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.

A Predictive 6G Network with Environment Sensing Enhancement:From Radio Wave Propagation Perspective

In order to support the future digital society,sixth generation(6G)network faces the challenge to work efficiently and flexibly in a wider range of scenarios.The traditional way of system design is to sequentially get the electromagnetic wave propagation model of typical scenarios firstly and then do the network design by simulation offline,which obviously leads to a 6G network lacking of adaptation to dynamic environments.Recently,with the aid of sensing enhancement,more environment information can be obtained.Based on this,from radio wave propagation perspective,we propose a predictive 6G network with environment sensing enhancement,the electromagnetic wave propagation characteristics prediction enabled network(EWave Net),to further release the potential of 6G.To this end,a prediction plane is created to sense,predict and utilize the physical environment information in EWave Net to realize the electromagnetic wave propagation characteristics prediction timely.A two-level closed feedback workflow is also designed to enhance the sensing and prediction ability for EWave Net.Several promising application cases of EWave Net are analyzed and the open issues to achieve this goal are addressed finally.

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.

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.

Hybrid NOMA Based MIMO Offloading for Mobile Edge Computing in 6G Networks

Non-orthogonal multiple access (NOMA), multiple-input multiple-output (MIMO) and mobile edge computing (MEC) are prominent technologies to meet high data rate demand in the sixth generation (6G) communication networks. In this paper, we aim to minimize the transmission delay in the MIMO-MEC in order to improve the spectral efficiency, energy efficiency, and data rate of MEC offloading. Dinkelbach transform and generalized singular value decomposition (GSVD) method are used to solve the delay minimization problem. Analytical results are provided to evaluate the performance of the proposed Hybrid-NOMA-MIMO-MEC system. Simulation results reveal that the H-NOMA-MIMO-MEC system can achieve better delay performance and lower energy consumption compared to OMA.

Intelligent Decision Making Framework for 6G Network

Sixth Generation(6G)wireless communication network has been expected to provide global coverage,enhanced spectral efficiency,and AI(Artificial Intelligence)-native intelligence,etc.To meet these requirements,the computational concept of Decision-Making of cognition intelligence,its implementation framework adapting to foreseen innovations on networks and services,and its empirical evaluations are key techniques to guarantee the generationagnostic intelligence evolution of wireless communication networks.In this paper,we propose an Intelligent Decision Making(IDM)framework,acting as the role of network brain,based on Reinforcement Learning modelling philosophy to empower autonomous intelligence evolution capability to 6G network.Besides,usage scenarios and simulation demonstrate the generality and efficiency of IDM.We hope that some of the ideas of IDM will assist the research of 6G network in a new or different light.

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.

面向全息通信的6G智简网络架构

全息通信作为一种全新的未来通信范式,通过再现真实物体细粒度画面,向用户提供三维沉浸式的交互体验,克服了传统媒体交互方式单一和场景临场感不足的问题。然而,相对于传统实时通信业务,全方位沉浸的真实全息通信对网络,特别在带宽、时延、安全性等多方面都提出了更为苛刻的要求。如何更好地扩展6G概念与特征,实现高质量、高沉浸的全息新通信成为当前学术界面临的一大挑战。为解决上述问题,率先提出面向全息通信的6G智简网络架构,旨在通过两层网络架构和三维动作模型合理调配6G泛网资源,解决全息通信所面对的超高带宽、超低时延、超大计算等挑战,推动经济社会高质量发展。

Metaheuristic Based Data Gathering Scheme for Clustered UAVs in 6G Communication Network

The sixth-generation(6G)wireless communication networks are anticipated in integrating aerial,terrestrial,and maritime communication into a robust system to accomplish trustworthy,quick,and low latency needs.It enables to achieve maximum throughput and delay for several applications.Besides,the evolution of 6G leads to the design of unmanned aerial vehicles(UAVs)in providing inexpensive and effective solutions in various application areas such as healthcare,environment monitoring,and so on.In the UAV network,effective data collection with restricted energy capacity poses a major issue to achieving high quality network communication.It can be addressed by the use of clustering techniques forUAVs in 6G networks.In this aspect,this study develops a novel metaheuristic based energy efficient data gathering scheme for clustered unmanned aerial vehicles(MEEDG-CUAV).The proposed MEEDG-CUAV technique intends in partitioning the UAV networks into various clusters and assign a cluster head(CH)to reduce the overall energy utilization.Besides,the quantum chaotic butterfly optimization algorithm(QCBOA)with a fitness function is derived to choose CHs and construct clusters.The experimental validation of the MEEDG-CUAV technique occurs utilizing benchmark dataset and the experimental results highlighted the better performance over the other state of art techniques interms of different measures.

An Efficient Scheme for Interference Mitigation in 6G-IoT Wireless Networks

The Internet of Things(IoT)is the fourth technological revolution in the global information industry after computers,the Internet,and mobile communication networks.It combines radio-frequency identification devices,infrared sensors,global positioning systems,and various other technologies.Information sensing equipment is connected via the Internet,thus forming a vast network.When these physical devices are connected to the Internet,the user terminal can be extended and expanded to exchange information,communicate with anything,and carry out identification,positioning,tracking,monitoring,and triggering of corresponding events on each device in the network.In real life,the IoT has a wide range of applications,covering many fields,such as smart homes,smart logistics,fine agriculture and animal husbandry,national defense,and military.One of the most significant factors in wireless channels is interference,which degrades the system performance.Although the existing QR decomposition-based signal detection method is an emerging topic because of its low complexity,it does not solve the problem of poor detection performance.Therefore,this study proposes a maximumlikelihood-based QR decomposition algorithm.The main idea is to estimate the initial level of detection using the maximum likelihood principle,and then the other layer is detected using a reliable decision.The optimal candidate is selected from the feedback by deploying the candidate points in an unreliable scenario.Simulation results show that the proposed algorithm effectively reduces the interference and propagation error compared with the algorithms reported in the literature.

面向星地融合的6G云雾化自组网

低轨星座朝支持星上处理、功能可重构、星地协作和通信遥感导航控制等多任务一体化的方向演进,受限于星上信号处理和计算能力不足、星间链路容量不宽等瓶颈,迫切需要和具有强大云化处理与计算能力的陆地移动通信网络深度融合。为此,提出了面向星地融合的6G云雾化自组网的体系架构和协议体系,实现了低轨雾化卫星网络和陆地云化通信网络的协同融合,研究了基于星上雾化处理的通信遥感导航控制一体化波形和资源管理,以及云雾化协同的移动性管理和智能信息处理等关键技术,展望了未来的标准化工作和硬件试验。

Addressing the CQI feedback delay in 5G/6G networks via machine learning and evolutionary computing

5G networks apply adaptive modulation and coding according to the channel condition reported by the user in order to keep the mobile communication quality.However,the delay incurred by the feedback may make the channel quality indicator(CQI)obsolete.This paper addresses this issue by proposing two approaches,one based on machine learning and another on evolutionary computing,which considers the user context and signal-to-interference-plus-noise ratio(SINR)besides the delay length to estimate the updated SINR to be mapped into a CQI value.Our proposals are designed to run at the user equipment(UE)side,neither requiring any change in the signalling between the base station(gNB)and UE nor overloading the gNB.They are evaluated in terms of mean squared error by adopting 5G network simulation data and the results show their high accuracy and feasibility to be employed in 5G/6G systems.

面向6G全域融合的智能接入关键技术综述

针对空天地一体化接入网络,该文在总结相关研究的基础上,阐述了未来空天地一体化接入架构的关键技术,分析了空口技术、多址技术、干扰分析、计算技术和人工智能(AI)技术等几个重点方向的研究进展,提出了多种接入形式并存的灵活性网络架构。针对6G全域融合网络接入的重点研究问题,结合用户的服务质量需求,构建了一体化AI赋能架构,提出了大规模混合多址接入及弹性资源适配策略。基于网络架构立体化、网络协同传输、一体化网络资源管理、未来空天地接入技术以及网络协同计算等未来重点研究方向进行了讨论和展望。

星载6G核心网架构与网元功能设计及验证

为全面提升卫星的在轨服务能力,将6G核心网与卫星融合。针对6G核心网在低轨卫星部署的迫切需求,设计星载6G核心网体系架构,包括分布式架构、离线自治、网元智能化等功能。对星载6G核心网主要网元进行了优化设计,包括接入与移动性管理、会话管理、分布式服务注册与发现等。通过在轨部署和仿真试验,验证了所提星载6G核心网体系架构的有效性。仿真结果表明,集中式星载5G核心网网元之间的平均通信时延为109.3 ms,而分布式星载6G核心网的平均通信时延为60.3 ms,相较而言降低了44.8%;此外,5G集中式服务注册和服务发现的平均时延分别为40.53 ms和40.04 ms,而6G分布式服务注册和服务发现的平均时延分别为35.18 ms和34.91 ms,分别降低13.2%和12.8%。

Energy-optimal DNN model placement in UAV-enabled edge computing networks

Unmanned aerial vehicle(UAV)-enabled edge computing is emerging as a potential enabler for Artificial Intelligence of Things(AIoT)in the forthcoming sixth-generation(6G)communication networks.With the use of flexible UAVs,massive sensing data is gathered and processed promptly without considering geographical locations.Deep neural networks(DNNs)are becoming a driving force to extract valuable information from sensing data.However,the lightweight servers installed on UAVs are not able to meet the extremely high requirements of inference tasks due to the limited battery capacities of UAVs.In this work,we investigate a DNN model placement problem for AIoT applications,where the trained DNN models are selected and placed on UAVs to execute inference tasks locally.It is impractical to obtain future DNN model request profiles and system operation states in UAV-enabled edge computing.The Lyapunov optimization technique is leveraged for the proposed DNN model placement problem.Based on the observed system overview,an advanced online placement(AOP)algorithm is developed to solve the transformed problem in each time slot,which can reduce DNN model transmission delay and disk I/O energy cost simultaneously while keeping the input data queues stable.Finally,extensive simulations are provided to depict the effectiveness of the AOP algorithm.The numerical results demonstrate that the AOP algorithm can reduce 18.14%of the model placement cost and 29.89%of the input data queue backlog on average by comparing it with benchmark algorithms.

6G网络中面向AI大模型的联邦学习与协同部署技术综述

随着6G网络的逐步推进和AI技术的迅猛发展,AI模型在无线网络中的应用日益广泛。综述了6G网络中面向AI大模型的无线联邦学习与协同部署技术发展现状与挑战。首先,对比了AI大模型与基础模型的区别,并介绍了不同的联邦学习范式。接着,概述了AI大模型在无线网络中的几种高效联邦微调方案,包括低秩自适应、模型分割、用户分簇和跨组织协作微调等。然后,阐述了AI大模型在无线网络中的多种部署方案,包括基于模型压缩的轻量化部署、分布式横向协同模型部署、云边端纵向协同模型部署等。最后,讨论了在6G网络中应用AI大模型面临的挑战与机遇。

6G网络任务卸载与细粒度切片资源调度联合优化算法

针对未来全域、全场景多样化的业务需求,6G网络需要提供场景化、个性化的服务能力。针对未来细粒度业务服务质量保障问题,提出了6G网络任务卸载与细粒度切片资源调度联合优化算法,联合考虑多MEC的计算卸载和网络切片的资源调度,在有限的资源内,最小化任务的执行时延和能耗成本,并采用异步训练的A3C强化学习算法进行求解。仿真结果表明,对比传统算法,该算法可以在满足用户业务需求的情况下降低计算成本,并且算法收敛速度快,可以实现快速决策。

OFDM Based Bidirectional Multi-Relay SWIPT Strategy for 6G IoT Networks

6G IoT networks aim for providing significantly higher data rates and extremely lower latency.However,due to the increasingly scarce spectrum bands and ever-growing massive number IoT devices(IoDs)deployed,6G IoT networks face two critical challenges,i.e.,energy limitation and severe signal attenuation.Simultaneous wireless information and power transfer(SWIPT)and cooperative relaying provide effective ways to address these two challenges.In this paper,we investigate the energy self-sustainability(ESS)of 6G IoT network and propose an OFDM based bidirectional multi-relay SWIPT strategy for 6G IoT networks.In the proposed strategy,the transmission process is equally divided into two phases.Specifically,in phase1 two source nodes transmit their signals to relay nodes which will then use different subcarrier sets to decode information and harvest energy,respectively.In phase2 relay nodes forward signals to corresponding destination nodes with the harvested energy.We maximize the weighted sum transmission rate by optimizing subcarriers and power allocation.Our proposed strategy achieves larger weighted sum transmission rate comparing with the benchmark scheme.

5G-A/6G无线网智能化技术研究

文章从标准化及业务需求等方面分析了5G-A/6G无线网智能化技术的发展趋势,并且提出5G-A/6G阶段无线网智能化演进的三层方案架构,从网元、网络及云端阐述无线网络智能化应具备的能力。基于无线网络智能化演进路线,分析无线网络智能化在业务保障、根因分析、波束管理及绿色节能等方面的应用。文章给出未来网络智能化发展所面临数据、安全、融合及节能方面的挑战,全面分析5G-A/6G无线网智能化技术的特点。

面向6G的信息超材料和智能超表面通信与感知技术

目前,信息超材料和智能超表面(Reconfigurable Intelligent Surface, RIS)已经在全球范围内受到了产业界与学术界的广泛关注,在5G和未来6G网络中具有重要机遇和应用前景。针对信息超材料和智能超表面在6G时代通信和感知的需求,分析与阐述了基于信息超材料和RIS的通信与感知理论、基本算法和系统设计,并对信息超材料和RIS的进一步发展进行了展望。