing offloading and resource allocation schemes,and improve system resource utilization,compared with other computing offloading policies.
基金This work was supported by the Fundamental Research Funds for the Central Universities(No.2019XD-A07)the Director Fund of Beijing Key Laboratory of Space-ground Interconnection and Convergencethe National Key Laboratory of Science and Technology on Vacuum Electronics.
文摘The main aim of future mobile networks is to provide secure,reliable,intelligent,and seamless connectivity.It also enables mobile network operators to ensure their customer’s a better quality of service(QoS).Nowadays,Unmanned Aerial Vehicles(UAVs)are a significant part of the mobile network due to their continuously growing use in various applications.For better coverage,cost-effective,and seamless service connectivity and provisioning,UAVs have emerged as the best choice for telco operators.UAVs can be used as flying base stations,edge servers,and relay nodes in mobile networks.On the other side,Multi-access EdgeComputing(MEC)technology also emerged in the 5G network to provide a better quality of experience(QoE)to users with different QoS requirements.However,UAVs in a mobile network for coverage enhancement and better QoS face several challenges such as trajectory designing,path planning,optimization,QoS assurance,mobilitymanagement,etc.The efficient and proactive path planning and optimization in a highly dynamic environment containing buildings and obstacles are challenging.So,an automated Artificial Intelligence(AI)enabled QoSaware solution is needed for trajectory planning and optimization.Therefore,this work introduces a well-designed AI and MEC-enabled architecture for a UAVs-assisted future network.It has an efficient Deep Reinforcement Learning(DRL)algorithm for real-time and proactive trajectory planning and optimization.It also fulfills QoS-aware service provisioning.A greedypolicy approach is used to maximize the long-term reward for serving more users withQoS.Simulation results reveal the superiority of the proposed DRL mechanism for energy-efficient and QoS-aware trajectory planning over the existing models.
基金This work was supported by National Natural Science Foundation of China(No.61971026)the Fundamental Research Funds for the Central Universities(No.FRF-TP-18-008A3).
文摘In this paper,the security problem for the multi-access edge computing(MEC)network is researched,and an intelligent immunity-based security defense system is proposed to identify the unauthorized mobile users and to protect the security of whole system.In the proposed security defense system,the security is protected by the intelligent immunity through three functions,identification function,learning function,and regulation function,respectively.Meanwhile,a three process-based intelligent algorithm is proposed for the intelligent immunity system.Numerical simulations are given to prove the effeteness of the proposed approach.
基金the National Key Re-search and Development Program of China(No.2020YFB1807500)the National Natural Science Foundation of China(No.62102297,No.61902292)+2 种基金the Guangdong Basic and Applied Basic Research Foundation(No.2020A1515110496)the Fundamen-tal Research Funds for the Central Universities(No.XJS210105,No.XJS201502)the Open Project of Shaanxi Key Laboratory of Information Communi-cation Network and Security(No.ICNS202005).
文摘Blockchain and multi-access edge com-puting(MEC)are two emerging promising tech-nologies that have received extensive attention from academia and industry.As a brand-new information storage,dissemination and management mechanism,blockchain technology achieves the reliable transmis-sion of data and value.While as a new computing paradigm,multi-access edge computing enables the high-frequency interaction and real-time transmission of data.The integration of communication and com-puting in blockchain-enabled multi-access edge com-puting networks has been studied without a systemat-ical view.In the survey,we focus on the integration of communication and computing,explores the mu-tual empowerment and mutual promotion effects be-tween the blockchain and MEC,and introduces the resource integration architecture of blockchain and multi-access edge computing.Then,the paper sum-marizes the applications of the resource integration ar-chitecture,resource management,data sharing,incen-tive mechanism,and consensus mechanism,and ana-lyzes corresponding applications in real-world scenar-ios.Finally,future challenges and potentially promis-ing research directions are discussed and present in de-tail.
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.
基金supported in part by the National Natural Science Foundation of China under Grants 62201105,62331017,and 62075024in part by the Natural Science Foundation of Chongqing under Grant cstc2021jcyj-msxmX0404+1 种基金in part by the Chongqing Municipal Education Commission under Grant KJQN202100643in part by Guangdong Basic and Applied Basic Research Foundation under Grant 2022A1515110056.
文摘The Multi-access Edge Cloud(MEC) networks extend cloud computing services and capabilities to the edge of the networks. By bringing computation and storage capabilities closer to end-users and connected devices, MEC networks can support a wide range of applications. MEC networks can also leverage various types of resources, including computation resources, network resources, radio resources,and location-based resources, to provide multidimensional resources for intelligent applications in 5/6G.However, tasks generated by users often consist of multiple subtasks that require different types of resources. It is a challenging problem to offload multiresource task requests to the edge cloud aiming at maximizing benefits due to the heterogeneity of resources provided by devices. To address this issue,we mathematically model the task requests with multiple subtasks. Then, the problem of task offloading of multi-resource task requests is proved to be NP-hard. Furthermore, we propose a novel Dual-Agent Deep Reinforcement Learning algorithm with Node First and Link features(NF_L_DA_DRL) based on the policy network, to optimize the benefits generated by offloading multi-resource task requests in MEC networks. Finally, simulation results show that the proposed algorithm can effectively improve the benefit of task offloading with higher resource utilization compared with baseline algorithms.
基金This work was supported by the National Natural Science Foundation of China(No.10371097).
文摘The truncated binary exponential back-off algorithm is one of the most effective methods applied in collision resolution process of random multi-access channel.In this study,two new strategies are presented to improve the capability of the truncated binary exponential back-off algorithm.In the new strategies,the sizes of the initial window size or the operating window sizes are adjusted dynamically,which always bring a significant improvement for the self-adaptability of the original algorithm.A series of experiments are simulated and the results verify that the new strategies can make the implementation more stable and effective than the original algorithm.
基金Supported by the National Natural Science Foundation of China(Nos. 60362001 and F0424104)the Natural Science Foundationof Yunnan Province (No. 2004F0011R)
文摘A higher quality of service （QoS） is provided for ad hoc networks through a multi-channel and slotted random multi-access （MSRM） protocol with two-dimensional probability. For this protocol, the system time is slotted into a time slot with high channel utilization realized by the choice of two parameters p1 and p2, and the channel load equilibrium. The protocol analyzes the throughput of the MSRM protocol for a load equilibrium state and the throughput based on priority. Simulations agree with the theoretical analysis. The simulations also show that the slotted-time system is better than the continuous-time system.
文摘张量转置(tensor transposition)作为基础张量运算原语,广泛应用于信号处理、科学计算以及深度学习等各种领域,在张量数据密集型应用及高性能计算中具有重要作用。随着能效指标在高性能计算系统中的重要性日益凸显,基于数字信号处理器(digital signal processors,DSPs)的加速器已被集成至通用计算系统。然而,传统面向多核CPU和GPU的张量转置库因架构差异无法充分适配DSP架构。一方面,DSP架构的向量化计算潜力尚未得到充分挖掘;另一方面,其复杂的片上存储体系与多层次共享内存结构为张量并行程序设计带来了显著挑战。针对国产多核DSP的架构特点,提出ftmTT算法,并设计实现了一个面向多核DSP架构的通用张量转置库。ftmTT算法通过设计适配DSP架构的高效内存访问模式充分挖掘其并行化和向量化潜力,其核心创新包括:1)采用分块策略将高维张量转置转化为多核DSP平台所提供的矩阵转置内核操作;2)提出基于DMA点对点传输的张量数据块访存合并方案来降低数据搬运开销;3)通过双缓冲设计异步重叠转置计算与DMA传输实现计算通信隐藏,最终面向多核DSP实现高性能并行张量转置。在国产多核DSP平台FT-M7032的实验表明,ftmTT张量转置算法取得了最高达理论带宽75.96%的性能,达到FT-M7032平台STREAM带宽99.23%的性能。
文摘高性能同轴电缆网络(High Performance Network Over Coax,HINOC)技术是一种光纤同轴混合接入技术,已发展至第3代。为了实现万兆以太网的接入速率,第3代HINOC引入了多信道绑定机制。但该机制在有效扩展HINOC网络信道带宽的同时易导致HIMAC(HINOC Medium Access Control)拆帧端接收的数据流失序。针对该问题,文中提出了一种拆帧重排序方法。通过重排序队列缓存管理、入队逻辑地址计算、超时判断及清空以及出队判断等关键技术的设计和实现来解决多信道绑定机制引起的拆帧乱序问题,并对其关键功能点进行仿真验证和板级验证。实验结果表明,所提方法能够有效处理多信道绑定导致的乱序问题,并且能够确保系统在遇到错误情况时稳定运行,具有较强的鲁棒性,满足万兆同轴宽带接入HIMAC 3.0的功能和性能要求。
