Recently,the Fog-Radio Access Network(F-RAN)has gained considerable attention,because of its flexible architecture that allows rapid response to user requirements.In this paper,computational offloading in F-RAN is con...Recently,the Fog-Radio Access Network(F-RAN)has gained considerable attention,because of its flexible architecture that allows rapid response to user requirements.In this paper,computational offloading in F-RAN is considered,where multiple User Equipments(UEs)offload their computational tasks to the F-RAN through fog nodes.Each UE can select one of the fog nodes to offload its task,and each fog node may serve multiple UEs.The tasks are computed by the fog nodes or further offloaded to the cloud via a capacity-limited fronhaul link.In order to compute all UEs'tasks quickly,joint optimization of UE-Fog association,radio and computation resources of F-RAN is proposed to minimize the maximum latency of all UEs.This min-max problem is formulated as a Mixed Integer Nonlinear Program(MINP).To tackle it,first,MINP is reformulated as a continuous optimization problem,and then the Majorization Minimization(MM)method is used to find a solution.The MM approach that we develop is unconventional in that each MM subproblem is solved inexactly with the same provable convergence guarantee as the exact MM,thereby reducing the complexity of MM iteration.In addition,a cooperative offloading model is considered,where the fog nodes compress-and-forward their received signals to the cloud.Under this model,a similar min-max latency optimization problem is formulated and tackled by the inexact MM.Simulation results show that the proposed algorithms outperform some offloading strategies,and that the cooperative offloading can exploit transmission diversity better than noncooperative offloading to achieve better latency performance.展开更多
为了降低前传(fronthaul)链路开销、避免传统云无线接入网基带单元池中无线信号处理大规模/高实时要求、充分利用边缘网络设备的计算和存储能力,提出了雾无线接入网络(fog computing based radio access network,F-RAN),作为异构云无线...为了降低前传(fronthaul)链路开销、避免传统云无线接入网基带单元池中无线信号处理大规模/高实时要求、充分利用边缘网络设备的计算和存储能力,提出了雾无线接入网络(fog computing based radio access network,F-RAN),作为异构云无线接入网络的演进。F-RAN的核心是利用用户和边缘网络设备的计算和存储功能,进行本地业务分发、分布式信号处理和分布式资源管理等。详细介绍了F-RAN的系统架构、关键技术及未来需研究的问题。展开更多
In order to alleviate capacity constraints on the fronthaul and decrease the transmit latency, a hierarchical content caching paradigm is applied in the fog radio access networks(F-RANs). In particular, a specific clu...In order to alleviate capacity constraints on the fronthaul and decrease the transmit latency, a hierarchical content caching paradigm is applied in the fog radio access networks(F-RANs). In particular, a specific cluster of remote radio heads is formed through a common centralized cloud at the baseband unit pool, while the local content is directly delivered at fog access points with edge cache and distributed radio signal processing capability. Focusing on a downlink F-RAN, the explicit expressions of ergodic rate for the hierarchical paradigm is derived. Meanwhile, both the waiting delay and latency ratio for users requiring a single content are exploited. According to the evaluation results of ergodic rate on waiting delay, the transmit latency can be effectively reduced through improving the capacity of both fronthaul and radio access links. Moreover, to fully explore the potential of hierarchical content caching, the transmit latency for users requiring multiple content objects is optimized as well in three content transmission cases with different radio access links. The simulation results verify the accuracy of the analysis, further show the latency decreases significantly due to the hierarchical paradigm.展开更多
As a promising paradigm of the fifth generation networks,fog radio access network(F-RAN)has attracted lots of attention nowadays.To fully utilize the promising gain of F-RANs,the acquisition of accurate channel state ...As a promising paradigm of the fifth generation networks,fog radio access network(F-RAN)has attracted lots of attention nowadays.To fully utilize the promising gain of F-RANs,the acquisition of accurate channel state information is significant.However,conventional channel estimation approaches are not suitable in F-RANs due to the large training and feedback overhead.In this paper,we consider the channel estimation in F-RANs with fog access point(F-AP)equipped with massive antennas.Thanks to the computing ability of F-AP and the sparsity of channel matrices in angular domain,Gated Recurrent Unit(GRU),a data-driven based channel estimation is proposed at F-AP to reduce the training and feedback overhead.The GRU-based method can capture the hidden sparsity structure automatically through the network training.Moreover,to further improve the channel estimation,a bidirectional GRU based method is proposed,whose target channel structure is decided by previous and subsequent structures.We compare the performance of our proposed channel estimation with traditional methods(Orthogonal Matching Pursuit(OMP)and Simultaneous OMP(SOMP)).Simulation results show that the proposed approaches have better performance compared with the traditional OMP and SOMP methods.展开更多
Coordinated signal processing can obtain a huge transmission gain for Fog Radio Access Networks(F-RANs).However,integrating into large scale,it will lead to high computation complexity in channel estimation and spectr...Coordinated signal processing can obtain a huge transmission gain for Fog Radio Access Networks(F-RANs).However,integrating into large scale,it will lead to high computation complexity in channel estimation and spectral efficiency loss in transmission performance.Thus,a joint cluster formation and channel estimation scheme is proposed in this paper.Considering research remote radio heads(RRHs)centred serving scheme,a coalition game is formulated in order to maximize the spectral efficiency of cooperative RRHs under the conditions of balancing the data rate and the cost of channel estimation.As the cost influences to the necessary consumption of training length and estimation error.Particularly,an iterative semi-blind channel estimation and symbol detection approach is designed by expectation maximization algorithm,where the channel estimation process is initialized by subspace method with lower pilot length.Finally,the simulation results show that a stable cluster formation is established by our proposed coalition game method and it outperforms compared with full coordinated schemes.展开更多
针对雾无线接入网络(Fog Radio Access Network,F-RAN)中能耗开销巨大的问题,提出了一种基于能量收集(Energy Harvesting,EH)约束的资源分配算法,从联合模式选择与功率分配两个方面进行了研究。首先建立传输模型和能量采集模型,根据功...针对雾无线接入网络(Fog Radio Access Network,F-RAN)中能耗开销巨大的问题,提出了一种基于能量收集(Energy Harvesting,EH)约束的资源分配算法,从联合模式选择与功率分配两个方面进行了研究。首先建立传输模型和能量采集模型,根据功率约束和电费支出约束建立最优化问题;再使用分枝定界法对通信模式进行选择,利用吞吐量注水法对不同传输模式下的发射功率进行分配。仿真结果表明,提出的基于可再生能量协作的F-RAN的吞吐量和电网能量效率均高于传统F-RAN,具有经济和环境双重效益。展开更多
With the explosive growth of highspeed wireless data demand and the number of mobile devices, fog radio access networks(F-RAN) with multi-layer network structure becomes a hot topic in recent research. Meanwhile, due ...With the explosive growth of highspeed wireless data demand and the number of mobile devices, fog radio access networks(F-RAN) with multi-layer network structure becomes a hot topic in recent research. Meanwhile, due to the rapid growth of mobile communication traffic, high cost and the scarcity of wireless resources, it is especially important to develop an efficient radio resource management mechanism. In this paper, we focus on the shortcomings of resource waste, and we consider the actual situation of base station dynamic coverage and user requirements. We propose a spectrum pricing and allocation scheme based on Stackelberg game model under F-RAN framework, realizing the allocation of resource on demand. This scheme studies the double game between the users and the operators, as well as between the traditional operators and the virtual operators, maximizing the profits of the operators. At the same time, spectrum reuse technology is adopted to improve the utilization of network resource. By analyzing the simulation results, it is verified that our proposed scheme can not only avoid resource waste, but also effectively improve the operator's revenue efficiency and overall network resource utilization.展开更多
The development of communication technologies which support traffic-intensive applications presents new challenges in designing a real-time traffic analysis architecture and an accurate method that suitable for a wide...The development of communication technologies which support traffic-intensive applications presents new challenges in designing a real-time traffic analysis architecture and an accurate method that suitable for a wide variety of traffic types.Current traffic analysis methods are executed on the cloud,which needs to upload the traffic data.Fog computing is a more promising way to save bandwidth resources by offloading these tasks to the fog nodes.However,traffic analysis models based on traditional machine learning need to retrain all traffic data when updating the trained model,which are not suitable for fog computing due to the poor computing power.In this study,we design a novel fog computing based traffic analysis system using broad learning.For one thing,fog computing can provide a distributed architecture for saving the bandwidth resources.For another,we use the broad learning to incrementally train the traffic data,which is more suitable for fog computing because it can support incremental updates of models without retraining all data.We implement our system on the Raspberry Pi,and experimental results show that we have a 98%probability to accurately identify these traffic data.Moreover,our method has a faster training speed compared with Convolutional Neural Network(CNN).展开更多
基金supported in part by the Natural Science Foundation of China (62171110,U19B2028 and U20B2070)。
文摘Recently,the Fog-Radio Access Network(F-RAN)has gained considerable attention,because of its flexible architecture that allows rapid response to user requirements.In this paper,computational offloading in F-RAN is considered,where multiple User Equipments(UEs)offload their computational tasks to the F-RAN through fog nodes.Each UE can select one of the fog nodes to offload its task,and each fog node may serve multiple UEs.The tasks are computed by the fog nodes or further offloaded to the cloud via a capacity-limited fronhaul link.In order to compute all UEs'tasks quickly,joint optimization of UE-Fog association,radio and computation resources of F-RAN is proposed to minimize the maximum latency of all UEs.This min-max problem is formulated as a Mixed Integer Nonlinear Program(MINP).To tackle it,first,MINP is reformulated as a continuous optimization problem,and then the Majorization Minimization(MM)method is used to find a solution.The MM approach that we develop is unconventional in that each MM subproblem is solved inexactly with the same provable convergence guarantee as the exact MM,thereby reducing the complexity of MM iteration.In addition,a cooperative offloading model is considered,where the fog nodes compress-and-forward their received signals to the cloud.Under this model,a similar min-max latency optimization problem is formulated and tackled by the inexact MM.Simulation results show that the proposed algorithms outperform some offloading strategies,and that the cooperative offloading can exploit transmission diversity better than noncooperative offloading to achieve better latency performance.
文摘为了降低前传(fronthaul)链路开销、避免传统云无线接入网基带单元池中无线信号处理大规模/高实时要求、充分利用边缘网络设备的计算和存储能力,提出了雾无线接入网络(fog computing based radio access network,F-RAN),作为异构云无线接入网络的演进。F-RAN的核心是利用用户和边缘网络设备的计算和存储功能,进行本地业务分发、分布式信号处理和分布式资源管理等。详细介绍了F-RAN的系统架构、关键技术及未来需研究的问题。
基金supported in part by the National Natural Science Foundation of China (Grant No.61361166005)the State Major Science and Technology Special Projects (Grant No.2016ZX03001020006)the National Program for Support of Top-notch Young Professionals
文摘In order to alleviate capacity constraints on the fronthaul and decrease the transmit latency, a hierarchical content caching paradigm is applied in the fog radio access networks(F-RANs). In particular, a specific cluster of remote radio heads is formed through a common centralized cloud at the baseband unit pool, while the local content is directly delivered at fog access points with edge cache and distributed radio signal processing capability. Focusing on a downlink F-RAN, the explicit expressions of ergodic rate for the hierarchical paradigm is derived. Meanwhile, both the waiting delay and latency ratio for users requiring a single content are exploited. According to the evaluation results of ergodic rate on waiting delay, the transmit latency can be effectively reduced through improving the capacity of both fronthaul and radio access links. Moreover, to fully explore the potential of hierarchical content caching, the transmit latency for users requiring multiple content objects is optimized as well in three content transmission cases with different radio access links. The simulation results verify the accuracy of the analysis, further show the latency decreases significantly due to the hierarchical paradigm.
基金supported in part by the State Major Science and Technology Special Project(Grant No.2018ZX03001023)the National Natural Science Foundation of China under No.61831002+1 种基金the National Science Foundation for Postdoctoral Scientists of China(Grant No.2018M641279)FundamentalResearch Funds for the Central Universities under Grant No.2018XKJC01
文摘As a promising paradigm of the fifth generation networks,fog radio access network(F-RAN)has attracted lots of attention nowadays.To fully utilize the promising gain of F-RANs,the acquisition of accurate channel state information is significant.However,conventional channel estimation approaches are not suitable in F-RANs due to the large training and feedback overhead.In this paper,we consider the channel estimation in F-RANs with fog access point(F-AP)equipped with massive antennas.Thanks to the computing ability of F-AP and the sparsity of channel matrices in angular domain,Gated Recurrent Unit(GRU),a data-driven based channel estimation is proposed at F-AP to reduce the training and feedback overhead.The GRU-based method can capture the hidden sparsity structure automatically through the network training.Moreover,to further improve the channel estimation,a bidirectional GRU based method is proposed,whose target channel structure is decided by previous and subsequent structures.We compare the performance of our proposed channel estimation with traditional methods(Orthogonal Matching Pursuit(OMP)and Simultaneous OMP(SOMP)).Simulation results show that the proposed approaches have better performance compared with the traditional OMP and SOMP methods.
基金supported in part by the State Major Science and Technology Special Project(Grant No.2018ZX03001025)the National Natural Science Foundation of China(No.61831002 and No.61671074)the Fundamental Research Funds for the Central Universities under Grant No.2018XKJC01
文摘Coordinated signal processing can obtain a huge transmission gain for Fog Radio Access Networks(F-RANs).However,integrating into large scale,it will lead to high computation complexity in channel estimation and spectral efficiency loss in transmission performance.Thus,a joint cluster formation and channel estimation scheme is proposed in this paper.Considering research remote radio heads(RRHs)centred serving scheme,a coalition game is formulated in order to maximize the spectral efficiency of cooperative RRHs under the conditions of balancing the data rate and the cost of channel estimation.As the cost influences to the necessary consumption of training length and estimation error.Particularly,an iterative semi-blind channel estimation and symbol detection approach is designed by expectation maximization algorithm,where the channel estimation process is initialized by subspace method with lower pilot length.Finally,the simulation results show that a stable cluster formation is established by our proposed coalition game method and it outperforms compared with full coordinated schemes.
文摘针对雾无线接入网络(Fog Radio Access Network,F-RAN)中能耗开销巨大的问题,提出了一种基于能量收集(Energy Harvesting,EH)约束的资源分配算法,从联合模式选择与功率分配两个方面进行了研究。首先建立传输模型和能量采集模型,根据功率约束和电费支出约束建立最优化问题;再使用分枝定界法对通信模式进行选择,利用吞吐量注水法对不同传输模式下的发射功率进行分配。仿真结果表明,提出的基于可再生能量协作的F-RAN的吞吐量和电网能量效率均高于传统F-RAN,具有经济和环境双重效益。
基金supported in part by the National Natural Science Foundation of China (61771120)the Fundamental Research Funds for the Central Universities (N171602002)
文摘With the explosive growth of highspeed wireless data demand and the number of mobile devices, fog radio access networks(F-RAN) with multi-layer network structure becomes a hot topic in recent research. Meanwhile, due to the rapid growth of mobile communication traffic, high cost and the scarcity of wireless resources, it is especially important to develop an efficient radio resource management mechanism. In this paper, we focus on the shortcomings of resource waste, and we consider the actual situation of base station dynamic coverage and user requirements. We propose a spectrum pricing and allocation scheme based on Stackelberg game model under F-RAN framework, realizing the allocation of resource on demand. This scheme studies the double game between the users and the operators, as well as between the traditional operators and the virtual operators, maximizing the profits of the operators. At the same time, spectrum reuse technology is adopted to improve the utilization of network resource. By analyzing the simulation results, it is verified that our proposed scheme can not only avoid resource waste, but also effectively improve the operator's revenue efficiency and overall network resource utilization.
基金supported by JSPS KAKENHI Grant Number JP16K00117, JP19K20250KDDI Foundationthe China Scholarship Council (201808050016)
文摘The development of communication technologies which support traffic-intensive applications presents new challenges in designing a real-time traffic analysis architecture and an accurate method that suitable for a wide variety of traffic types.Current traffic analysis methods are executed on the cloud,which needs to upload the traffic data.Fog computing is a more promising way to save bandwidth resources by offloading these tasks to the fog nodes.However,traffic analysis models based on traditional machine learning need to retrain all traffic data when updating the trained model,which are not suitable for fog computing due to the poor computing power.In this study,we design a novel fog computing based traffic analysis system using broad learning.For one thing,fog computing can provide a distributed architecture for saving the bandwidth resources.For another,we use the broad learning to incrementally train the traffic data,which is more suitable for fog computing because it can support incremental updates of models without retraining all data.We implement our system on the Raspberry Pi,and experimental results show that we have a 98%probability to accurately identify these traffic data.Moreover,our method has a faster training speed compared with Convolutional Neural Network(CNN).
