Channel assignment and power allocation for throughput improvement with PPO in B5G heterogeneous edge networks

In Beyond the Fifth Generation(B5G)heterogeneous edge networks,numerous users are multiplexed on a channel or served on the same frequency resource block,in which case the transmitter applies coding and the receiver uses interference cancellation.Unfortunately,uncoordinated radio resource allocation can reduce system throughput and lead to user inequity,for this reason,in this paper,channel allocation and power allocation problems are formulated to maximize the system sum rate and minimum user achievable rate.Since the construction model is non-convex and the response variables are high-dimensional,a distributed Deep Reinforcement Learning(DRL)framework called distributed Proximal Policy Optimization(PPO)is proposed to allocate or assign resources.Specifically,several simulated agents are trained in a heterogeneous environment to find robust behaviors that perform well in channel assignment and power allocation.Moreover,agents in the collection stage slow down,which hinders the learning of other agents.Therefore,a preemption strategy is further proposed in this paper to optimize the distributed PPO,form DP-PPO and successfully mitigate the straggler problem.The experimental results show that our mechanism named DP-PPO improves the performance over other DRL methods.

A Hybrid Machine Learning Approach for Improvised QoE in Video Services over 5G Wireless Networks

Video streaming applications have grown considerably in recent years.As a result,this becomes one of the most significant contributors to global internet traffic.According to recent studies,the telecommunications industry loses millions of dollars due to poor video Quality of Experience(QoE)for users.Among the standard proposals for standardizing the quality of video streaming over internet service providers(ISPs)is the Mean Opinion Score(MOS).However,the accurate finding of QoE by MOS is subjective and laborious,and it varies depending on the user.A fully automated data analytics framework is required to reduce the inter-operator variability characteristic in QoE assessment.This work addresses this concern by suggesting a novel hybrid XGBStackQoE analytical model using a two-level layering technique.Level one combines multiple Machine Learning(ML)models via a layer one Hybrid XGBStackQoE-model.Individual ML models at level one are trained using the entire training data set.The level two Hybrid XGBStackQoE-Model is fitted using the outputs(meta-features)of the layer one ML models.The proposed model outperformed the conventional models,with an accuracy improvement of 4 to 5 percent,which is still higher than the current traditional models.The proposed framework could significantly improve video QoE accuracy.

Information Centric Networking Based Cooperative Caching Framework for 5G Communication Systems

The demands on conventional communication networks are increasing rapidly because of the exponential expansion of connected multimedia content.In light of the data-centric aspect of contemporary communication,the information-centric network(ICN)paradigm offers hope for a solution by emphasizing content retrieval by name instead of location.If 5G networks are to meet the expected data demand surge from expanded connectivity and Internet of Things(IoT)devices,then effective caching solutions will be required tomaximize network throughput andminimize the use of resources.Hence,an ICN-based Cooperative Caching(ICN-CoC)technique has been used to select a cache by considering cache position,content attractiveness,and rate prediction.The findings show that utilizing our suggested approach improves caching regarding the Cache Hit Ratio(CHR)of 84.3%,Average Hop Minimization Ratio(AHMR)of 89.5%,and Mean Access Latency(MAL)of 0.4 s.Within a framework,it suggests improved caching strategies to handle the difficulty of effectively controlling data consumption in 5G networks.These improvements aim to make the network run more smoothly by enhancing content delivery,decreasing latency,and relieving congestion.By improving 5G communication systems’capacity tomanage the demands faced by modern data-centric applications,the research ultimately aids in advancement.

5G/B5G异构网络切换过程分析和移动性管理解决方案

有效的移动性管理对于确保5G异构网络预期的QoS和QoE至关重要。切换过程在移动性管理中起着至关重要的作用。在此过程中的故障可能会对5G通信网络的可靠性、稳定性和连接性产生不利影响。对5G HetNet的切换过程进行了分析,包括5G切换过程步骤、5G HetNet的三层切换、5G HetNet的关键切换参数。此外,针对具体问题,如超高速、负载均衡和小区间干扰,归纳总结了5G HetNet的移动性管理主要解决方案,并提出了5G/B5G HetNet的移动性管理新方法。

LncRNA LBX2-AS1调节miR-873-5p/G6PD轴对胃癌细胞增殖迁移和侵袭的影响

目的:探讨长链非编码RNA(LncRNA)瓢虫同源盒2反义RNA1(LBX2-AS1)调节微小RNA-873-5p(miR-873-5p)/葡萄糖-6-磷酸脱氢酶(G6PD)轴对胃癌(GC)细胞增殖、迁移和侵袭的影响。方法:以SGC7901细胞为研究对象,将其随机分为Control组、sh-NC组、sh-LBX2-AS1组、sh-LBX2-AS1+inhibitor-NC组、sh-LBX2-AS1+miR-873-5p inhibitor组;qRT-PCR法检测LncRNA LBX2-AS1、miR-873-5p、G6PD的表达;MTT法和平板克隆实验检测SGC7901细胞增殖;划痕实验检测SGC7901细胞迁移;Transwell实验检测SGC7901细胞的侵袭;Western blot检测SGC7901细胞中MMP-2、PCNA、MMP-9、G6PD蛋白表达;荧光素酶报告基因实验检测LncRNA LBX2-AS1与miR-873-5p,miR-873-5p与G6PD之间的相互作用;小鼠荷瘤实验验证敲除LncRNALBX2-AS1对CC肿瘤生长及G6PD表达的影响。结果:sh-LBX2-AS1组SGC7901细胞中A490值、克隆数、划痕愈合率、侵袭数、LncRNA LBX2-AS1、G6PD mRNA和PCNA、MMP-2、MMP-9蛋白表达低于sh-NC组、Control组,miR-873-5p表达高于sh-NC组、Control组(P<0.05);与sh-LBX2-AS1组、sh-LBX2-AS1+inhibitor-NC组相比,sh-LBX2-AS+miR-873-5p inhibitor组miR-873-5p表达降低,A490值、克隆数、划痕愈合率、侵袭数、G6PD mRNA和PCNA、MMP-2、MMP-9蛋白表达升高(P<0.05)。LncRNA LBX2-AS1靶向负调控miR-873-5p,miR-873-5p靶向负调控G6PD。实验组移植瘤质量、体积、Ki-67阳性率、G6PD阳性率低于对照组(P<0.05)。结论:敲除LncRNA LBX2-AS1可能抑制GC细胞的增殖、迁移和侵袭,其机制可能是调节miR-873-5p/G6PD轴实现的。

基于5G+UWB和惯导技术的井下人员定位系统

针对煤矿井下人员定位系统在实际应用中存在因设备算力与存储资源不足导致无法使用复杂测距与定位算法,定位数据即时传输与响应性能不足,在系统部署方面人力物力损耗较大等问题,提出了一种基于5G+UWB和惯导技术的井下人员定位系统。在末端部署能耗低、抗干扰性强的UWB定位基站,定位基站与5G基站以级联的方式连接,定位基站采集UWB与惯导数据,利用5G网络回传至计算平台,在计算平台上完成定位信息的解算和存储。将基于惯导的人员位置估计作为预测值,将基于UWB的三边定位算法获取的人员位置估计作为观测值,利用卡尔曼滤波器将预测值和观测值进行融合,降低定位误差。在煤矿主体实验基地搭建测试系统,模拟真实煤矿井下环境并进行对比实验。结果表明:(1)在x轴和y轴,融合惯导的卡尔曼滤波算法得出的位置信息和真实位置信息的重合度最高,说明融合惯导的卡尔曼滤波算法得出的位置信息最接近真实位置,平均误差为22.192 cm。(2)5G+UWB和惯导技术组合的井下人员定位系统的位置信息和真实位置信息的重合度最高,误差为[15 cm,20 cm],x轴最大平均误差为26 cm,y轴最大平均误差为24 cm,超过目前大多数井下人员定位系统精度。

5G语音EPS Fallback主叫流程优化思路

5G网络实现语音呼叫的方式有2种,一种是EPS Fallback回落方式,另一种是目标VoNR非回落方式。由于EPS Fallback方式需要先回落至4G网络,主叫接通时延相比VoNR方式要慢1 s左右。提出针对EPS Fallback主叫流程的优化思路,通过网络侧升级改造,最终达到EPS Fallback主叫时延与VoNR主叫时延一致的效果。

IBNAD:一种基于交互的5G核心网网络功能异常检测模型

现有5G(5th GenerationMobile Communication Technology)核心网异常检测主要基于信令流量深度解析,但较少利用核心网网络功能交互关系的作用。针对上述问题,提出一种基于交互的5G核心网网络功能异常检测模型。首先,该模型以行为分析为驱动,基于信令流量和网络功能注册数据提取多维属性,通过行为画像来表征网络功能行为模式,并采用集成学习算法RFECV(Recursive Feature Elimination with Cross-Validation)进行属性特征选择,降低特征维度的同时筛选出与区分网络功能行为模式高度相关的属性特征。然后,模型基于网络功能交互关系对核心网进行图建模,建模后的图数据融合了网络功能属性信息和交互信息。最后,模型通过基于空间域的图卷积网络聚合邻域节点属性信息和结构信息来融合行为模式特征,新生成的节点表示用于分类,从而将核心网网络功能异常检测问题转化为图节点分类问题。通过在free5GC仿真平台上采集数据,并在搭建的异常检测系统中的实验表明,该模型的异常检测性能优于基于属性特征分析的传统机器学习模型、基于结构特征分析的图嵌入模型及部分5G核心网异常检测模型。10%数据集作为训练集时,所提模型的准确率比支持向量机模型提高6.6%,比Struc2vec模型提高13%,比深度神经网络模型提高8%。

50 Gbit/s前传光模块关键技术研究与应用展望

随着全球5G网络的规模化商用及移动互联网流量的迅猛增长,更为丰富的无线频谱资源得以释放,高速率光模块在无线前传网络中的需求日益凸显。聚焦于50 Gbit/s前传光模块的关键技术,深入剖析了前传网络的两种主要组网方式,分析了前传链路的典型传输距离及链路损耗情况,探讨了10 km场景下粗波分复用(coarse wavelength division multiplex,CWDM)中各波长的最大色散值和最小色散值,并研究了相应的色散补偿策略,通过实验验证了不同多径干扰(multi-path interference,MPI)强度下系统光功率代价的变化,详细分析了多径干扰噪声对系统误码性能的潜在影响,为前传光模块参数的优化提供了重要的理论依据。此外,深入研究了50 Gbit/s前传光模块的光电芯片技术方案,对多种不同类型光模块的参数性能进行了严格的测试,提出了针对50 Gbit/s前传光模块光电接口参数的标准化建议,详细介绍了当前50 Gbit/s前传光模块在国内外的标准现状,并展望了未来应用的发展趋势。

Machine Learning Based Classifiers for QoE Prediction Framework in Video Streaming over 5G Wireless Networks

Recently,the combination of video services and 5G networks have been gaining attention in the wireless communication realm.With the brisk advancement in 5G network usage and the massive popularity of threedimensional video streaming,the quality of experience(QoE)of video in 5G systems has been receiving overwhelming significance from both customers and service provider ends.Therefore,effectively categorizing QoE-aware video streaming is imperative for achieving greater client satisfaction.This work makes the following contribution:First,a simulation platform based on NS-3 is introduced to analyze and improve the performance of video services.The simulation is formulated to offer real-time measurements,saving the expensive expenses associated with real-world equipment.Second,A valuable framework for QoE-aware video streaming categorization is introduced in 5G networks based on machine learning(ML)by incorporating the hyperparameter tuning(HPT)principle.It implements an enhanced hyperparameter tuning(EHPT)ensemble and decision tree(DT)classifier for video streaming categorization.The performance of the ML approach is assessed by considering precision,accuracy,recall,and computation time metrics for manifesting the superiority of these classifiers regarding video streaming categorization.This paper demonstrates that our ML classifiers achieve QoE prediction accuracy of 92.59%for(EHPT)ensemble and 87.037%for decision tree(DT)classifiers.

Satellite Communications with 5G, B5G, and 6G: Challenges and Prospects

Satellite communications, pivotal for global connectivity, are increasingly converging with cutting-edge mobile networks, notably 5G, B5G, and 6G. This amalgamation heralds the promise of universal, high-velocity communication, yet it is not without its challenges. Paramount concerns encompass spectrum allocation, the harmonization of network architectures, and inherent latency issues in satellite transmissions. Potential mitigations, such as dynamic spectrum sharing and the deployment of edge computing, are explored as viable solutions. Looking ahead, the advent of quantum communications within satellite frameworks and the integration of AI spotlight promising research trajectories. These advancements aim to foster a seamless and synergistic coexistence between satellite communications and next-gen mobile networks.

A 5G Perspective of an SDN-Based Privacy-Preserving Scheme for IoT Networks

The ever-increasing needs of Internet of Things networks (IoTn) present considerable issues in computing complexity, security, trust, and authentication, among others. This gets increasingly more challenging as technology advances, and its use expands. As a consequence, boosting the capacity of these networks has garnered widespread attention. As a result, 5G, the next phase of cellular networks, is expected to be a game-changer, bringing with it faster data transmission rates, more capacity, improved service quality, and reduced latency. However, 5G networks continue to confront difficulties in establishing pervasive and dependable connections amongst high-speed IoT devices. Thus, to address the shortcomings in current recommendations, we present a unified architecture based on software-defined networks (SDNs) that provides 5G-enabled devices that must have complete secrecy. Through SDN, the architecture streamlines network administration while optimizing network communications. A mutual authentication protocol using elliptic curve cryptography is introduced for mutual authentication across certificate authorities and clustered heads in IoT network deployments based on IoT. Again, a dimensionality reduction intrusion detection mechanism is introduced to decrease computational cost and identify possible network breaches. However, to leverage the method’s potential, the initial module's security is reviewed. The second module is evaluated and compared to modern models.

Advancing 5G Network Applications Lifecycle Security:An ML-Driven Approach

As 5th Generation(5G)and Beyond 5G(B5G)networks become increasingly prevalent,ensuring not only networksecurity but also the security and reliability of the applications,the so-called network applications,becomesof paramount importance.This paper introduces a novel integrated model architecture,combining a networkapplication validation framework with an AI-driven reactive system to enhance security in real-time.The proposedmodel leverages machine learning(ML)and artificial intelligence(AI)to dynamically monitor and respond tosecurity threats,effectively mitigating potential risks before they impact the network infrastructure.This dualapproach not only validates the functionality and performance of network applications before their real deploymentbut also enhances the network’s ability to adapt and respond to threats as they arise.The implementation ofthis model,in the shape of an architecture deployed in two distinct sites,demonstrates its practical viability andeffectiveness.Integrating application validation with proactive threat detection and response,the proposed modeladdresses critical security challenges unique to 5G infrastructures.This paper details the model,architecture’sdesign,implementation,and evaluation of this solution,illustrating its potential to improve network securitymanagement in 5G environments significantly.Our findings highlight the architecture’s capability to ensure boththe operational integrity of network applications and the security of the underlying infrastructure,presenting asignificant advancement in network security.

GPRC5A调控的ABCB1表达对肺腺癌增殖的影响

目的ATP结合盒B亚家族成员1(ATP binding cassette subfamily B member 1,ABCB1)的异常表达在多种癌症的发生发展中发挥关键作用。然而,G蛋白偶联受体C家族5组A型(G protein coupled receptor family C group5 type A,GPRC5A)调控的ABCB1表达对肺腺癌增殖的影响仍不清楚。本研究探讨了GPRC5A调控的ABCB1表达对肺腺癌增殖的影响。方法我们采用RT-PCR、Western-blot或免疫组化实验,分析ABCB1在肺腺癌细胞系、人肺腺癌组织以及GPRC5A基因敲除小鼠和野生型小鼠的气管上皮细胞和肺组织中的表达。采用细胞计数试剂盒-8(CCK-8)分析GPRC5A基因敲除小鼠气管上皮细胞对化疗药物的敏感性。采用皮下肿瘤形成实验探讨下调ABCB1表达是否可抑制体内肺腺癌增殖。采用免疫荧光和免疫沉淀实验研究GPRC5A和ABCB1之间潜在的调控关系。结果ABCB1在肺腺癌细胞系和人类肺腺癌组织中表达上调。GPRC5A基因敲除小鼠的气管上皮细胞及肺组织的ABCB1表达高于野生型小鼠。与GPRC5A野生型小鼠的气管上皮细胞相比,GPRC5A基因敲除小鼠的气管上皮细胞对塔立奇达和多柔比星更敏感。注射移植细胞28天后,接受ABCB1基因敲除细胞移植的GPRC5A-/-C57BL/6小鼠的肺肿瘤的体积和重量均明显低于野生型细胞移植小鼠(P=0.0043,P=0.0060)。此外,免疫荧光和免疫沉淀实验表明,GPRC5A通过直接结合方式调控ABCB1的表达。结论GPRC5A通过抑制ABCB1表达降低肺腺癌增殖。GPRC5A调节ABCB1表达的途径有待研究。

QoE oriented intelligent online learning evaluation technology in B5G scenario

Students'demand for online learning has exploded during the post-COvID-19 pandemic era.However,due to their poor learning experience,students'dropout rate and learning performance of online learning are not always satisfactory.The technical advantages of Beyond Fifth Generation(B5G)can guarantee a good multimedia Quality of Experience(QoE).As a special case of multimedia services,online learning takes into account both the usability of the service and the cognitive development of the users.Factors that affect the Quality of Online Learning Experience(OL-QoE)become more complicated.To get over this dilemma,we propose a systematic scheme by integrating big data,Machine Learning(ML)technologies,and educational psychology theory.Specifically,we first formulate a general definition of OL-QoE by data analysis and experimental verification.This formula considers both the subjective and objective factors(i.e.,video watching ratio and test scores)that most affect OLQoE.Then,we induce an extended layer to the classic Broad Learning System(BLS)to construct an Extended Broad Learning System(EBLS)for the students'OL-QoE prediction.Since the extended layer can increase the width of the BLS model and reduce the redundant nodes of BLS,the proposed EBLS can achieve a trade-off between the prediction accuracy and computation complexity.Finally,we provide a series of early intervention suggestions for different types of students according to their predicted OL-QoE values.Through timely interventions,their OL-QoE and learning performance can be improved.Experimental results verify the effectiveness oftheproposed scheme.

A 24−30 GHz 8-element dual-polarized 5G FR2 phased-array transceiver IC with 20.8-dBm TX OP1dB and 4.1-dB RX NF in 65-nm CMOS

This article presents an 8-element dual-polarized phased-array transceiver(TRX)front-end IC for millimeter-wave(mm-Wave)5G new radio(NR).Power enhancement technologies for power amplifiers(PA)in mm-Wave 5G phased-array TRX are discussed.A four-stage wideband high-power class-AB PA with distributed-active-transformer(DAT)power combining and multi-stage second-harmonic traps is proposed,ensuring the mitigated amplitude-to-phase(AM-PM)distortions across wide carrier frequencies without degrading transmitting(TX)power,gain and efficiency.TX and receiving(RX)switching is achieved by a matching network co-designed on-chip T/R switch.In each TRX element,6-bit 360°phase shifting and 6-bit 31.5-dB gain tuning are respectively achieved by the digital-controlled vector-modulated phase shifter(VMPS)and differential attenuator(ATT).Fabricated in 65-nm bulk complementary metal oxide semiconductor(CMOS),the proposed TRX demonstrates the measured peak TX/RX gains of 25.5/21.3 dB,covering the 24−29.5 GHz band.The measured peak TX OP1dB and power-added efficiency(PAE)are 20.8 dBm and 21.1%,respectively.The measured minimum RX NF is 4.1 dB.The TRX achieves an output power of 11.0−12.4 dBm and error vector magnitude(EVM)of 5%with 400-MHz 5G NR FR2 OFDM 64-QAM signals across 24−29.5 GHz,covering 3GPP 5G NR FR2 operating bands of n257,n258,and n261.

Analysis on unit maximum capacity of orthogonal multiple watermarking for multimedia signals in B5G wireless communications

Beyond-5G(B5G)aims to meet the growing demands of mobile traffic and expand the communication space.Considering that intelligent applications to B5G wireless communications will involve security issues regarding user data and operational data,this paper analyzes the maximum capacity of the multi-watermarking method for multimedia signal hiding as a means of alleviating the information security problem of B5G.The multiwatermarking process employs spread transform dither modulation.During the watermarking procedure,Gram-Schmidt orthogonalization is used to obtain the multiple spreading vectors.Consequently,multiple watermarks can be simultaneously embedded into the same position of a multimedia signal.Moreover,the multiple watermarks can be extracted without affecting one another during the extraction process.We analyze the effect of the size of the spreading vector on the unit maximum capacity,and consequently derive the theoretical relationship between the size of the spreading vector and the unit maximum capacity.A number of experiments are conducted to determine the optimal parameter values for maximum robustness on the premise of high capacity and good imperceptibility.

A Hybrid Integrated and Low-Cost Multi-Chip Broadband Doherty Power Amplifier Module for 5G Massive MIMO Application

In this paper,a hybrid integrated broadband Doherty power amplifier(DPA)based on a multi-chip module(MCM),whose active devices are fabricated using the gallium nitride(GaN)process and whose passive circuits are fabricated using the gallium arsenide(GaAs)integrated passive device(IPD)process,is proposed for 5G massive multiple-input multiple-output(MIMO)application.An inverted DPA structure with a low-Q output network is proposed to achieve better bandwidth performance,and a single-driver architecture is adopted for a chip with high gain and small area.The proposed DPA has a bandwidth of 4.4-5.0 GHz that can achieve a saturation of more than 45.0 dBm.The gain compression from 37 dBm to saturation power is less than 4 dB,and the average power-added efficiency(PAE)is 36.3%with an 8.5 dB peak-to-average power ratio(PAPR)in 4.5-5.0 GHz.The measured adjacent channel power ratio(ACPR)is better than50 dBc after digital predistortion(DPD),exhibiting satisfactory linearity.

An artificial intelligence diabetes management architecture based on 5G

Along with the development of 5G network and Internet of Things technologies,there has been an explosion in personalized healthcare systems.When the 5G and Artificial Intelligence(Al)is introduced into diabetes management architecture,it can increase the efficiency of existing systems and complications of diabetes can be handled more effectively by taking advantage of 5G.In this article,we propose a 5G-based Artificial Intelligence Diabetes Management architecture(AIDM),which can help physicians and patients to manage both acute complications and chronic complications.The AIDM contains five layers:the sensing layer,the transmission layer,the storage layer,the computing layer,and the application layer.We build a test bed for the transmission and application layers.Specifically,we apply a delay-aware RA optimization based on a double-queue model to improve access efficiency in smart hospital wards in the transmission layer.In application layer,we build a prediction model using a deep forest algorithm.Results on real-world data show that our AIDM can enhance the efficiency of diabetes management and improve the screening rate of diabetes as well.

Reinforcement learning based edge computing in B5G

The development of communication technology will promote the application of Internet of Things,and Beyond 5G will become a new technology promoter.At the same time,Beyond 5G will become one of the important supports for the development of edge computing technology.This paper proposes a communication task allocation algorithm based on deep reinforcement learning for vehicle-to-pedestrian communication scenarios in edge computing.Through trial and error learning of agent,the optimal spectrum and power can be determined for transmission without global information,so as to balance the communication between vehicle-to-pedestrian and vehicle-to-infrastructure.The results show that the agent can effectively improve vehicle-to-infrastructure communication rate as well as meeting the delay constraints on the vehicle-to-pedestrian link.