Abdelrahim Ahmad, Peizheng Li, Robert Piechocki, Rui Inacio
The radio access network (RAN) is a critical component of modern telecom infrastructure, currently undergoing significant transformation towards disaggregated and open architectures. These advancements are pivotal for integrating intelligent, data-driven applications aimed at enhancing network reliability and operational autonomy through the introduction of cognition capabilities, exemplified by the set of enhancements proposed by the emerging Open radio access network (O-RAN) standards. Despite its potential, the nascent nature of O-RAN technology presents challenges, primarily due to the absence of mature operational standards. This complicates the management of data and applications, particularly in integrating with traditional network management and operational support systems. Divergent vendor-specific design approaches further hinder migration and limit solution reusability. Addressing the skills gap in telecom business-oriented engineering is crucial for the effective deployment of O-RAN and the development of robust data-driven applications. To address these challenges, Boldyn Networks, a global Neutral Host provider, has implemented a novel cloud-native data analytics platform. This platform underwent rigorous testing in real-world scenarios of using advanced artificial intelligence (AI) techniques, significantly improving operational efficiency, and enhancing customer experience. Implementation involved adopting development operations (DevOps) practices, leveraging data lakehouse architectures tailored for AI applications, and employing sophisticated data engineering strategies. The platform successfully addresses connectivity challenges inherent in offshore windfarm deployments using long short-term memory (LSTM) Models for anomaly detection of the connectivity, providing detailed insights into its specialized architecture developed for this purpose.
{"title":"Anomaly Detection in Offshore Open Radio Access Network Using Long Short-Term Memory Models on a Novel Artificial Intelligence-Driven Cloud-Native Data Platform","authors":"Abdelrahim Ahmad, Peizheng Li, Robert Piechocki, Rui Inacio","doi":"arxiv-2409.02849","DOIUrl":"https://doi.org/arxiv-2409.02849","url":null,"abstract":"The radio access network (RAN) is a critical component of modern telecom\u0000infrastructure, currently undergoing significant transformation towards\u0000disaggregated and open architectures. These advancements are pivotal for\u0000integrating intelligent, data-driven applications aimed at enhancing network\u0000reliability and operational autonomy through the introduction of cognition\u0000capabilities, exemplified by the set of enhancements proposed by the emerging\u0000Open radio access network (O-RAN) standards. Despite its potential, the nascent\u0000nature of O-RAN technology presents challenges, primarily due to the absence of\u0000mature operational standards. This complicates the management of data and\u0000applications, particularly in integrating with traditional network management\u0000and operational support systems. Divergent vendor-specific design approaches\u0000further hinder migration and limit solution reusability. Addressing the skills\u0000gap in telecom business-oriented engineering is crucial for the effective\u0000deployment of O-RAN and the development of robust data-driven applications. To\u0000address these challenges, Boldyn Networks, a global Neutral Host provider, has\u0000implemented a novel cloud-native data analytics platform. This platform\u0000underwent rigorous testing in real-world scenarios of using advanced artificial\u0000intelligence (AI) techniques, significantly improving operational efficiency,\u0000and enhancing customer experience. Implementation involved adopting development\u0000operations (DevOps) practices, leveraging data lakehouse architectures tailored\u0000for AI applications, and employing sophisticated data engineering strategies.\u0000The platform successfully addresses connectivity challenges inherent in\u0000offshore windfarm deployments using long short-term memory (LSTM) Models for\u0000anomaly detection of the connectivity, providing detailed insights into its\u0000specialized architecture developed for this purpose.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":"52 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Roshan Sedar, Charalampos Kalalas, Paolo Dini, Francisco Vazquez-Gallego, Jesus Alonso-Zarate, Luis Alonso
Vehicular mobility underscores the need for collaborative misbehavior detection at the vehicular edge. However, locally trained misbehavior detection models are susceptible to adversarial attacks that aim to deliberately influence learning outcomes. In this paper, we introduce a deep reinforcement learning-based approach that employs transfer learning for collaborative misbehavior detection among roadside units (RSUs). In the presence of label-flipping and policy induction attacks, we perform selective knowledge transfer from trustworthy source RSUs to foster relevant expertise in misbehavior detection and avoid negative knowledge sharing from adversary-influenced RSUs. The performance of our proposed scheme is demonstrated with evaluations over a diverse set of misbehavior detection scenarios using an open-source dataset. Experimental results show that our approach significantly reduces the training time at the target RSU and achieves superior detection performance compared to the baseline scheme with tabula rasa learning. Enhanced robustness and generalizability can also be attained, by effectively detecting previously unseen and partially observable misbehavior attacks.
车辆的流动性凸显了在车辆边缘进行协同不当行为检测的必要性。然而，本地训练的不当行为检测模型容易受到旨在故意影响学习结果的对抗性攻击。在本文中，我们介绍了一种基于深度强化学习的方法，该方法利用迁移学习在路边装置（RSU）之间进行协同不当行为检测。在存在标签翻转和策略诱导攻击的情况下，我们有选择地从值得信赖的源RSU处进行知识转移，以培养不当行为检测中的相关专业知识，并避免来自受逆向影响的RSU的负面知识共享。我们利用一个开源数据集，在一系列不同的不当行为检测场景中进行了评估，从而证明了我们提出的方案的性能。实验结果表明，我们的方法大大缩短了目标 RSU 的训练时间，与使用 tabula rasalearning 的基线方案相比，检测性能更优。通过有效检测以前未见和部分可观察到的不当行为攻击，我们还增强了鲁棒性和普适性。
{"title":"Knowledge Transfer for Collaborative Misbehavior Detection in Untrusted Vehicular Environments","authors":"Roshan Sedar, Charalampos Kalalas, Paolo Dini, Francisco Vazquez-Gallego, Jesus Alonso-Zarate, Luis Alonso","doi":"arxiv-2409.02844","DOIUrl":"https://doi.org/arxiv-2409.02844","url":null,"abstract":"Vehicular mobility underscores the need for collaborative misbehavior\u0000detection at the vehicular edge. However, locally trained misbehavior detection\u0000models are susceptible to adversarial attacks that aim to deliberately\u0000influence learning outcomes. In this paper, we introduce a deep reinforcement\u0000learning-based approach that employs transfer learning for collaborative\u0000misbehavior detection among roadside units (RSUs). In the presence of\u0000label-flipping and policy induction attacks, we perform selective knowledge\u0000transfer from trustworthy source RSUs to foster relevant expertise in\u0000misbehavior detection and avoid negative knowledge sharing from\u0000adversary-influenced RSUs. The performance of our proposed scheme is\u0000demonstrated with evaluations over a diverse set of misbehavior detection\u0000scenarios using an open-source dataset. Experimental results show that our\u0000approach significantly reduces the training time at the target RSU and achieves\u0000superior detection performance compared to the baseline scheme with tabula rasa\u0000learning. Enhanced robustness and generalizability can also be attained, by\u0000effectively detecting previously unseen and partially observable misbehavior\u0000attacks.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":"66 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Multiprotocol Label Switching (MPLS) is a high-performance telecommunications technology that directs data from one network node to another based on short path labels rather than long network addresses. Its efficiency and scalability have made it a popular choice for large-scale and enterprise networks. However, as MPLS networks grow and evolve, they encounter various security challenges. This paper explores the security implications associated with MPLS networks, including risks such as label spoofing, traffic interception, and denial of service attacks. Additionally, it evaluates advanced mitigation strategies to address these vulnerabilities, leveraging mathematical models and security protocols to enhance MPLS network resilience. By integrating theoretical analysis with practical solutions, this paper aims to provide a comprehensive understanding of MPLS security and propose effective methods for safeguarding network infrastructure.
{"title":"Security Implications and Mitigation Strategies in MPLS Networks","authors":"Ayush Thakur","doi":"arxiv-2409.03795","DOIUrl":"https://doi.org/arxiv-2409.03795","url":null,"abstract":"Multiprotocol Label Switching (MPLS) is a high-performance telecommunications\u0000technology that directs data from one network node to another based on short\u0000path labels rather than long network addresses. Its efficiency and scalability\u0000have made it a popular choice for large-scale and enterprise networks. However,\u0000as MPLS networks grow and evolve, they encounter various security challenges.\u0000This paper explores the security implications associated with MPLS networks,\u0000including risks such as label spoofing, traffic interception, and denial of\u0000service attacks. Additionally, it evaluates advanced mitigation strategies to\u0000address these vulnerabilities, leveraging mathematical models and security\u0000protocols to enhance MPLS network resilience. By integrating theoretical\u0000analysis with practical solutions, this paper aims to provide a comprehensive\u0000understanding of MPLS security and propose effective methods for safeguarding\u0000network infrastructure.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Danilo Fernandes, Douglas L. L. Moura, Gean Santos, Geymerson S. Ramos, Fabiane Queiroz, Andre L. L. Aquino
The rapid urbanization growth has underscored the need for innovative solutions to enhance transportation efficiency and safety. Intelligent Transportation Systems (ITS) have emerged as a promising solution in this context. However, analyzing and processing the massive and intricate data generated by ITS presents significant challenges for traditional data processing systems. This work proposes an Edge-based Data Lake Architecture to integrate and analyze the complex data from ITS efficiently. The architecture offers scalability, fault tolerance, and performance, improving decision-making and enhancing innovative services for a more intelligent transportation ecosystem. We demonstrate the effectiveness of the architecture through an analysis of three different use cases: (i) Vehicular Sensor Network, (ii) Mobile Network, and (iii) Driver Identification applications.
城市化的快速发展凸显了对创新解决方案的需求，以提高运输效率和安全性。在此背景下，智能交通系统（ITS）成为一种前景广阔的解决方案。然而，分析和处理智能交通系统产生的大量复杂数据对传统数据处理系统提出了巨大挑战。这项工作提出了一种基于边缘的数据湖架构，以有效整合和分析 ITS 的复杂数据。该架构提供了可扩展性、容错性和性能，可改善决策并增强创新服务，从而打造更加智能的交通生态系统。我们通过分析以下三种不同的使用案例来证明该架构的有效性：(i) 车辆传感器网络；(ii) 移动网络；(iii) 驾驶员识别应用。
{"title":"Towards Edge-Based Data Lake Architecture for Intelligent Transportation System","authors":"Danilo Fernandes, Douglas L. L. Moura, Gean Santos, Geymerson S. Ramos, Fabiane Queiroz, Andre L. L. Aquino","doi":"arxiv-2409.02808","DOIUrl":"https://doi.org/arxiv-2409.02808","url":null,"abstract":"The rapid urbanization growth has underscored the need for innovative\u0000solutions to enhance transportation efficiency and safety. Intelligent\u0000Transportation Systems (ITS) have emerged as a promising solution in this\u0000context. However, analyzing and processing the massive and intricate data\u0000generated by ITS presents significant challenges for traditional data\u0000processing systems. This work proposes an Edge-based Data Lake Architecture to\u0000integrate and analyze the complex data from ITS efficiently. The architecture\u0000offers scalability, fault tolerance, and performance, improving decision-making\u0000and enhancing innovative services for a more intelligent transportation\u0000ecosystem. We demonstrate the effectiveness of the architecture through an\u0000analysis of three different use cases: (i) Vehicular Sensor Network, (ii)\u0000Mobile Network, and (iii) Driver Identification applications.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jordi Pérez-Romero, Oriol Sallent, David Campoy, Antoni Gelonch, Xavier Gelabert, Bleron Klaiqi
Radio Access Network (RAN) disaggregation is emerging as a key trend in beyond 5G, as it offers new opportunities for more flexible deployments and intelligent network management. A relevant problem in disaggregated RAN is the functional split selection, which dynamically decides which baseband (BB) functions of a base station are kept close to the radio units and which ones are centralized. In this context, this paper firstly presents an architectural framework for supporting this concept relying on the O-RAN architecture. Then, the paper analyzes how the functional split can be optimized to adapt to the different load conditions while minimizing energy costs.
无线接入网（RAN）分解正在成为 5G 之后的一个重要趋势，因为它为更灵活的部署和智能网络管理提供了新的机遇。分解式 RAN 中的一个相关问题是功能拆分选择，即动态决定基站的哪些基带（BB）功能靠近无线电单元，哪些功能集中在一起。在这种情况下，本文首先提出了一个基于 O-RAN 架构的支持这一概念的架构框架。然后，本文分析了如何优化功能拆分，以适应不同的负载条件，同时最大限度地降低能源成本。
{"title":"Low Layer Functional Split Management in 5G and Beyond: Architecture and Self-adaptation","authors":"Jordi Pérez-Romero, Oriol Sallent, David Campoy, Antoni Gelonch, Xavier Gelabert, Bleron Klaiqi","doi":"arxiv-2409.01701","DOIUrl":"https://doi.org/arxiv-2409.01701","url":null,"abstract":"Radio Access Network (RAN) disaggregation is emerging as a key trend in\u0000beyond 5G, as it offers new opportunities for more flexible deployments and\u0000intelligent network management. A relevant problem in disaggregated RAN is the\u0000functional split selection, which dynamically decides which baseband (BB)\u0000functions of a base station are kept close to the radio units and which ones\u0000are centralized. In this context, this paper firstly presents an architectural\u0000framework for supporting this concept relying on the O-RAN architecture. Then,\u0000the paper analyzes how the functional split can be optimized to adapt to the\u0000different load conditions while minimizing energy costs.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
George Stamatelis, Kyriakos Stylianopoulos, George C. Alexandropoulos
In this paper, we consider the problem of jointly controlling the configuration of a Reconfigurable Intelligent Surface (RIS) with unit elements of discrete responses and a codebook-based transmit precoder in RIS-empowered Multiple-Input Single-Output (MISO) communication systems. The adjustable elements of the RIS and the precoding vector need to be jointly modified in real time to account for rapid changes in the wireless channels, making the application of complicated discrete optimization algorithms impractical. We present a novel Multi-Branch Attention Convolutional Neural Network (MBACNN) architecture for this design objective which is optimized using NeuroEvolution (NE), leveraging its capability to effectively tackle the non-differentiable problem arising from the discrete phase states of the RIS elements. The channel matrices of all involved links are first passed to separate self-attention layers to obtain initial embeddings, which are then concatenated and passed to a convolutional network for spatial feature extraction, before being fed to a per-element multi-layered perceptron for the final RIS phase configuration calculation. Our MBACNN architecture is then extended to multi-RIS-empowered MISO communication systems, and a novel NE-based optimization approach for the online distributed configuration of multiple RISs is presented. The superiority of the proposed single-RIS approach over both learning-based and classical discrete optimization benchmarks is showcased via extensive numerical evaluations over both stochastic and geometrical channel models. It is also demonstrated that the proposed distributed multi-RIS approach outperforms both distributed controllers with feedforward neural networks and fully centralized ones.
{"title":"Multi-Branch Attention Convolutional Neural Network for Online RIS Configuration with Discrete Responses: A Neuroevolution Approach","authors":"George Stamatelis, Kyriakos Stylianopoulos, George C. Alexandropoulos","doi":"arxiv-2409.01765","DOIUrl":"https://doi.org/arxiv-2409.01765","url":null,"abstract":"In this paper, we consider the problem of jointly controlling the\u0000configuration of a Reconfigurable Intelligent Surface (RIS) with unit elements\u0000of discrete responses and a codebook-based transmit precoder in RIS-empowered\u0000Multiple-Input Single-Output (MISO) communication systems. The adjustable\u0000elements of the RIS and the precoding vector need to be jointly modified in\u0000real time to account for rapid changes in the wireless channels, making the\u0000application of complicated discrete optimization algorithms impractical. We\u0000present a novel Multi-Branch Attention Convolutional Neural Network (MBACNN)\u0000architecture for this design objective which is optimized using NeuroEvolution\u0000(NE), leveraging its capability to effectively tackle the non-differentiable\u0000problem arising from the discrete phase states of the RIS elements. The channel\u0000matrices of all involved links are first passed to separate self-attention\u0000layers to obtain initial embeddings, which are then concatenated and passed to\u0000a convolutional network for spatial feature extraction, before being fed to a\u0000per-element multi-layered perceptron for the final RIS phase configuration\u0000calculation. Our MBACNN architecture is then extended to multi-RIS-empowered\u0000MISO communication systems, and a novel NE-based optimization approach for the\u0000online distributed configuration of multiple RISs is presented. The superiority\u0000of the proposed single-RIS approach over both learning-based and classical\u0000discrete optimization benchmarks is showcased via extensive numerical\u0000evaluations over both stochastic and geometrical channel models. It is also\u0000demonstrated that the proposed distributed multi-RIS approach outperforms both\u0000distributed controllers with feedforward neural networks and fully centralized\u0000ones.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the advancement and boom of autonomous vehicles, vehicular digital twins (VDTs) have become an emerging research area. VDT can solve the issues related to autonomous vehicles and provide improved and enhanced services to users. Recent studies have demonstrated the potential of using priorities in acquiring improved response time. However, since VDT is comprised of intra-twin and inter-twin communication, it leads to a reduced response time as traffic congestion grows, which causes issues in the form of accidents. It would be encouraging if priorities could be used in inter-twin communication of VDT for data sharing and processing tasks. Moreover, it would also be effective for managing the communication overhead on the digital twin layer of the cloud. This paper proposes a novel priority-based inter-twin communication in VDT to address this issue. We formulate the problem for priorities of digital twins and applications according to their categories. In addition, we describe the priority-based inter-twin communication in VDT in detail and algorithms for priority communication for intra-twin and inter-twin are designed, respectively. Finally, experiments on different priority tasks are conducted and compared with two existing algorithms, demonstrating our proposed algorithm's effectiveness and efficiency.
{"title":"Priority based inter-twin communication in vehicular digital twin networks","authors":"Qasim Zia, Chenyu Wang, Saide Zhu, Yingshu Li","doi":"arxiv-2409.01683","DOIUrl":"https://doi.org/arxiv-2409.01683","url":null,"abstract":"With the advancement and boom of autonomous vehicles, vehicular digital twins\u0000(VDTs) have become an emerging research area. VDT can solve the issues related\u0000to autonomous vehicles and provide improved and enhanced services to users.\u0000Recent studies have demonstrated the potential of using priorities in acquiring\u0000improved response time. However, since VDT is comprised of intra-twin and\u0000inter-twin communication, it leads to a reduced response time as traffic\u0000congestion grows, which causes issues in the form of accidents. It would be\u0000encouraging if priorities could be used in inter-twin communication of VDT for\u0000data sharing and processing tasks. Moreover, it would also be effective for\u0000managing the communication overhead on the digital twin layer of the cloud.\u0000This paper proposes a novel priority-based inter-twin communication in VDT to\u0000address this issue. We formulate the problem for priorities of digital twins\u0000and applications according to their categories. In addition, we describe the\u0000priority-based inter-twin communication in VDT in detail and algorithms for\u0000priority communication for intra-twin and inter-twin are designed,\u0000respectively. Finally, experiments on different priority tasks are conducted\u0000and compared with two existing algorithms, demonstrating our proposed\u0000algorithm's effectiveness and efficiency.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":"70 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The convergence of digital twin technology and the emerging 6G network presents both challenges and numerous research opportunities. This article explores the potential synergies between digital twin and 6G, highlighting the key challenges and proposing fundamental principles for their integration. We discuss the unique requirements and capabilities of digital twin in the context of 6G networks, such as sustainable deployment, real-time synchronization, seamless migration, predictive analytic, and closed-loop control. Furthermore, we identify research opportunities for leveraging digital twin and artificial intelligence to enhance various aspects of 6G, including network optimization, resource allocation, security, and intelligent service provisioning. This article aims to stimulate further research and innovation at the intersection of digital twin and 6G, paving the way for transformative applications and services in the future.
{"title":"When Digital Twin Meets 6G: Concepts, Obstacles, and Research Prospects","authors":"Wenshuai Liu, Yaru Fu, Zheng Shi, Hong Wang","doi":"arxiv-2409.02008","DOIUrl":"https://doi.org/arxiv-2409.02008","url":null,"abstract":"The convergence of digital twin technology and the emerging 6G network\u0000presents both challenges and numerous research opportunities. This article\u0000explores the potential synergies between digital twin and 6G, highlighting the\u0000key challenges and proposing fundamental principles for their integration. We\u0000discuss the unique requirements and capabilities of digital twin in the context\u0000of 6G networks, such as sustainable deployment, real-time synchronization,\u0000seamless migration, predictive analytic, and closed-loop control. Furthermore,\u0000we identify research opportunities for leveraging digital twin and artificial\u0000intelligence to enhance various aspects of 6G, including network optimization,\u0000resource allocation, security, and intelligent service provisioning. This\u0000article aims to stimulate further research and innovation at the intersection\u0000of digital twin and 6G, paving the way for transformative applications and\u0000services in the future.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":"157 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xinyang Du, Xuming Fang, Rong He, Li Yan, Liuming Lu, Chaoming Luo
The IEEE 802.11 MAC layer utilizes the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) mechanism for channel contention and access. However, in densely deployed Wi-Fi scenarios, intense competition may lead to packet collisions among users. Although many studies have used machine learning methods to optimize channel contention and access mechanisms, most of them are based on AP-centric single-agent models or distributed models, which still suffer poor generalization and insensitivity to dynamic environments. To address these challenges, this paper proposes an intelligent channel contention access mechanism that combines Federated Learning (FL) and Deep Deterministic Policy Gradient (DDPG) algorithms. Additionally, an FL model training pruning strategy and weight aggregation algorithm are designed to enhance the effectiveness of training samples and reduce the average MAC delay. We evaluate and validate the proposed solution using NS3-AI framework. Simulation results show that in static scenarios, our proposed scheme reduces the average MAC delay by 25.24% compared to traditional FL algorithms. In dynamic scenarios, it outperforms Average Federated Reinforcement Learning (A-FRL) and distributed Deep Reinforcement Learning (DRL) algorithms by 25.72% and 45.9%, respectively.
IEEE 802.11 MAC 层利用载波侦测多路访问与碰撞避免（CSMA/CA）机制进行信道争用和访问。然而，在密集部署的 Wi-Fi 场景中，激烈的竞争可能会导致用户之间的分组碰撞。虽然许多研究都使用机器学习方法来优化信道争用和接入机制，但大多数研究都是基于以接入点为中心的单个代理模型或分布式模型，这些模型仍然存在泛化能力差和对动态环境不敏感的问题。为了应对这些挑战，本文提出了一种智能信道争用和访问机制，该机制结合了联合学习（FL）和深度确定性策略梯度（DDPG）算法。此外，还设计了一种 FL 模型训练剪枝策略和权重聚合算法，以提高训练样本的有效性并降低平均 MAC 时延。我们使用 NS3-AI 框架对所提出的解决方案进行了评估和验证。仿真结果表明，在静态场景下，与传统的 FL 算法相比，我们提出的方案降低了 25.24% 的平均 MAC 延迟。在动态场景中，它优于平均联合强化学习（A-FRL）和分布式深度强化学习（DRL）算法，分别提高了 25.72% 和 45.9%。
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Caglar Tunc, Kubra Duran, Buse Bilgin, Gokhan Kalem, Berk Canberk
The emergence of beyond 5G (B5G) and 6G networks underscores the critical role of advanced computer-aided tools, such as network digital twins (DTs), in fostering autonomous networks and ubiquitous intelligence. Existing solutions in the DT domain primarily aim to model and automate specific tasks within the network lifecycle, which lack flexibility and adaptability for fully autonomous design and management. Unlike the existing DT approaches, we propose RAN optimization using the Digital Twin (DTRAN) framework that follows a holistic approach from core to edge networks. The proposed DTRAN framework enables real-time data management and communication with the physical network, which provides a more accurate and detailed digital replica than the existing approaches. We outline the main building blocks of the DTRAN and describe the details of our specific use case, which is RAN configuration optimization, to demonstrate the applicability of the proposed framework for a real-world scenario.
超越 5G (B5G) 和 6G 网络的出现凸显了先进计算机辅助工具（如网络数字孪生（DT）、自主网络信息ostering 和泛在智能）的重要作用。DT 领域的现有解决方案主要旨在对网络生命周期内的特定任务进行建模和自动化，缺乏灵活性和适应性，无法实现完全自主的设计和管理。与现有的 DT 方法不同，我们提出使用数字孪生（DTRAN）框架进行 RAN 优化，该框架采用从核心到边缘网络的整体方法。拟议的 DTRAN 框架可实现实时数据管理以及与物理网络的通信，从而提供比现有方法更准确、更详细的数字副本。我们概述了 DTRAN 的主要构建模块，并描述了我们的特定用例（即 RAN 配置优化）的细节，以演示所提框架在现实世界场景中的适用性。
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