A Review and Bibliometric Analysis of the Current Studies for the 6G Networks

The race to develop the next generation of wireless networks,known as Sixth Generation(6G)wireless,which will be operational in 2030,has already begun.To realize its full potential over the next decade,6G will undoubtedly necessitate additional improvements that integrate existing solutions with cutting-edge ones.However,the studies about 6G are mainly limited and scattered,whereas no bibliometric study covers the 6G field.Thus,this study aims to review,examine,and summarize existing studies and research activities in 6G.This study has examined the Scopus database through a bibliometric analysis of more than 1,000 papers published between 2017 and 2021.Then,we applied the bibliometric analysis methods by including(1)document type,(2)subject area,(3)author,and(4)country of publication.The study’s results reflect the research 6G community’s trends,highlight important research challenges,and elucidate potential directions for future research in this interesting area.

Intelligent UAV Based Energy Supply for 6G Wireless Powered IoT Networks

In this paper,we develop a 6G wireless powered Internet of Things(IoT)system assisted by unmanned aerial vehicles(UAVs)to intelligently supply energy and collect data at the same time.In our dual-UAV scheme,UAV-E,with a constant power supply,transmits energy to charge the IoT devices on the ground,whereas UAV-B serves the IoT devices by data collection as a base station.In this framework,the system's energy efficiency is maximized,which we define as a ratio of the sum rate of IoT devices to the energy consumption of two UAVs during a fixed working duration.With the constraints of duration,transmit power,energy,and mobility,a difficult non-convex issue is presented by optimizing the trajectory,time duration allocation,and uplink transmit power of concurrently.To tackle the non-convex fractional optimization issue,we deconstruct it into three subproblems and we solve each of them iteratively using the descent method in conjunction with sequential convex approximation(SCA)approaches and the Dinkelbach algorithm.The simulation findings indicate that the suggested cooperative design has the potential to greatly increase the energy efficiency of the 6G intelligent UAV-assisted wireless powered IoT system when compared to previous benchmark systems.

A Probabilistic Trust Model and Control Algorithm to Protect 6G Networks against Malicious Data Injection Attacks in Edge Computing Environments

Future 6G communications are envisioned to enable a large catalogue of pioneering applications.These will range from networked Cyber-Physical Systems to edge computing devices,establishing real-time feedback control loops critical for managing Industry 5.0 deployments,digital agriculture systems,and essential infrastructures.The provision of extensive machine-type communications through 6G will render many of these innovative systems autonomous and unsupervised.While full automation will enhance industrial efficiency significantly,it concurrently introduces new cyber risks and vulnerabilities.In particular,unattended systems are highly susceptible to trust issues:malicious nodes and false information can be easily introduced into control loops.Additionally,Denialof-Service attacks can be executed by inundating the network with valueless noise.Current anomaly detection schemes require the entire transformation of the control software to integrate new steps and can only mitigate anomalies that conform to predefined mathematical models.Solutions based on an exhaustive data collection to detect anomalies are precise but extremely slow.Standard models,with their limited understanding of mobile networks,can achieve precision rates no higher than 75%.Therefore,more general and transversal protection mechanisms are needed to detect malicious behaviors transparently.This paper introduces a probabilistic trust model and control algorithm designed to address this gap.The model determines the probability of any node to be trustworthy.Communication channels are pruned for those nodes whose probability is below a given threshold.The trust control algorithmcomprises three primary phases,which feed themodel with three different probabilities,which are weighted and combined.Initially,anomalous nodes are identified using Gaussian mixture models and clustering technologies.Next,traffic patterns are studied using digital Bessel functions and the functional scalar product.Finally,the information coherence and content are analyzed.The noise content and abnormal information sequences are detected using a Volterra filter and a bank of Finite Impulse Response filters.An experimental validation based on simulation tools and environments was carried out.Results show the proposed solution can successfully detect up to 92%of malicious data injection attacks.

Distributed Application Addressing in 6G Network

To ensure the extreme performances of the new 6G services,applications will be deployed at deep edge,resulting in a serious challenge of distributed application addressing.This paper traces back the latest development of mobile network application addressing,analyzes two novel addressing methods in carrier network,and puts forward a 6G endogenous application addressing scheme by integrating some of their essence into the 6G network architecture,combining the new 6G capabilities of computing&network convergence,endogenous intelligence,and communication-sensing integration.This paper further illustrates how that the proposed method works in 6G networks and gives preliminary experimental verification.

Intelligent 6G Wireless Network with Multi-Dimensional Information Perception

Intelligence and perception are two operative technologies in 6G scenarios.The intelligent wireless network and information perception require a deep fusion of artificial intelligence(AI)and wireless communications in 6G systems.Therefore,fusion is becoming a typical feature and key challenge of 6G wireless communication systems.In this paper,we focus on the critical issues and propose three application scenarios in 6G wireless systems.Specifically,we first discuss the fusion of AI and 6G networks for the enhancement of 5G-advanced technology and future wireless communication systems.Then,we introduce the wireless AI technology architecture with 6G multidimensional information perception,which includes the physical layer technology of multi-dimensional feature information perception,full spectrum fusion technology,and intelligent wireless resource management.The discussion of key technologies for intelligent 6G wireless network networks is expected to provide a guideline for future research.

Ultra Dense Satellite-Enabled 6G Networks:Resource Optimization and Interference Management

With the evolution of the sixth generation(6G)mobile communication technology,ample attention has gone to the integrated terrestrial-satellite networks.This paper notes that four typical application scenarios of integrated terrestrial-satellite networks are integrated into ultra dense satellite-enabled 6G networks architecture.Then the subchannel and power allocation schemes for the downlink of the ultra dense satellite-enabled 6G heterogeneous networks are introduced.Satellite mobile edge computing(SMEC)with edge caching in three-layer heterogeneous networks serves to reduce the link traffic of networks.Furthermore,a scheme for interference management is presented,involving quality-of-service(QoS)and co-tier/cross-tier interference constraints.The simulation results show that the proposed schemes can significantly increase the total capacity of ultra dense satellite-enabled 6G heterogeneous networks.

m^(6)A相关基因在激素性股骨头坏死中的生物信息学分析

背景:m^(6)A修饰与股骨头坏死的发生发展相关,但在激素性股骨头坏死中的作用尚不清楚。目的:基于GEO数据库,采用生物信息学方法分析激素性股骨头坏死中表达差异的m^(6)A基因及互作miRNAs,探寻其潜在发病机制。方法:在GEO数据库中检索并下载与激素性股骨头坏死相关的mRNA表达谱数据集(GSE123568),通过R软件对数据集进行差异基因筛选及GO功能、KEGG通路富集分析。识别差异基因中的m^(6)A差异表达基因(m^(6)A-DEGs)并对其进行GO功能与KEGG通路富集分析,比较m^(6)A-DEGs的表达量并分析它们之间的相关性。最后通过Cytoscape构建m^(6)A-DEGs的PPI互作网络及筛选核心基因。使用TargetScan,miRTarBase和miRBD数据库预测m^(6)A-DEGs相关的潜在miRNAs,同时使用ChIPBase及hTFtarget数据库预测7个核心基因潜在转录因子,然后分别构建m^(6)A-miRNA与转录因子m^(6)A调控网络。最后使用数据集GSE74089验证7个核心m^(6)A-DEGs的表达水平。结果与结论:①从数据集中共筛选出2460个差异表达的基因,其中1455个上调,1005个下调。②从数据集中筛选出了14个m^(6)A-DEGs,包括3个下调和11个上调基因,m^(6)A-DEGs在激素性股骨头坏死中的表达具有显著差异(P<0.05),Spearman分析表明它们之间具有一定相关性。③m^(6)A-DEGs的GO和KEGG富集分析主要集中在骨髓细胞分化与发育、免疫受体与细胞因子受体活性、破骨细胞分化、AMPK与白细胞介素17信号通路。④m^(6)A-DEGs前7个核心基因包括YTHDF3,YTHDF1,YTHDF2,ALKBH5,METTL3,HNRNPA2B1及HNRNPC,它们在miRTarBase,miRDB和TargetScan数据库中共有44个miRNA重叠,在ChIPBase及hTFtarget数据库中共有79个重叠转录因子。⑤在GSE74089数据集中有6个核心m^(6)A-DEGs的表达水平与GSE123568数据集一致。⑥结果证实,根据生物信息学方法筛选的7个m^(6)A-DEGs可能通过调控多个miRNA、转录因子和AMPK及白细胞介素17信号通路表达,进而影响激素性股骨头坏死中骨髓细胞分化发育与破骨细胞分化,为进一步深入研究激素性股骨头坏死的发病机制和靶向治疗提供了数据支持和研究方向。

Intelligent Modulation Recognition of Communication Signal for Next-Generation 6G Networks

In recent years,the need for a fast,efficient and a reliable wireless network has increased dramatically.Numerous 5G networks have already been tested while a few are in the early stages of deployment.In noncooperative communication scenarios,the recognition of digital signal modulations assists people in identifying the communication targets and ensures an effective management over them.The recent advancements in both Machine Learning(ML)and Deep Learning(DL)models demand the development of effective modulation recognition models with self-learning capability.In this background,the current research article designs aDeep Learning enabled Intelligent Modulation Recognition of Communication Signal(DLIMR-CS)technique for next-generation networks.The aim of the proposed DLIMR-CS technique is to classify different kinds of digitally-modulated signals.In addition,the fractal feature extraction process is appliedwith the help of the Sevcik Fractal Dimension(SFD)approach.Then,the extracted features are fed into the Deep Variational Autoencoder(DVAE)model for the classification of the modulated signals.In order to improve the classification performance of the DVAE model,the Tunicate Swarm Algorithm(TSA)is used to finetune the hyperparameters involved in DVAE model.A wide range of simulations was conducted to establish the enhanced performance of the proposed DLIMR-CS model.The experimental outcomes confirmed the superior recognition rate of the DLIMR-CS model over recent state-of-the-art methods under different evaluation parameters.

基于GF-6/WFV卫星遥感的大田冬小麦叶片氮素含量估测

为对大田冬小麦叶片氮素含量(LNC)进行快速、准确及无损监测,通过在江苏省泰州泰兴市、盐城大丰区和南通如皋市布设冬小麦遥感监测大田试验,在获取试验样点冬小麦冠层红光波段反射率(REDref)、近红外波段反射率(NIRref)和计算的十个光谱指数(RVI、NDVI、DVI、SAVI、OSAVI、MSR、RDVI、EVI2、NLI和SVI)基础上,将12个遥感光谱指标与冬小麦LNC进行相关分析,选出与LNC相关性较好的作为模型输入变量,构建基于BP神经网络的冬小麦LNC估测模型,并利用GF-6/WFV卫星遥感影像对县域冬小麦LNC的空间分布开展监测。结果表明,12个遥感光谱指标与冬小麦LNC之间存在不同程度的相关性,其中NDVI、RVI、MSR、OSAVI和NLI与冬小麦LNC的相关性较好(相关系数不低于0.65)。将优选的5个遥感光谱指标作为模型输入变量,构建基于BP神经网络的冬小麦LNC估测模型(LNC-BPEM),模型的估测精度r^(2)=0.866,RMSE=0.246%,ARE=12.9%。将冬小麦LNC-BPEM估测模型和GF-6/WFV影像结合对县域冬小麦LNC的空间信息监测,获得了如皋县域冬小麦LNC的空间分布特征,该区域冬小麦LNC范围在0.9%~2.0%(长势正常)的种植面积为29 693.3 hm^(2),占冬小麦总种植面积的74%。这说明利用GF-6/WFV卫星的多个遥感光谱指标与神经网络结合建模可有效估测县域大田冬小麦叶片氮素含量。

基于LSTM⁃MIV神经网络的SF_(6)断路器触头电寿命预测

文中基于LW30⁃40.5型SF_(6)断路器全寿命烧蚀试验数据,采用归一化异常指数F反映断路器触头剩余电寿命,0表示电寿命完备,1表示电寿命耗尽。根据断路器分闸动态电阻—行程曲线,提出了将动态电阻—行程曲线上行程大于8 mm且电阻值大于500μΩ的第一个峰值点作为断路器主触头接触阶段与弧触头接触阶段分界点。据此得到反映断路器触头异常指数F的5个特征参数,采用皮尔逊相关系数法对特征参数进行独立性分析。通过LSTM神经网络建立了基于特征参数与异常指数F之间的回归预测模型,F回归预测均方差和绝对平均误差分别为0.014921和0.013053,采用MIV方法确定了各特征参数对异常指数F影响权重。

Identification and characterization of genes related to m^(6)A modification in kiwifruit using RNA-seq and ATAC-seq

N6-methyladenosine(m^(6)A)RNA modification is a conserved mechanism that regulates the fate of RNA across eukaryotic organisms.Despite its significance,a comprehensive analysis of m^(6)A-related genes in non-model plants,such as kiwifruit,is lacking.Here,we identified 36 m^(6)A-related genes in the kiwifruit genome according to homology and phylogenetic inference.We performed bioinformatics and evolutionary analyses of the writer,eraser,and reader families of m^(6)A modification.Reanalysis of public RNA-seq data collected from samples under various biotic and abiotic stresses indicated that most m^(6)A-related genes were remarkably expressed under different conditions.Through construction of gene co-expression networks,we found significant correlations between several m^(6)A-related genes and transcription factors(TFs)as well as receptor-like genes during the development and ripening of kiwifruit.Furthermore,we performed ATAC-seq assays on diverse kiwifruit tissues to investigate the regulatory mechanisms of m^(6)A-related genes.We identified 10 common open chromatin regions that were present in at least two tissues,and these regions might serve as potential binding sites for MADS protein,C2H2 protein,and other predicted TFs.Our study offers comprehensive insights into the gene family of m^(6)A-related components in kiwifruit,which will lay foundation for exploring mechanisms of post-transcriptional regulation involved in development and adaptation of kiwifruit.

A Novel 6G Scalable Blockchain Clustering-Based Computer Vision Character Detection for Mobile Images

6G is envisioned as the next generation of wireless communication technology,promising unprecedented data speeds,ultra-low Latency,and ubiquitous Connectivity.In tandem with these advancements,blockchain technology is leveraged to enhance computer vision applications’security,trustworthiness,and transparency.With the widespread use of mobile devices equipped with cameras,the ability to capture and recognize Chinese characters in natural scenes has become increasingly important.Blockchain can facilitate privacy-preserving mechanisms in applications where privacy is paramount,such as facial recognition or personal healthcare monitoring.Users can control their visual data and grant or revoke access as needed.Recognizing Chinese characters from images can provide convenience in various aspects of people’s lives.However,traditional Chinese character text recognition methods often need higher accuracy,leading to recognition failures or incorrect character identification.In contrast,computer vision technologies have significantly improved image recognition accuracy.This paper proposed a Secure end-to-end recognition system(SE2ERS)for Chinese characters in natural scenes based on convolutional neural networks(CNN)using 6G technology.The proposed SE2ERS model uses the Weighted Hyperbolic Curve Cryptograph(WHCC)of the secure data transmission in the 6G network with the blockchain model.The data transmission within the computer vision system,with a 6G gradient directional histogram(GDH),is employed for character estimation.With the deployment of WHCC and GDH in the constructed SE2ERS model,secure communication is achieved for the data transmission with the 6G network.The proposed SE2ERS compares the performance of traditional Chinese text recognition methods and data transmission environment with 6G communication.Experimental results demonstrate that SE2ERS achieves an average recognition accuracy of 88%for simple Chinese characters,compared to 81.2%with traditional methods.For complex Chinese characters,the average recognition accuracy improves to 84.4%with our system,compared to 72.8%with traditional methods.Additionally,deploying the WHCC model improves data security with the increased data encryption rate complexity of∼12&higher than the traditional techniques.

基于Ipv6的5G移动通信网络组网设计研究

文章针对网络通信传输效果不佳的问题,提出一种基于互联网协议第6版(Internet Protocol version 6,IPv6)的5G移动通信网络组网设计方案。通过选取网络通信性能指标,引入网络性能改善(Network Performance Improvement,NPI)指数构建通信需求标准。结合IPv6,采用支持IPv6的动态主机配置协议(Dynamic Host Configuration Protocol for IPv6,DHCPv6)分配网络节点网际互连协议(Internet Protocol,IP)地址,完成节点传输链路建模。引入冗余节点,构建网络节点自适应调度模型,根据调度结果完成核心交换机部署,从而搭建通信组网结构。测试结果表明,采用提出方法构建的通信网络组网,节点通信能耗值较低,通信传输效果较为理想。

基于GA-BP神经网络的SF_(6)/N_(2)混合气体GIS设备故障诊断

为实现SF_(6)/N_(2)混合气体GIS设备的潜伏性故障诊断,搭建实验平台探究SO_(2)浓度与放电量之间的关系,以SO_(2)浓度增量为输入参数之一,建立BP、GA-BP神经网络模型来预测四种缺陷类型下的放电量,分析预测结果后选取最优诊断算法。结果表明:每种缺陷类型下的放电量与SO_(2)浓度之间呈现正相关性,GA-BP神经网络模型的预测准确率和拟合度分别为0.86和0.9386,平均相对误差为1.23%,在评估结果中占明显优势,可为SF_(6)/N_(2)混合气体GIS设备的潜伏性故障建立诊断算法提供基础性数据。

高职院校数字化校园网络基础环境建设研究——基于IPv6的校园网升级改造

文章以临夏现代职业学院校园网互联网协议版本6(Internet Protocol version 6,IPv6)升级改造为例,介绍互联网协议版本4(Internet Protocol version 4,IPv4)校园网现状和存在的问题,比较IPv4与IPv6的优缺点,结合IPv6迁移改造的双协议栈、隧道技术,探索规划阶段性升级改造IPv6的建设方案,重点讨论IPv4/IPv6双栈校园网技术实现。

广电网络向IPv6过渡的技术挑战与解决方案

随着互联网用户数量的迅猛增长和物联网设备的普及,全球范围内的网络运营商和服务提供商逐步开始向网际互连协议第6版(Internet Protocol Version 6,IPv6)过渡。在这一背景下,广电网络作为重要的通信基础设施,也面临着向IPv6过渡的迫切需求。基于此,深入分析广电网络向IPv6过渡的技术挑战并提出针对性的解决方案,为未来的广电网络发展提供参考。

5G IPv6网络中的区域性数据加密与隐私保护研究

5G环境下的互联网协议第6版(Internet Protocol Version 6,IPv6)网络面临着诸多安全挑战和隐私风险,包括数据窃取、身份盗用及网络攻击等,对用户隐私保护提出了更高的要求。因此,探索适用于IPv6的区域性数据加密技术和隐私保护策略显得尤为重要。文章探讨了5G环境下IPv6网络面临的安全挑战和隐私风险,分析多种区域性数据加密和网络隐私保护策略,为实现更安全、更私密的网络通信环境提供理论依据和实践指南。

IP技术应用下医院IPv4网络向IPv6网络过渡技术研究

文章主要探讨了医院网际互连协议第4版(Internet Protocol version 4,IPv4)网络向网际互连协议第6版(Internet Protocol version 6,IPv6)网络过渡的通信技术应用,重点分析了双栈技术、隧道技术以及转换技术在过渡过程中的应用。通过华为eNSP模拟器构建双栈网络环境,并进行多项测试验证其可行性。结果表明,双栈技术能有效支持IPv4和IPv6协议共存,为医院网络现代化提供技术保障。同时,研究强调在过渡过程中需考虑的安全挑战和设备升级问题,提出了具体的实施策略和优化建议。

6kV高压电动机故障智能诊断方法

针对6 kV高压电动机故障智能诊断方法存在错诊率较高和诊断响应时间较长的问题,提出6k V高压电动机故障智能诊断方法。利用无线传感器采集电动机运行数据,利用降噪自编码网络模型对数据去噪处理,利用小波包分析技术获取数据小波包能量熵信息,提取数据特征,根据计算故障特征频率,诊断电动机故障类型,以完成6 kV高压电动机故障智能诊断。实验证明,该设计方法错诊率在1%以内,能在1s内完成故障诊断,具有良好的应用前景。

运用Network Coding改进IPv6网络的邻居发现协议

通过对IPv6网络中,对网络管理、拓扑发现等起着重要作用的以ICMPv6报文为基础的邻居发现协议相关算法的分析,结合Network Coding的基本思想,针对广泛使用多播包容易引发网络拥塞、降低链接带宽利用率这一问题,提出了一种理论上基于Network Coding传输模式的改进方案,并以一个实例验证其有效性.