为应对开放型无线接入网(Open Radio Access Network,O-RAN)中的数据传输成本过高及网络兼容不足等问题,研究了面向O-RAN的多级边缘服务资源分配与部署联合优化问题。首先,利用四层融合模型将多目标联合优化问题转化为异构边缘服务器数...为应对开放型无线接入网(Open Radio Access Network,O-RAN)中的数据传输成本过高及网络兼容不足等问题,研究了面向O-RAN的多级边缘服务资源分配与部署联合优化问题。首先,利用四层融合模型将多目标联合优化问题转化为异构边缘服务器数量选择及位置确定问题,并提出了一种负载约束和迭代优化的异构边缘服务器资源分配算法,解决了O-RAN网络中的异构资源分配与数据传输问题。然后,提出了一种能效驱动的异构节点部署优化算法,解决了多级异构资源最佳部署位置问题。最后,利用上海电信基站的真实数据集,验证了所提资源优化与部署算法的有效性,实验结果表明,所提算法较其它算法在部署成本上至少降低了22.5%,能效比值上至少提高了25.96%。展开更多
The open nature and heterogeneous architecture of Open Radio Access Network(Open RAN)undermine the consistency of security policies and broaden the attack surface,thereby increasing the risk of security vulnerabilitie...The open nature and heterogeneous architecture of Open Radio Access Network(Open RAN)undermine the consistency of security policies and broaden the attack surface,thereby increasing the risk of security vulnerabilities.The dynamic nature of network performance and traffic patterns in Open RAN necessitates advanced detection models that can overcome the constraints of traditional techniques and adapt to evolving behaviors.This study presents a methodology for effectively detecting malicious traffic in Open RAN by utilizing an Artificial-Intelligence/MachineLearning(AI/ML)Framework.A hybrid Transformer–Convolutional-Neural-Network(Transformer-CNN)ensemble model is employed for anomaly detection.The proposed model generates final predictions through a soft-voting technique based on the predictive outputs of the two models with distinct features.This approach improves accuracy by up to 1.06%and F1 score by 1.48%compared with a hard-voting technique to determine the final prediction.Furthermore,the proposed model achieves an average accuracy of approximately 98.3%depending on the time step,exhibiting a 1.43%increase in accuracy over single-model approaches.Unlike single-model approaches,which are prone to overfitting,the ensemble model resolves the overfitting problem by reducing the deviation in validation loss.展开更多
The emergence of various technologies such as terahertz communications,Reconfigurable Intelligent Surfaces(RIS),and AI-powered communication services will burden network operators with rising infrastructure costs.Rece...The emergence of various technologies such as terahertz communications,Reconfigurable Intelligent Surfaces(RIS),and AI-powered communication services will burden network operators with rising infrastructure costs.Recently,the Open Radio Access Network(O-RAN)has been introduced as a solution for growing financial and operational burdens in Beyond 5G(B5G)and 6G networks.O-RAN promotes openness and intelligence to overcome the limitations of traditional RANs.By disaggregating conventional Base Band Units(BBUs)into O-RAN Distributed Units(O-DU)and O-RAN Centralized Units(O-CU),O-RAN offers greater flexibility for upgrades and network automation.However,this openness introduces new security challenges compared to traditional RANs.Many existing studies overlook these security requirements of the O-RAN networks.To gain deeper insights into the O-RAN system and security,this paper first provides an overview of the general O-RAN architecture and its diverse use cases relevant to B5G and 6G applications.We then delve into specifications of O-RAN security threats and requirements,aiming to mitigate security vulnerabilities effectively.By providing a comprehensive understanding of O-RAN architecture,use cases,and security considerations,thisworkserves as a valuable resource for future research in O-RAN and its security.展开更多
文摘为应对开放型无线接入网(Open Radio Access Network,O-RAN)中的数据传输成本过高及网络兼容不足等问题,研究了面向O-RAN的多级边缘服务资源分配与部署联合优化问题。首先,利用四层融合模型将多目标联合优化问题转化为异构边缘服务器数量选择及位置确定问题,并提出了一种负载约束和迭代优化的异构边缘服务器资源分配算法,解决了O-RAN网络中的异构资源分配与数据传输问题。然后,提出了一种能效驱动的异构节点部署优化算法,解决了多级异构资源最佳部署位置问题。最后,利用上海电信基站的真实数据集,验证了所提资源优化与部署算法的有效性,实验结果表明,所提算法较其它算法在部署成本上至少降低了22.5%,能效比值上至少提高了25.96%。
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(RS-2024-00396797,Development of core technology for intelligent O-RAN security platform)。
文摘The open nature and heterogeneous architecture of Open Radio Access Network(Open RAN)undermine the consistency of security policies and broaden the attack surface,thereby increasing the risk of security vulnerabilities.The dynamic nature of network performance and traffic patterns in Open RAN necessitates advanced detection models that can overcome the constraints of traditional techniques and adapt to evolving behaviors.This study presents a methodology for effectively detecting malicious traffic in Open RAN by utilizing an Artificial-Intelligence/MachineLearning(AI/ML)Framework.A hybrid Transformer–Convolutional-Neural-Network(Transformer-CNN)ensemble model is employed for anomaly detection.The proposed model generates final predictions through a soft-voting technique based on the predictive outputs of the two models with distinct features.This approach improves accuracy by up to 1.06%and F1 score by 1.48%compared with a hard-voting technique to determine the final prediction.Furthermore,the proposed model achieves an average accuracy of approximately 98.3%depending on the time step,exhibiting a 1.43%increase in accuracy over single-model approaches.Unlike single-model approaches,which are prone to overfitting,the ensemble model resolves the overfitting problem by reducing the deviation in validation loss.
基金supported by the Research Program funded by the SeoulTech(Seoul National University of Science and Technology).
文摘The emergence of various technologies such as terahertz communications,Reconfigurable Intelligent Surfaces(RIS),and AI-powered communication services will burden network operators with rising infrastructure costs.Recently,the Open Radio Access Network(O-RAN)has been introduced as a solution for growing financial and operational burdens in Beyond 5G(B5G)and 6G networks.O-RAN promotes openness and intelligence to overcome the limitations of traditional RANs.By disaggregating conventional Base Band Units(BBUs)into O-RAN Distributed Units(O-DU)and O-RAN Centralized Units(O-CU),O-RAN offers greater flexibility for upgrades and network automation.However,this openness introduces new security challenges compared to traditional RANs.Many existing studies overlook these security requirements of the O-RAN networks.To gain deeper insights into the O-RAN system and security,this paper first provides an overview of the general O-RAN architecture and its diverse use cases relevant to B5G and 6G applications.We then delve into specifications of O-RAN security threats and requirements,aiming to mitigate security vulnerabilities effectively.By providing a comprehensive understanding of O-RAN architecture,use cases,and security considerations,thisworkserves as a valuable resource for future research in O-RAN and its security.
