基于NOMA的F-RAN下的无线资源分配优化

在4G到5G过渡阶段,射频拉远头(remote radio head,RRH)和雾节点(fog access point,F-AP)并存的情况将越来越普遍,为了充分利用RRH和F-AP的通信资源,本文提出了一种基于非正交多址接入(non-orthogonal multiple access,NOMA)的雾无线接入网(fog radio access networks,F-RAN)下的新型卸载方案.该方案以最小化所有用户的最大任务卸载时延为优化目标,联合优化了资源块(resource block,RB)分配和上行NOMA用户发射功率.本文对优化问题进行解耦,首先基于超图的匹配算法解决了RB分配问题,然后利用序列二次规划(sequential quadratic programing,SQP)算法解决非凸的功率优化问题,最后通过对两算法进行迭代提出一种资源分配算法.仿真结果表明,本文提出的卸载方案及联合资源分配算法相比于已有的方案在用户时延上具有更好的性能.

雾网络中基于统计分布的内容缓存与交付方案

作为5G中的一种重要模型,雾无线接入网络(Fog Radio Access Network,F-RAN)通过设备到设备通信和无线中继等技术获得了显著的性能增益,而边缘设备中合适的缓存则可以让内容缓存用户(Caching Users,CUs)向内容请求用户(Requesting Users,RUs)直接发送缓存内容,有效减小前传链路的负担和下载延迟。考虑一个F-RAN模型下用户发出请求并获得交付的场景,将每个CU的内容请求队列建模为独立的M/D/1模型,分析导出CUs缓存命中率和平均下载延迟关于内容缓存与交付方案的表达式,证明CUs缓存命中率与内容统计分布之间的联系有助于实现前者的近似最优解。针对在一段时间内的期望视角下建立的优化问题,提出了基于统计分布的算法并注意了执行时的交付控制。仿真结果表明,相较于现有缓存策略,优化内容整体统计分布的方案能够最大化CUs缓存命中率,同时减小平均下载延迟。

雾无线接入网中基于神经网络的资源分配方案

考虑雾无线接入网(Fog Radio Access Network,F-RAN)中的性能优化问题,提出一种基于深度神经网络(Deep Neural Network,DNN)的资源分配方案。该方案旨在通过资源分配策略来最大化经济频谱效率(Economical Spectral Efficiency,ESE)。为解决传统资源分配方案需要大量计算的问题,该方案借助神经网络模型,将ESE作为损失函数,使用更少的计算量来确定用户的波束赋形,从而实现实时处理。仿真结果表明,相比于基于传统凸优化功率分配方案或者是基于监督学习的CNN方法,所提出的方案的光谱效率(Spectral Efficiency,SE)和ESE的最大增益分别可以达到5%和20%。此外,该方案在执行时间上与CNN方案接近,明显优于传统算法。

Efficient oneM2M Protocol Conversion Platform Based on NB-IoT Access

The protocol is the foundation of IoT technology, which plays an important role in the IoT device interworking and interoperability. The 5 G wireless communication system provides large-scale NB-IoT terminals access with F-RAN, and the vertical industry applications need support for the device management. As one of the most influential protocol in the IoT application, the oneM2M protocol not only has a complete architecture, but also has an interface with other IoT protocols. Therefore, to bridge the gap between the operator and industrial enterprises, the main contributions of this paper are as follows: Firstly, a general multi-protocol conversion method is proposed based on oneM2M platform where the protocol classification is used in different scenarios. Secondly, the F-RAN architecture of oneM2M platform is designed and implemented with NB-IoT device access. Thirdly, a multiplexing scheme to process the device information is proposed for interworking proxy entity(IPE), which improves the conversion efficiency for different protocols. Finally, the feasibility and efficiency of the scheme are verified.

Joint Design of Coalition Formation and Semi-Blind Channel Estimation in Fog Radio Access Networks

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.

Deep Learning Based Channel Estimation in Fog Radio Access Networks

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.

The Interplay between Artificial Intelligence and Fog Radio Access Networks

The interplay between artificial intelligence(AI) and fog radio access networks(F-RANs) is investigated in this work from two perspectives: how F-RANs enable hierarchical AI to be deployed in wireless networks and how AI makes F-RANs smarter to better serve mobile devices. Due to the heterogeneity of processing capability, the cloud, fog, and device layers in F-RANs provide hierarchical intelligence via centralized, distributed, and federated learning. In addition, cross-layer learning is also introduced to further reduce the demand for the memory size of the mobile devices. On the other hand, AI provides F-RANs with technologies and methods to deal with massive data and make smarter decisions. Specifically, machine learning tools such as deep neural networks are introduced for data processing, while reinforcement learning(RL) algorithms are adopted for network optimization and decisions. Then, two examples of AI-based applications in F-RANs, i.e., health monitoring and intelligent transportation systems, are presented, followed by a case study of an RL-based caching application in the presence of spatio-temporal unknown content popularity to showcase the potential of applying AI to F-RANs.

A Dynamic Distributed Spectrum Allocation Mechanism Based on Game Model in Fog Radio Access Networks

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.

基于可再生能量协作的雾无线接入网络资源分配

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。

Machine Learning Based Unmanned Aerial Vehicle Enabled Fog-Radio Access Network and Edge Computing

The emerging unmanned aerial vehicle(UAV)technology and its applications have become part of the massive Internet of Things(mIoT)ecosystem for future cellular networks.Internet of things(IoT)devices have limited computation capacity and battery life and the cloud is not suitable for offloading IoT tasks due to the distance,latency and high energy consumption.Mobile edge computing(MEC)and fog radio access network(F-RAN)together with machine learning algorithms are an emerging approach to solving complex network problems as described above.In this paper,we suggest a new orientation with UAV enabled F-RAN architecture.This architecture adopts the decentralized deep reinforcement learning(DRL)algorithm for edge IoT devices which makes independent decisions to perform computation offloading,resource allocation,and association in the aerial to ground(A2G)network.Addi tionally,we summarized the works on machine learning approaches for UAV networks and MEC networks,which are related to the suggested architecture and discussed some technical challenges in the smart UAV-IoT,F-RAN 5G and Beyond 5G(6G).

Blockchain-based fog radio access networks:Architecture,key technologies,and challenges

Fog Radio Access Network(F-RAN)has been regarded as a promising solution to the alleviation of the ever-increasing traffic burden on current and future wireless networks,for it shifts the caching and computing resources from remote cloud to the network edge.However,it makes wireless networks more vulnerable to security attacks as well.To resolve this issue,in this article,we propose a secure yet trustless Blockchain-based F-RAN(BF-RAN),which allows a massive number of trustless devices to form a large-scale trusted cooperative network by leveraging the key features of blockchain,such as decentralization,tamper-proof,and traceability.The architecture of BF-RAN is first presented.Then,the key technologies,including access control,dynamic resource management,and network deployment are discussed.Finally,challenges and open problems in the BF-RAN are identified.

面向6G的雾无线接入网内生安全数据共享机制研究

为解决6G移动通信系统中雾无线接入网中数据共享的数据安全问题,提出了一种实现本地差分隐私和动态批量审计的内生安全数据共享机制。首先,用户本地对数据运行RAPPOR算法保护数据隐私;其次,雾接入点对数据进行缓存和预处理;再次,大功率节点对雾接入点上的数据进行基于BLS签名和Merkle哈希树的数据完整性审计;最后,BBU池通过统计分析推断出共享数据的原始分布。安全性分析和仿真表明,所提机制实现了用户的本地差分隐私,并支持安全的多客户端批量审计和数据动态操作,同时具有较高的时间、空间和通信效率。

面向5G的三种无线接入网络方案及优劣势比较

5G移动通信系统目前有三种无线接入网络方案,云无线接入网络,异构云无线接入网络和雾无线接入网络,本文分别比较了三种无线接入网络的系统架构,运行原理,各自优势和不足之处,指出了雾无线是最新的5G无线接入网技术具有很大的技术优势.

论陈染内倾型创作方式的成因

陈染的内倾型创作与她体验世界的方式密切相关。陈染的早年人生体验不仅是激发她创作动机的重要因素 ,也影响着她创作题材的选择。基于自身内心经历和体验 ,陈染的内倾型创作方式同时受到西方文化思潮和中国新时期文学思潮的影响。通过了解这些影响 ,我们可以深入理解陈染内倾型创作方式的得失。

Joint uplink and downlink resource allocation for low-latency mobile virtual reality delivery in fog radio access networks

Fog radio access networks(F-RANs),in which the fog access points are equipped with communication,caching,and computing functionalities,have been anticipated as a promising architecture for enabling virtual reality(VR)applications in wireless networks.Although extensive research efforts have been devoted to designing efficient resource allocation strategies for realizing successful mobile VR delivery in downlink,the equally important resource allocation problem of mobile VR delivery in uplink has so far drawn little attention.In this work,we investigate a mobile VR F-RAN delivery framework,where both the uplink and downlink transmissions are considered.We first characterize the round-trip latency of the system,which reveals its dependence on the communication,caching,and computation resource allocations.Based on this information,we propose a simple yet efficient algorithm to minimize the round-trip latency,while satisfying the practical constraints on caching,computation capability,and transmission capacity in the uplink and downlink.Numerical results show that our proposed algorithm can effectively reduce the round-trip latency compared with various baselines,and the impacts of communication,caching,and computing resources on latency performance are illustrated.