As investigated by 3GPP,support of UPF(user plane function)onboard satellite can reduce the latency of communications via satellite,and then it becomes a key enhancement in 5G network integrating with satellite commun...As investigated by 3GPP,support of UPF(user plane function)onboard satellite can reduce the latency of communications via satellite,and then it becomes a key enhancement in 5G network integrating with satellite communication.However,current 5G system cannot support UPF onboard LEO(low earth orbit)satellites,as it would face challenges like UPF mobility handling,synchronization between mobile network and satellite network,and condition of activating local data switching.To solve such challenges,this paper proposes a solution to support UPF onboard LEO satellite,which consists of enhanced network architecture,I-UPF(intermediate UPF)based local data switching scheme and communication latency based data path selection.We subsequently develop analytic models for performance evaluation and conduct simulations using the constellation configuration of iridium II.The simulation results show that the data switching via I-UPF onboard LEO satellite can reduce E2E(end to end)packet delivery latency and E2E packet loss ratio significantly compared with that of routing the data back to 5GC on the ground.The proposed scheme yet has increased signaling cost for handling UPF mobility.els,compared with existing similar companding algorithms.展开更多
The emergence of next generation networks(NextG),including 5G and beyond,is reshaping the technological landscape of cellular and mobile networks.These networks are sufficiently scaled to interconnect billions of user...The emergence of next generation networks(NextG),including 5G and beyond,is reshaping the technological landscape of cellular and mobile networks.These networks are sufficiently scaled to interconnect billions of users and devices.Researchers in academia and industry are focusing on technological advancements to achieve highspeed transmission,cell planning,and latency reduction to facilitate emerging applications such as virtual reality,the metaverse,smart cities,smart health,and autonomous vehicles.NextG continuously improves its network functionality to support these applications.Multiple input multiple output(MIMO)technology offers spectral efficiency,dependability,and overall performance in conjunctionwithNextG.This article proposes a secure channel estimation technique in MIMO topology using a norm-estimation model to provide comprehensive insights into protecting NextG network components against adversarial attacks.The technique aims to create long-lasting and secure NextG networks using this extended approach.The viability of MIMO applications and modern AI-driven methodologies to combat cybersecurity threats are explored in this research.Moreover,the proposed model demonstrates high performance in terms of reliability and accuracy,with a 20%reduction in the MalOut-RealOut-Diff metric compared to existing state-of-the-art techniques.展开更多
The explosive growth of data traffic and heterogeneous service requirements of 5G networks—covering Enhanced Mobile Broadband(eMBB),Ultra-Reliable Low Latency Communication(URLLC),and Massive Machine Type Communicati...The explosive growth of data traffic and heterogeneous service requirements of 5G networks—covering Enhanced Mobile Broadband(eMBB),Ultra-Reliable Low Latency Communication(URLLC),and Massive Machine Type Communication(mMTC)—present tremendous challenges to conventional methods of bandwidth allocation.A new deep reinforcement learning-based(DRL-based)bandwidth allocation system for real-time,dynamic management of 5G radio access networks is proposed in this paper.Unlike rule-based and static strategies,the proposed system dynamically updates itself according to shifting network conditions such as traffic load and channel conditions to maximize the achievable throughput,fairness,and compliance with QoS requirements.By using extensive simulations mimicking real-world 5G scenarios,the proposed DRL model outperforms current baselines like Long Short-Term Memory(LSTM),linear regression,round-robin,and greedy algorithms.It attains 90%–95%of the maximum theoretical achievable throughput and nearly twice the conventional equal allocation.It is also shown to react well under delay and reliability constraints,outperforming round-robin(hindered by excessive delay and packet loss)and proving to be more efficient than greedy approaches.In conclusion,the efficiency of DRL in optimizing the allocation of bandwidth is highlighted,and its potential to realize self-optimizing,Artificial Intelligence-assisted(AI-assisted)resource management in 5G as well as upcoming 6G networks is revealed.展开更多
Smart edge computing(SEC)is a novel paradigm for computing that could transfer cloud-based applications to the edge network,supporting computation-intensive services like face detection and natural language processing...Smart edge computing(SEC)is a novel paradigm for computing that could transfer cloud-based applications to the edge network,supporting computation-intensive services like face detection and natural language processing.A core feature of mobile edge computing,SEC improves user experience and device performance by offloading local activities to edge processors.In this framework,blockchain technology is utilized to ensure secure and trustworthy communication between edge devices and servers,protecting against potential security threats.Additionally,Deep Learning algorithms are employed to analyze resource availability and optimize computation offloading decisions dynamically.IoT applications that require significant resources can benefit from SEC,which has better coverage.Although access is constantly changing and network devices have heterogeneous resources,it is not easy to create consistent,dependable,and instantaneous communication between edge devices and their processors,specifically in 5G Heterogeneous Network(HN)situations.Thus,an Intelligent Management of Resources for Smart Edge Computing(IMRSEC)framework,which combines blockchain,edge computing,and Artificial Intelligence(AI)into 5G HNs,has been proposed in this paper.As a result,a unique dual schedule deep reinforcement learning(DS-DRL)technique has been developed,consisting of a rapid schedule learning process and a slow schedule learning process.The primary objective is to minimize overall unloading latency and system resource usage by optimizing computation offloading,resource allocation,and application caching.Simulation results demonstrate that the DS-DRL approach reduces task execution time by 32%,validating the method’s effectiveness within the IMRSEC framework.展开更多
The Fifth Generation of Mobile Communications for Railways(5G-R)brings significant opportunities for the rail industry.However,alongside the potential and benefits of the railway 5G network are complex security challe...The Fifth Generation of Mobile Communications for Railways(5G-R)brings significant opportunities for the rail industry.However,alongside the potential and benefits of the railway 5G network are complex security challenges.Ensuring the security and reliability of railway 5G networks is therefore essential.This paper presents a detailed examination of security assessment techniques for railway 5G networks,focusing on addressing the unique security challenges in this field.In this paper,various security requirements in railway 5G networks are analyzed,and specific processes and methods for conducting comprehensive security risk assessments are presented.This study provides a framework for securing railway 5G network development and ensuring its long-term sustainability.展开更多
The adoption of 5G for Railways(5G-R)is expanding,particularly in high-speed trains,due to the benefits offered by 5G technology.High-speed trains must provide seamless connectivity and Quality of Service(QoS)to ensur...The adoption of 5G for Railways(5G-R)is expanding,particularly in high-speed trains,due to the benefits offered by 5G technology.High-speed trains must provide seamless connectivity and Quality of Service(QoS)to ensure passengers have a satisfactory experience throughout their journey.Installing base stations along urban environments can improve coverage but can dramatically reduce the experience of users due to interference.In particular,when a user with a mobile phone is a passenger in a high speed train traversing between urban centres,the coverage and the 5G resources in general need to be adequate not to diminish her experience of the service.The utilization of macro,pico,and femto cells may optimize the utilization of 5G resources.In this paper,a Genetic Algorithm(GA)-based approach to address the challenges of 5G network planning for 5G-R services is presented.The network is divided into three cell types,macro,pico,and femto cells—and the optimization process is designed to achieve a balance between key objectives:providing comprehensive coverage,minimizing interference,and maximizing energy efficiency.The study focuses on environments with high user density,such as high-speed trains,where reliable and high-quality connectivity is critical.Through simulations,the effectiveness of the GA-driven framework in optimizing coverage and performance in such scenarios is demonstrated.The algorithm is compared with the Particle Swarm Optimisation(PSO)and the Simulated Annealing(SA)methods and interesting insights emerged.The GA offers a strong balance between coverage and efficiency,achieving significantly higher coverage than PSO while maintaining competitive energy efficiency and interference levels.Its steady fitness improvement and adaptability make it well-suited for scenarios where wide coverage is a priority alongside acceptable performance trade-offs.展开更多
With the rapid advancement of mobile communication networks,key technologies such as Multi-access Edge Computing(MEC)and Network Function Virtualization(NFV)have enhanced the quality of service for 5G users but have a...With the rapid advancement of mobile communication networks,key technologies such as Multi-access Edge Computing(MEC)and Network Function Virtualization(NFV)have enhanced the quality of service for 5G users but have also significantly increased the complexity of network threats.Traditional static defense mechanisms are inadequate for addressing the dynamic and heterogeneous nature of modern attack vectors.To overcome these challenges,this paper presents a novel algorithmic framework,SD-5G,designed for high-precision intrusion detection in 5G environments.SD-5G adopts a three-stage architecture comprising traffic feature extraction,elastic representation,and adaptive classification.Specifically,an enhanced Concrete Autoencoder(CAE)is employed to reconstruct and compress high-dimensional network traffic features,producing compact and expressive representations suitable for large-scale 5G deployments.To further improve accuracy in ambiguous traffic classification,a Residual Convolutional Long Short-Term Memory model with an attention mechanism(ResCLA)is introduced,enabling multi-level modeling of spatial–temporal dependencies and effective detection of subtle anomalies.Extensive experiments on benchmark datasets—including 5G-NIDD,CIC-IDS2017,ToN-IoT,and BoT-IoT—demonstrate that SD-5G consistently achieves F1 scores exceeding 99.19%across diverse network environments,indicating strong generalization and real-time deployment capabilities.Overall,SD-5G achieves a balance between detection accuracy and deployment efficiency,offering a scalable,flexible,and effective solution for intrusion detection in 5G and next-generation networks.展开更多
This study proposes an efficient traffic classification model to address the growing threat of distributed denial-of-service(DDoS)attacks in 5th generation technology standard(5G)slicing networks.The proposed method u...This study proposes an efficient traffic classification model to address the growing threat of distributed denial-of-service(DDoS)attacks in 5th generation technology standard(5G)slicing networks.The proposed method utilizes an ensemble of encoder components from multiple autoencoders to compress and extract latent representations from high-dimensional traffic data.These representations are then used as input for a support vector machine(SVM)-based metadata classifier,enabling precise detection of attack traffic.This architecture is designed to achieve both high detection accuracy and training efficiency,while adapting flexibly to the diverse service requirements and complexity of 5G network slicing.The model was evaluated using the DDoS Datasets 2022,collected in a simulated 5G slicing environment.Experiments were conducted under both class-balanced and class-imbalanced conditions.In the balanced setting,the model achieved an accuracy of 89.33%,an F1-score of 88.23%,and an Area Under the Curve(AUC)of 89.45%.In the imbalanced setting(attack:normal 7:3),the model maintained strong robustness,=achieving a recall of 100%and an F1-score of 90.91%,demonstrating its effectiveness in diverse real-world scenarios.Compared to existing AI-based detection methods,the proposed model showed higher precision,better handling of class imbalance,and strong generalization performance.Moreover,its modular structure is well-suited for deployment in containerized network function(NF)environments,making it a practical solution for real-world 5G infrastructure.These results highlight the potential of the proposed approach to enhance both the security and operational resilience of 5G slicing networks.展开更多
5G technology has endowed mobile communication terminals with features such as ultrawideband access,low latency,and high reliability transmission,which can complete the network access and interconnection of a large nu...5G technology has endowed mobile communication terminals with features such as ultrawideband access,low latency,and high reliability transmission,which can complete the network access and interconnection of a large number of devices,thus realizing richer application scenarios and constructing 5G-enabled vehicular networks.However,due to the vulnerability of wireless communication,vehicle privacy and communication security have become the key problems to be solved in vehicular networks.Moreover,the large-scale communication in the vehicular networks also makes the higher communication efficiency an inevitable requirement.In order to achieve efficient and secure communication while protecting vehicle privacy,this paper proposes a lightweight key agreement and key update scheme for 5G vehicular networks based on blockchain.Firstly,the key agreement is accomplished using certificateless public key cryptography,and based on the aggregate signature and the cooperation between the vehicle and the trusted authority,an efficient key updating method is proposed,which reduces the overhead and protects the privacy of the vehicle while ensuring the communication security.Secondly,by introducing blockchain and using smart contracts to load the vehicle public key table for key management,this meets the requirements of vehicle traceability and can dynamically track and revoke misbehaving vehicles.Finally,the formal security proof under the eck security model and the informal security analysis is conducted,it turns out that our scheme is more secure than other authentication schemes in the vehicular networks.Performance analysis shows that our scheme has lower overhead than existing schemes in terms of communication and computation.展开更多
In the traditional manufacturing industry system,the ceramic industry occupies animportant position due to its unique technological characteristics.As the core equipment for theproduction of artistic and daily-use cer...In the traditional manufacturing industry system,the ceramic industry occupies animportant position due to its unique technological characteristics.As the core equipment for theproduction of artistic and daily-use ceramics,the intermittent kiln has become an indispensable keylink in the industry by virtue of its advantage of flexibly adapting to the production of multiplevarieties in small batches.However,the current operation mode of ceramic intermittent kilns facessevere challenges:although instrument control has been initially achieved,the dependence on on-site manual operation and supervision,combined with the characteristics of small-scale andworkshop-style production,has led to widespread blind spots in supervision and numerous safetyrisks.Existing technologies mainly focus on the improvement of the kiln structure and theoptimization of local control,which is difficult to meet the complex requirements of collaborativemanagement and control of multiple kilns.The centralized ceramic kiln management and controldevice proposed in this paper deeply integrates Internet of Things technology and constructs anintelligent management system covering the entire ceramic production area.By collecting andtransmitting the operation data of the kiln in real time,this device not only enables all-weatherprecise monitoring of the state of the intermittent kiln,but also has the functions of intelligentaccident warning and remote control,providing a new technical path and practical model for theintelligent and safe development of the ceramic industry.展开更多
Low-carbon smart parks achieve selfbalanced carbon emission and absorption through the cooperative scheduling of direct current(DC)-based distributed photovoltaic,energy storage units,and loads.Direct current power li...Low-carbon smart parks achieve selfbalanced carbon emission and absorption through the cooperative scheduling of direct current(DC)-based distributed photovoltaic,energy storage units,and loads.Direct current power line communication(DC-PLC)enables real-time data transmission on DC power lines.With traffic adaptation,DC-PLC can be integrated with other complementary media such as 5G to reduce transmission delay and improve reliability.However,traffic adaptation for DC-PLC and 5G integration still faces the challenges such as coupling between traffic admission control and traffic partition,dimensionality curse,and the ignorance of extreme event occurrence.To address these challenges,we propose a deep reinforcement learning(DRL)-based delay sensitive and reliable traffic adaptation algorithm(DSRTA)to minimize the total queuing delay under the constraints of traffic admission control,queuing delay,and extreme events occurrence probability.DSRTA jointly optimizes traffic admission control and traffic partition,and enables learning-based intelligent traffic adaptation.The long-term constraints are incorporated into both state and bound of drift-pluspenalty to achieve delay awareness and enforce reliability guarantee.Simulation results show that DSRTA has lower queuing delay and more reliable quality of service(QoS)guarantee than other state-of-the-art algorithms.展开更多
为解决以LTE-5G(Long Term Evolution-5G)为代表的新一代移动通信技术存在的网络恶意节点检出效率较低、流量查证识别能力较弱等不足,提出了一种基于多维参数采样判决机制的LTE-5G网络恶意节点检出算法.首先,针对网络恶意节点行为所产...为解决以LTE-5G(Long Term Evolution-5G)为代表的新一代移动通信技术存在的网络恶意节点检出效率较低、流量查证识别能力较弱等不足,提出了一种基于多维参数采样判决机制的LTE-5G网络恶意节点检出算法.首先,针对网络恶意节点行为所产生的外溢流量特点,根据节点时域抽样特性及指纹特点,设计了多维参数采样判决机制,该机制通过采样鉴权序列定向匹配节点时域特征并筛除不符合特征的疑似恶意节点,从而达到较高的检测效果.其次,为进一步清除潜伏状态的恶意节点,结合微分机制,构建黑洞数据鉴权阈值,通过该阈值控制网络清洗流量,仅将高于该阈值的疑似节点予以清洗处理,从而降低误判情形,改善因节点离线而导致网络传输性能出现下降的现象.仿真实验表明,与当前常用的基于神经网络的加权投票鉴权机制的数据清洗方案和基于鲸鱼-狮子联合优化机制的数据清洗方案相比,本文算法具有更高的网络传输带宽和恶意节点检出率,以及更低的网络节点离线频次,在实践中具有较高的推广价值.展开更多
The fifth generation(5G) of mobile communications are facing big challenges, due to the proliferation of diversified terminals and unprecedented services such as internet of things(IoT), high-definition videos, virtua...The fifth generation(5G) of mobile communications are facing big challenges, due to the proliferation of diversified terminals and unprecedented services such as internet of things(IoT), high-definition videos, virtual/augmented reality(VR/AR). To accommodate massive connections and astonish mobile traffic, an efficient 5G transport network is required. Optical transport network has been demonstrated to play an important role for carrying 5G radio signals. This paper focuses on the future challenges, recent studies and potential solutions for the 5G flexible optical transport networks with the performances on large-capacity, low-latency and high-efficiency. In addition, we discuss the technology development trends of the 5G transport networks in terms of the optical device, optical transport system, optical switching, and optical networking. Finally, we conclude the paper with the improvement of network intelligence enabled by these technologies to deterministic content delivery over 5G optical transport networks.展开更多
The traffic explosion and the rising of diverse requirements lead to many challenges for traditional mobile network architecture on flexibility, scalability, and deployability. To meet new requirements in the 5 G era,...The traffic explosion and the rising of diverse requirements lead to many challenges for traditional mobile network architecture on flexibility, scalability, and deployability. To meet new requirements in the 5 G era, service based architecture is introduced into mobile networks. The monolithic network elements(e.g., MME, PGW, etc.) are split into smaller network functions to provide customized services. However, the management and deployment of network functions in service based 5 G core network are still big challenges. In this paper, we propose a novel management architecture for 5 G service based core network based on NFV and SDN. Combined with SDN, NFV and edge computing, the proposed framework can provide distributed and on-demand deployment of network functions, service guaranteed network slicing, flexible orchestration of network functions and optimal workload allocation. Simulations are conducted to show that the proposed framework and algorithm are effective in terms of reducing network operating cost.展开更多
In this work,a frame work for time-varying channel modeling and simulation is proposed by using neural network(NN)to overcome the shortcomings in geometry based stochastic model(GBSM)and simulation approach.Two NN mod...In this work,a frame work for time-varying channel modeling and simulation is proposed by using neural network(NN)to overcome the shortcomings in geometry based stochastic model(GBSM)and simulation approach.Two NN models are developed for modeling of path loss together with shadow fading(SF)and joint small scale channel parameters.The NN models can predict path loss plus SF and small scale channel parameters accurately compared with measurement at 26 GHz performed in an outdoor microcell.The time-varying path loss and small scale channel parameters generated by the NN models are proposed to replace the empirical path loss and channel parameter random numbers in GBSM-based framework to playback the measured channel and match with its environment.Moreover,the sparse feature of clusters,delay and angular spread,channel capacity are investigated by a virtual array measurement at 28 GHz in a large waiting hall.展开更多
Recent developments in the aerospace industry have led to a dramatic reduction in the manufacturing and launch costs of low Earth orbit satellites.The new trend enables the paradigm shift of satelliteterrestrial integ...Recent developments in the aerospace industry have led to a dramatic reduction in the manufacturing and launch costs of low Earth orbit satellites.The new trend enables the paradigm shift of satelliteterrestrial integrated networks with global coverage.In particular,the integration of 5G communication systems and satellites has the potential to restructure nextgeneration mobile networks.By leveraging the network function virtualization and network slicing,the satellite 5G core networks will facilitate the coordination and management of network functions in satellite-terrestrial integrated networks.We are the first to deploy a 5G core network on a real-world satellite to investigate its feasibility.We conducted experiments to validate the satellite 5G core network functions.The validated procedures include registration and session setup procedures.The results show that the satellite 5G core network can function normally and generate correct signaling.展开更多
基金supported by the national key research and development program of China under Grant 2020YFB1807901the National Science Foundation Project in China under grant 61931005.
文摘As investigated by 3GPP,support of UPF(user plane function)onboard satellite can reduce the latency of communications via satellite,and then it becomes a key enhancement in 5G network integrating with satellite communication.However,current 5G system cannot support UPF onboard LEO(low earth orbit)satellites,as it would face challenges like UPF mobility handling,synchronization between mobile network and satellite network,and condition of activating local data switching.To solve such challenges,this paper proposes a solution to support UPF onboard LEO satellite,which consists of enhanced network architecture,I-UPF(intermediate UPF)based local data switching scheme and communication latency based data path selection.We subsequently develop analytic models for performance evaluation and conduct simulations using the constellation configuration of iridium II.The simulation results show that the data switching via I-UPF onboard LEO satellite can reduce E2E(end to end)packet delivery latency and E2E packet loss ratio significantly compared with that of routing the data back to 5GC on the ground.The proposed scheme yet has increased signaling cost for handling UPF mobility.els,compared with existing similar companding algorithms.
基金funding from King Saud University through Researchers Supporting Project number(RSP2024R387),King Saud University,Riyadh,Saudi Arabia.
文摘The emergence of next generation networks(NextG),including 5G and beyond,is reshaping the technological landscape of cellular and mobile networks.These networks are sufficiently scaled to interconnect billions of users and devices.Researchers in academia and industry are focusing on technological advancements to achieve highspeed transmission,cell planning,and latency reduction to facilitate emerging applications such as virtual reality,the metaverse,smart cities,smart health,and autonomous vehicles.NextG continuously improves its network functionality to support these applications.Multiple input multiple output(MIMO)technology offers spectral efficiency,dependability,and overall performance in conjunctionwithNextG.This article proposes a secure channel estimation technique in MIMO topology using a norm-estimation model to provide comprehensive insights into protecting NextG network components against adversarial attacks.The technique aims to create long-lasting and secure NextG networks using this extended approach.The viability of MIMO applications and modern AI-driven methodologies to combat cybersecurity threats are explored in this research.Moreover,the proposed model demonstrates high performance in terms of reliability and accuracy,with a 20%reduction in the MalOut-RealOut-Diff metric compared to existing state-of-the-art techniques.
文摘The explosive growth of data traffic and heterogeneous service requirements of 5G networks—covering Enhanced Mobile Broadband(eMBB),Ultra-Reliable Low Latency Communication(URLLC),and Massive Machine Type Communication(mMTC)—present tremendous challenges to conventional methods of bandwidth allocation.A new deep reinforcement learning-based(DRL-based)bandwidth allocation system for real-time,dynamic management of 5G radio access networks is proposed in this paper.Unlike rule-based and static strategies,the proposed system dynamically updates itself according to shifting network conditions such as traffic load and channel conditions to maximize the achievable throughput,fairness,and compliance with QoS requirements.By using extensive simulations mimicking real-world 5G scenarios,the proposed DRL model outperforms current baselines like Long Short-Term Memory(LSTM),linear regression,round-robin,and greedy algorithms.It attains 90%–95%of the maximum theoretical achievable throughput and nearly twice the conventional equal allocation.It is also shown to react well under delay and reliability constraints,outperforming round-robin(hindered by excessive delay and packet loss)and proving to be more efficient than greedy approaches.In conclusion,the efficiency of DRL in optimizing the allocation of bandwidth is highlighted,and its potential to realize self-optimizing,Artificial Intelligence-assisted(AI-assisted)resource management in 5G as well as upcoming 6G networks is revealed.
文摘Smart edge computing(SEC)is a novel paradigm for computing that could transfer cloud-based applications to the edge network,supporting computation-intensive services like face detection and natural language processing.A core feature of mobile edge computing,SEC improves user experience and device performance by offloading local activities to edge processors.In this framework,blockchain technology is utilized to ensure secure and trustworthy communication between edge devices and servers,protecting against potential security threats.Additionally,Deep Learning algorithms are employed to analyze resource availability and optimize computation offloading decisions dynamically.IoT applications that require significant resources can benefit from SEC,which has better coverage.Although access is constantly changing and network devices have heterogeneous resources,it is not easy to create consistent,dependable,and instantaneous communication between edge devices and their processors,specifically in 5G Heterogeneous Network(HN)situations.Thus,an Intelligent Management of Resources for Smart Edge Computing(IMRSEC)framework,which combines blockchain,edge computing,and Artificial Intelligence(AI)into 5G HNs,has been proposed in this paper.As a result,a unique dual schedule deep reinforcement learning(DS-DRL)technique has been developed,consisting of a rapid schedule learning process and a slow schedule learning process.The primary objective is to minimize overall unloading latency and system resource usage by optimizing computation offloading,resource allocation,and application caching.Simulation results demonstrate that the DS-DRL approach reduces task execution time by 32%,validating the method’s effectiveness within the IMRSEC framework.
基金supported in part by the Fundamental Research Funds for the Central Universities under Grant No.2025JBXT010in part by NSFC under Grant No.62171021,in part by the Project of China State Railway Group under Grant No.N2024B004in part by ZTE IndustryUniversityInstitute Cooperation Funds under Grant No.l23L00010.
文摘The Fifth Generation of Mobile Communications for Railways(5G-R)brings significant opportunities for the rail industry.However,alongside the potential and benefits of the railway 5G network are complex security challenges.Ensuring the security and reliability of railway 5G networks is therefore essential.This paper presents a detailed examination of security assessment techniques for railway 5G networks,focusing on addressing the unique security challenges in this field.In this paper,various security requirements in railway 5G networks are analyzed,and specific processes and methods for conducting comprehensive security risk assessments are presented.This study provides a framework for securing railway 5G network development and ensuring its long-term sustainability.
文摘The adoption of 5G for Railways(5G-R)is expanding,particularly in high-speed trains,due to the benefits offered by 5G technology.High-speed trains must provide seamless connectivity and Quality of Service(QoS)to ensure passengers have a satisfactory experience throughout their journey.Installing base stations along urban environments can improve coverage but can dramatically reduce the experience of users due to interference.In particular,when a user with a mobile phone is a passenger in a high speed train traversing between urban centres,the coverage and the 5G resources in general need to be adequate not to diminish her experience of the service.The utilization of macro,pico,and femto cells may optimize the utilization of 5G resources.In this paper,a Genetic Algorithm(GA)-based approach to address the challenges of 5G network planning for 5G-R services is presented.The network is divided into three cell types,macro,pico,and femto cells—and the optimization process is designed to achieve a balance between key objectives:providing comprehensive coverage,minimizing interference,and maximizing energy efficiency.The study focuses on environments with high user density,such as high-speed trains,where reliable and high-quality connectivity is critical.Through simulations,the effectiveness of the GA-driven framework in optimizing coverage and performance in such scenarios is demonstrated.The algorithm is compared with the Particle Swarm Optimisation(PSO)and the Simulated Annealing(SA)methods and interesting insights emerged.The GA offers a strong balance between coverage and efficiency,achieving significantly higher coverage than PSO while maintaining competitive energy efficiency and interference levels.Its steady fitness improvement and adaptability make it well-suited for scenarios where wide coverage is a priority alongside acceptable performance trade-offs.
文摘With the rapid advancement of mobile communication networks,key technologies such as Multi-access Edge Computing(MEC)and Network Function Virtualization(NFV)have enhanced the quality of service for 5G users but have also significantly increased the complexity of network threats.Traditional static defense mechanisms are inadequate for addressing the dynamic and heterogeneous nature of modern attack vectors.To overcome these challenges,this paper presents a novel algorithmic framework,SD-5G,designed for high-precision intrusion detection in 5G environments.SD-5G adopts a three-stage architecture comprising traffic feature extraction,elastic representation,and adaptive classification.Specifically,an enhanced Concrete Autoencoder(CAE)is employed to reconstruct and compress high-dimensional network traffic features,producing compact and expressive representations suitable for large-scale 5G deployments.To further improve accuracy in ambiguous traffic classification,a Residual Convolutional Long Short-Term Memory model with an attention mechanism(ResCLA)is introduced,enabling multi-level modeling of spatial–temporal dependencies and effective detection of subtle anomalies.Extensive experiments on benchmark datasets—including 5G-NIDD,CIC-IDS2017,ToN-IoT,and BoT-IoT—demonstrate that SD-5G consistently achieves F1 scores exceeding 99.19%across diverse network environments,indicating strong generalization and real-time deployment capabilities.Overall,SD-5G achieves a balance between detection accuracy and deployment efficiency,offering a scalable,flexible,and effective solution for intrusion detection in 5G and next-generation networks.
基金supported by an Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korean government(MSIT)(RS-2024-00438156,Development of Security Resilience Technology Based on Network Slicing Services in a 5G Specialized Network).
文摘This study proposes an efficient traffic classification model to address the growing threat of distributed denial-of-service(DDoS)attacks in 5th generation technology standard(5G)slicing networks.The proposed method utilizes an ensemble of encoder components from multiple autoencoders to compress and extract latent representations from high-dimensional traffic data.These representations are then used as input for a support vector machine(SVM)-based metadata classifier,enabling precise detection of attack traffic.This architecture is designed to achieve both high detection accuracy and training efficiency,while adapting flexibly to the diverse service requirements and complexity of 5G network slicing.The model was evaluated using the DDoS Datasets 2022,collected in a simulated 5G slicing environment.Experiments were conducted under both class-balanced and class-imbalanced conditions.In the balanced setting,the model achieved an accuracy of 89.33%,an F1-score of 88.23%,and an Area Under the Curve(AUC)of 89.45%.In the imbalanced setting(attack:normal 7:3),the model maintained strong robustness,=achieving a recall of 100%and an F1-score of 90.91%,demonstrating its effectiveness in diverse real-world scenarios.Compared to existing AI-based detection methods,the proposed model showed higher precision,better handling of class imbalance,and strong generalization performance.Moreover,its modular structure is well-suited for deployment in containerized network function(NF)environments,making it a practical solution for real-world 5G infrastructure.These results highlight the potential of the proposed approach to enhance both the security and operational resilience of 5G slicing networks.
基金supported in part by the National Natural Science Foundation of China under Grant 61941113,Grant 61971033,and Grant 61671057by the Henan Provincial Department of Science and Technology Project(No.212102210408)by the Henan Provincial Key Scientific Research Project(No.22A520041).
文摘5G technology has endowed mobile communication terminals with features such as ultrawideband access,low latency,and high reliability transmission,which can complete the network access and interconnection of a large number of devices,thus realizing richer application scenarios and constructing 5G-enabled vehicular networks.However,due to the vulnerability of wireless communication,vehicle privacy and communication security have become the key problems to be solved in vehicular networks.Moreover,the large-scale communication in the vehicular networks also makes the higher communication efficiency an inevitable requirement.In order to achieve efficient and secure communication while protecting vehicle privacy,this paper proposes a lightweight key agreement and key update scheme for 5G vehicular networks based on blockchain.Firstly,the key agreement is accomplished using certificateless public key cryptography,and based on the aggregate signature and the cooperation between the vehicle and the trusted authority,an efficient key updating method is proposed,which reduces the overhead and protects the privacy of the vehicle while ensuring the communication security.Secondly,by introducing blockchain and using smart contracts to load the vehicle public key table for key management,this meets the requirements of vehicle traceability and can dynamically track and revoke misbehaving vehicles.Finally,the formal security proof under the eck security model and the informal security analysis is conducted,it turns out that our scheme is more secure than other authentication schemes in the vehicular networks.Performance analysis shows that our scheme has lower overhead than existing schemes in terms of communication and computation.
基金supported by Jiangxi 03 Special and 5G Project(20232ABC03A33)Ganpo Talent Support Program(20232BCJ23106)。
文摘In the traditional manufacturing industry system,the ceramic industry occupies animportant position due to its unique technological characteristics.As the core equipment for theproduction of artistic and daily-use ceramics,the intermittent kiln has become an indispensable keylink in the industry by virtue of its advantage of flexibly adapting to the production of multiplevarieties in small batches.However,the current operation mode of ceramic intermittent kilns facessevere challenges:although instrument control has been initially achieved,the dependence on on-site manual operation and supervision,combined with the characteristics of small-scale andworkshop-style production,has led to widespread blind spots in supervision and numerous safetyrisks.Existing technologies mainly focus on the improvement of the kiln structure and theoptimization of local control,which is difficult to meet the complex requirements of collaborativemanagement and control of multiple kilns.The centralized ceramic kiln management and controldevice proposed in this paper deeply integrates Internet of Things technology and constructs anintelligent management system covering the entire ceramic production area.By collecting andtransmitting the operation data of the kiln in real time,this device not only enables all-weatherprecise monitoring of the state of the intermittent kiln,but also has the functions of intelligentaccident warning and remote control,providing a new technical path and practical model for theintelligent and safe development of the ceramic industry.
基金supported by the Science and Technology Project of State Grid Corporation of China under grant 52094021N010(5400-202199534A-0-5-ZN)。
文摘Low-carbon smart parks achieve selfbalanced carbon emission and absorption through the cooperative scheduling of direct current(DC)-based distributed photovoltaic,energy storage units,and loads.Direct current power line communication(DC-PLC)enables real-time data transmission on DC power lines.With traffic adaptation,DC-PLC can be integrated with other complementary media such as 5G to reduce transmission delay and improve reliability.However,traffic adaptation for DC-PLC and 5G integration still faces the challenges such as coupling between traffic admission control and traffic partition,dimensionality curse,and the ignorance of extreme event occurrence.To address these challenges,we propose a deep reinforcement learning(DRL)-based delay sensitive and reliable traffic adaptation algorithm(DSRTA)to minimize the total queuing delay under the constraints of traffic admission control,queuing delay,and extreme events occurrence probability.DSRTA jointly optimizes traffic admission control and traffic partition,and enables learning-based intelligent traffic adaptation.The long-term constraints are incorporated into both state and bound of drift-pluspenalty to achieve delay awareness and enforce reliability guarantee.Simulation results show that DSRTA has lower queuing delay and more reliable quality of service(QoS)guarantee than other state-of-the-art algorithms.
文摘为解决以LTE-5G(Long Term Evolution-5G)为代表的新一代移动通信技术存在的网络恶意节点检出效率较低、流量查证识别能力较弱等不足,提出了一种基于多维参数采样判决机制的LTE-5G网络恶意节点检出算法.首先,针对网络恶意节点行为所产生的外溢流量特点,根据节点时域抽样特性及指纹特点,设计了多维参数采样判决机制,该机制通过采样鉴权序列定向匹配节点时域特征并筛除不符合特征的疑似恶意节点,从而达到较高的检测效果.其次,为进一步清除潜伏状态的恶意节点,结合微分机制,构建黑洞数据鉴权阈值,通过该阈值控制网络清洗流量,仅将高于该阈值的疑似节点予以清洗处理,从而降低误判情形,改善因节点离线而导致网络传输性能出现下降的现象.仿真实验表明,与当前常用的基于神经网络的加权投票鉴权机制的数据清洗方案和基于鲸鱼-狮子联合优化机制的数据清洗方案相比,本文算法具有更高的网络传输带宽和恶意节点检出率,以及更低的网络节点离线频次,在实践中具有较高的推广价值.
基金supported by the National Nature Science Foundation of China Projects(No.61871051,61771073)the Nature Science Foundation of Beijing project(No.4192039)
文摘The fifth generation(5G) of mobile communications are facing big challenges, due to the proliferation of diversified terminals and unprecedented services such as internet of things(IoT), high-definition videos, virtual/augmented reality(VR/AR). To accommodate massive connections and astonish mobile traffic, an efficient 5G transport network is required. Optical transport network has been demonstrated to play an important role for carrying 5G radio signals. This paper focuses on the future challenges, recent studies and potential solutions for the 5G flexible optical transport networks with the performances on large-capacity, low-latency and high-efficiency. In addition, we discuss the technology development trends of the 5G transport networks in terms of the optical device, optical transport system, optical switching, and optical networking. Finally, we conclude the paper with the improvement of network intelligence enabled by these technologies to deterministic content delivery over 5G optical transport networks.
基金supported by China Ministry of Education-CMCC Research Fund Project No.MCM20160104National Science and Technology Major Project No.No.2018ZX03001016+1 种基金Beijing Municipal Science and technology Commission Research Fund Project No.Z171100005217001Fundamental Research Funds for Central Universities NO.2018RC06
文摘The traffic explosion and the rising of diverse requirements lead to many challenges for traditional mobile network architecture on flexibility, scalability, and deployability. To meet new requirements in the 5 G era, service based architecture is introduced into mobile networks. The monolithic network elements(e.g., MME, PGW, etc.) are split into smaller network functions to provide customized services. However, the management and deployment of network functions in service based 5 G core network are still big challenges. In this paper, we propose a novel management architecture for 5 G service based core network based on NFV and SDN. Combined with SDN, NFV and edge computing, the proposed framework can provide distributed and on-demand deployment of network functions, service guaranteed network slicing, flexible orchestration of network functions and optimal workload allocation. Simulations are conducted to show that the proposed framework and algorithm are effective in terms of reducing network operating cost.
基金supported by the National Nature Science Foundation of China(NSFC)under grant No.61771194supported by Key Program of Beijing Municipal Natural Science Foundation with No.17L20052
文摘In this work,a frame work for time-varying channel modeling and simulation is proposed by using neural network(NN)to overcome the shortcomings in geometry based stochastic model(GBSM)and simulation approach.Two NN models are developed for modeling of path loss together with shadow fading(SF)and joint small scale channel parameters.The NN models can predict path loss plus SF and small scale channel parameters accurately compared with measurement at 26 GHz performed in an outdoor microcell.The time-varying path loss and small scale channel parameters generated by the NN models are proposed to replace the empirical path loss and channel parameter random numbers in GBSM-based framework to playback the measured channel and match with its environment.Moreover,the sparse feature of clusters,delay and angular spread,channel capacity are investigated by a virtual array measurement at 28 GHz in a large waiting hall.
基金supported by the National Key R&D Program of China(2020YFB1805500)National Natural Science Foundation of China(61922017,62032003 and 61921003)。
文摘Recent developments in the aerospace industry have led to a dramatic reduction in the manufacturing and launch costs of low Earth orbit satellites.The new trend enables the paradigm shift of satelliteterrestrial integrated networks with global coverage.In particular,the integration of 5G communication systems and satellites has the potential to restructure nextgeneration mobile networks.By leveraging the network function virtualization and network slicing,the satellite 5G core networks will facilitate the coordination and management of network functions in satellite-terrestrial integrated networks.We are the first to deploy a 5G core network on a real-world satellite to investigate its feasibility.We conducted experiments to validate the satellite 5G core network functions.The validated procedures include registration and session setup procedures.The results show that the satellite 5G core network can function normally and generate correct signaling.
