Pub Date : 2022-10-01DOI: 10.1109/FNWF55208.2022.00009
S. Häger, S. Böcker, C. Wietfeld
The profound integration of sensing functionalities is seen as a major step stone towards unleashing the full potential of 6G, yet recent advances in current networks already offer new opportunities for sensing. This is especially true for the mmWave domain which offers a suitable environment for sensing services, e.g. due to the ability to detect and determine the angles of available link opportunities. Whereas previous work devised a fine 3D motion tracking by combining phase measurements along with several co-deployed nodes' links to the mmWave network, this work instead exploits multiple available propagation paths. We observe sub-10 $mumathrm{m}$ 3D motion tracking accuracy for the proposed single user equipment (UE) enhancement, mirroring the conventional multi-UE-based approach performance. However, our detailed error analysis finds that multipath may turn from friend to foe if undesired components are not suppressed sufficiently, as these amplify the effects of phase distortions due to channel noise and hardware imperfections. Our evaluation further yields that the technique is sensitive to erroneous propagation path angle information.
{"title":"3D Self-Motion Tracking Services: Coalescence of mmWave Beam Orientations and Phase Information","authors":"S. Häger, S. Böcker, C. Wietfeld","doi":"10.1109/FNWF55208.2022.00009","DOIUrl":"https://doi.org/10.1109/FNWF55208.2022.00009","url":null,"abstract":"The profound integration of sensing functionalities is seen as a major step stone towards unleashing the full potential of 6G, yet recent advances in current networks already offer new opportunities for sensing. This is especially true for the mmWave domain which offers a suitable environment for sensing services, e.g. due to the ability to detect and determine the angles of available link opportunities. Whereas previous work devised a fine 3D motion tracking by combining phase measurements along with several co-deployed nodes' links to the mmWave network, this work instead exploits multiple available propagation paths. We observe sub-10 $mumathrm{m}$ 3D motion tracking accuracy for the proposed single user equipment (UE) enhancement, mirroring the conventional multi-UE-based approach performance. However, our detailed error analysis finds that multipath may turn from friend to foe if undesired components are not suppressed sufficiently, as these amplify the effects of phase distortions due to channel noise and hardware imperfections. Our evaluation further yields that the technique is sensitive to erroneous propagation path angle information.","PeriodicalId":300165,"journal":{"name":"2022 IEEE Future Networks World Forum (FNWF)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123022107","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}
Pub Date : 2022-10-01DOI: 10.1109/FNWF55208.2022.00066
Nour Gritli, F. Khendek, M. Toeroe
The network slicing paradigm allows for partitioning a common network infrastructure into logical networks, i.e. network slices, tailored to specific user intents, including intents for isolation, security or performance reasons. A user may require isolation at different scopes: for the entire network slice, for the network slice subnets or for its composing network functions. Considering the relation between network slicing and Network Function Virtualization (NFV), the intents for isolation need to be mapped to and reflected in the descriptor(s) of network service(s) supporting the network slice(s). However, the network service descriptor (NSD) as defined today cannot capture all the network slice isolation requirements to be enforced during instantiation and at runtime. To overcome some of these limitations we propose extensions to the NSD based on our mapping of different isolation intents of the user to the NSD. We also show how to process the NSD extensions at instantiation and at runtime.
网络切片范式允许将公共网络基础设施划分为逻辑网络，即网络切片，根据特定的用户意图进行定制，包括出于隔离、安全或性能原因的意图。用户可能需要在不同的范围内进行隔离:对整个网络切片、对网络切片子网或对其组成网络功能进行隔离。考虑到网络切片和NFV (network Function Virtualization)之间的关系，隔离的意图需要映射到支持网络切片的网络服务的描述符中，并反映在描述符中。然而，目前定义的网络服务描述符(NSD)不能捕获在实例化和运行时实施的所有网络片隔离需求。为了克服其中的一些限制，我们根据用户到NSD的不同隔离意图的映射，提出了对NSD的扩展。我们还将展示如何在实例化和运行时处理NSD扩展。
{"title":"Extending the Network Service Descriptor to Capture User Isolation Intents for Network Slices","authors":"Nour Gritli, F. Khendek, M. Toeroe","doi":"10.1109/FNWF55208.2022.00066","DOIUrl":"https://doi.org/10.1109/FNWF55208.2022.00066","url":null,"abstract":"The network slicing paradigm allows for partitioning a common network infrastructure into logical networks, i.e. network slices, tailored to specific user intents, including intents for isolation, security or performance reasons. A user may require isolation at different scopes: for the entire network slice, for the network slice subnets or for its composing network functions. Considering the relation between network slicing and Network Function Virtualization (NFV), the intents for isolation need to be mapped to and reflected in the descriptor(s) of network service(s) supporting the network slice(s). However, the network service descriptor (NSD) as defined today cannot capture all the network slice isolation requirements to be enforced during instantiation and at runtime. To overcome some of these limitations we propose extensions to the NSD based on our mapping of different isolation intents of the user to the NSD. We also show how to process the NSD extensions at instantiation and at runtime.","PeriodicalId":300165,"journal":{"name":"2022 IEEE Future Networks World Forum (FNWF)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115341136","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}
In connected cars, the Controller Area Network (CAN) bus communication is the central connectivity and communication system for electronic control units (ECUs). Although the CAN bus is the central communication system for most cars, it lacks basic security features, i.e., authentication and encryption. Consequently, an attacker may compromise the CAN bus system effortlessly with even free attacking tools. In case of an attacker succeeds in compromising the ECUs, they can take control and stop the engine, disable the brakes, turn the lights on/off, etc., which makes the questions concerning the transformation of modern cars and safe driving. In this study, we propose a Personalized Federated learning-based Intrusion Detection System that ensures effective, secure training procedures without sharing any sort of data. In our research, we contemplate Supervised and Unsupervised Federated Learning to observe the behavior of CAN bus intrusion data. Our experiment result demonstrates that the Federated Learning-based supervised classifier effectively detects the CAN bus attacks, with accuracy of 99.98%.
{"title":"Personalized Federated Learning for Automotive Intrusion Detection Systems","authors":"Kabid Hassan Shibly, Md. Delwar Hossain, Hiroyuki Inoue, Yuzo Taenaka, Y. Kadobayashi","doi":"10.1109/FNWF55208.2022.00101","DOIUrl":"https://doi.org/10.1109/FNWF55208.2022.00101","url":null,"abstract":"In connected cars, the Controller Area Network (CAN) bus communication is the central connectivity and communication system for electronic control units (ECUs). Although the CAN bus is the central communication system for most cars, it lacks basic security features, i.e., authentication and encryption. Consequently, an attacker may compromise the CAN bus system effortlessly with even free attacking tools. In case of an attacker succeeds in compromising the ECUs, they can take control and stop the engine, disable the brakes, turn the lights on/off, etc., which makes the questions concerning the transformation of modern cars and safe driving. In this study, we propose a Personalized Federated learning-based Intrusion Detection System that ensures effective, secure training procedures without sharing any sort of data. In our research, we contemplate Supervised and Unsupervised Federated Learning to observe the behavior of CAN bus intrusion data. Our experiment result demonstrates that the Federated Learning-based supervised classifier effectively detects the CAN bus attacks, with accuracy of 99.98%.","PeriodicalId":300165,"journal":{"name":"2022 IEEE Future Networks World Forum (FNWF)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115377664","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}
Pub Date : 2022-10-01DOI: 10.1109/FNWF55208.2022.00138
Haijian Sun, Chris T. K. Ng, Yiming Huo, R. Hu, Ning Wang, Chi-Ming Chen, K. Vasudevan, Jin Yang, Webert Montlouis, D. Ayanda, K. Mishra, Kürşat Tekbıyık, N. Hussain, H. K. Sahoo, Yang Miao
The use of a large number of antenna elements, known as Massive MIMO, is seen as a key enabling technology in the 5G and Beyond wireless ecosystem. The intelligent use of a multitude of antenna elements unleashes unprecedented flexibility and control on the physical channel of the wireless medium. Through Massive MIMO and other techniques, it is envisioned that the 5G and beyond wireless system will be able to support high throughput, high reliability (low bit-error-rate (BER)), high energy efficiency, low latency, and an Internet-scale number of connected devices. Massive MIMO and related technologies will be deployed in the mid-band (sub 6 GHz) for coverage, all the way to mmWave bands to support large channel bandwidths. It is envisioned that Massive MIMO will be deployed in different environments: Frequency Division Duplex (FDD), (Time Division Duplex (TDD), indoor/outdoor, small cell, macro cell, and other heterogeneous networks (HetNet) configurations. Accurate and useful channel estimation remains a challenge in the efficient adoption of Massive MIMO techniques, and different performance-complexity tradeoffs may be supported by different Massive MIMO architectures such as digital, analog, and/or digital/analog hybrid. Carrier frequency offset (CFO), which arises due to the relative motion between the transmitter and receiver, is another important topic. Recently, maximum likelihood (ML) methods of CFO estimation have been proposed, that achieve very low root mean square (RMS) estimation errors, with a large scope for parallel processing and well suited for application with turbo codes. Massive MIMO opens up a whole new dimension of parameters where the wireless applications or other network layers may control or influence the operation and performance of the physical wireless channel. To fully reap the benefits of such flexibility, the latest advances in artificial intelligence (AI) and machine learning (ML) techniques will be leveraged to monitor and optimize the Massive MIMO sub-system. As such, a cross-layer open interface can facilitate exposing the programmability of Massive MIMO through techniques such as network slicing (NS) and network function virtualization (NFV). Finally, security needs to be integrated into the design of the system so the new functionality and performance of Massive MIMO can be utilized in a reliable manner.
{"title":"Massive MIMO","authors":"Haijian Sun, Chris T. K. Ng, Yiming Huo, R. Hu, Ning Wang, Chi-Ming Chen, K. Vasudevan, Jin Yang, Webert Montlouis, D. Ayanda, K. Mishra, Kürşat Tekbıyık, N. Hussain, H. K. Sahoo, Yang Miao","doi":"10.1109/FNWF55208.2022.00138","DOIUrl":"https://doi.org/10.1109/FNWF55208.2022.00138","url":null,"abstract":"The use of a large number of antenna elements, known as Massive MIMO, is seen as a key enabling technology in the 5G and Beyond wireless ecosystem. The intelligent use of a multitude of antenna elements unleashes unprecedented flexibility and control on the physical channel of the wireless medium. Through Massive MIMO and other techniques, it is envisioned that the 5G and beyond wireless system will be able to support high throughput, high reliability (low bit-error-rate (BER)), high energy efficiency, low latency, and an Internet-scale number of connected devices. Massive MIMO and related technologies will be deployed in the mid-band (sub 6 GHz) for coverage, all the way to mmWave bands to support large channel bandwidths. It is envisioned that Massive MIMO will be deployed in different environments: Frequency Division Duplex (FDD), (Time Division Duplex (TDD), indoor/outdoor, small cell, macro cell, and other heterogeneous networks (HetNet) configurations. Accurate and useful channel estimation remains a challenge in the efficient adoption of Massive MIMO techniques, and different performance-complexity tradeoffs may be supported by different Massive MIMO architectures such as digital, analog, and/or digital/analog hybrid. Carrier frequency offset (CFO), which arises due to the relative motion between the transmitter and receiver, is another important topic. Recently, maximum likelihood (ML) methods of CFO estimation have been proposed, that achieve very low root mean square (RMS) estimation errors, with a large scope for parallel processing and well suited for application with turbo codes. Massive MIMO opens up a whole new dimension of parameters where the wireless applications or other network layers may control or influence the operation and performance of the physical wireless channel. To fully reap the benefits of such flexibility, the latest advances in artificial intelligence (AI) and machine learning (ML) techniques will be leveraged to monitor and optimize the Massive MIMO sub-system. As such, a cross-layer open interface can facilitate exposing the programmability of Massive MIMO through techniques such as network slicing (NS) and network function virtualization (NFV). Finally, security needs to be integrated into the design of the system so the new functionality and performance of Massive MIMO can be utilized in a reliable manner.","PeriodicalId":300165,"journal":{"name":"2022 IEEE Future Networks World Forum (FNWF)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132505714","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}
Pub Date : 2022-10-01DOI: 10.1109/FNWF55208.2022.00051
A. Fayad, T. Cinkler, J. Rak, Balázs Sonkoly
Fifth-generation and Beyond (5GB) wireless networks have introduced new centralized architectures such as cloud radio access network (CRAN), which necessitate extremely high-capacity low latency Fronthaul (FH). CRAN has many advantageous features in terms of cost reduction, performance enhancement, ease of deployment, and centralization of network management. Nevertheless, designing and deploying a cost-efficient FH is still a stumbling block against mobile network operators (MNOs) that aim to deploy 5GB in a cost-effective manner. Many technologies have been proposed as a candidate for 5GB FH. Optical networking is the best long-term solution for overcoming the connection barrier between the radio access domain and the core network of 5GB. Therefore, we focus on optical technologies such as point-to-point optical fiber (P2P), passive optical networks (PON), and free-space optics (FSO). With that in mind, we propose in this paper an integer linear program (ILP) that results in a minimal total cost of ownership (TCO) considering both capital expenditure (Capex) and operational expenditure (Opex). For the scalability issue, we propose a heuristic algorithm to solve the problem for large network instances. In order to evaluate the applicability of the proposed framework, we run the simulations to compare different FH architectures for two deployment areas (dense and sparse).
{"title":"Cost-Efficient Optical Fronthaul Architectures for 5G and Future 6G Networks","authors":"A. Fayad, T. Cinkler, J. Rak, Balázs Sonkoly","doi":"10.1109/FNWF55208.2022.00051","DOIUrl":"https://doi.org/10.1109/FNWF55208.2022.00051","url":null,"abstract":"Fifth-generation and Beyond (5GB) wireless networks have introduced new centralized architectures such as cloud radio access network (CRAN), which necessitate extremely high-capacity low latency Fronthaul (FH). CRAN has many advantageous features in terms of cost reduction, performance enhancement, ease of deployment, and centralization of network management. Nevertheless, designing and deploying a cost-efficient FH is still a stumbling block against mobile network operators (MNOs) that aim to deploy 5GB in a cost-effective manner. Many technologies have been proposed as a candidate for 5GB FH. Optical networking is the best long-term solution for overcoming the connection barrier between the radio access domain and the core network of 5GB. Therefore, we focus on optical technologies such as point-to-point optical fiber (P2P), passive optical networks (PON), and free-space optics (FSO). With that in mind, we propose in this paper an integer linear program (ILP) that results in a minimal total cost of ownership (TCO) considering both capital expenditure (Capex) and operational expenditure (Opex). For the scalability issue, we propose a heuristic algorithm to solve the problem for large network instances. In order to evaluate the applicability of the proposed framework, we run the simulations to compare different FH architectures for two deployment areas (dense and sparse).","PeriodicalId":300165,"journal":{"name":"2022 IEEE Future Networks World Forum (FNWF)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131440535","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}
Pub Date : 2022-10-01DOI: 10.1109/FNWF55208.2022.00060
Jorge Gallego-Madrid, Ana Hermosilla, A. Gómez-Skarmeta
5G networks are encountering virtualization technologies as the foundations of the softwarization of the infrastructure. The usage of these techniques in the Connected and Automated Mobility (CAM) vertical is the key to address mobility and computing issues. The next generation of CAM services are demanding continuous sensor-data gathering and processing, but current solutions lack of flexibility and computing capabilities in the On-Board Units (OBUs). Consequently, a dynamic intermediate stratum with adaptable networking resources and data processing offloading is required to cover the requirements imposed by the upcoming vehicular applications and users. Besides, due to the changing nature of these environments, dynamic testing and validation of the deployed services is necessary to assure their correct functioning. In this line, a solution that exploits the Multi-access Edge Computing (MEC) paradigm to instantiate virtual OBUs (vOBUs) to act as virtual counterparts of the physical ones is presented. By doing so, in-vehicle OBUs can be protected from the characteristic disconnections of vehicular networks using the vOBU as an intermediate communication layer. Besides, they can offload heavy computing processes to the edge. The solution is dynamically deployed as a Network Application (NetApp) in a real 5G testbed in the context of the 5GASP project, in which it is also possible to test and evaluate the functioning of the NetApp after the deployment.
{"title":"Dynamic Deployment and Testing of Virtual On-board Units in 5G","authors":"Jorge Gallego-Madrid, Ana Hermosilla, A. Gómez-Skarmeta","doi":"10.1109/FNWF55208.2022.00060","DOIUrl":"https://doi.org/10.1109/FNWF55208.2022.00060","url":null,"abstract":"5G networks are encountering virtualization technologies as the foundations of the softwarization of the infrastructure. The usage of these techniques in the Connected and Automated Mobility (CAM) vertical is the key to address mobility and computing issues. The next generation of CAM services are demanding continuous sensor-data gathering and processing, but current solutions lack of flexibility and computing capabilities in the On-Board Units (OBUs). Consequently, a dynamic intermediate stratum with adaptable networking resources and data processing offloading is required to cover the requirements imposed by the upcoming vehicular applications and users. Besides, due to the changing nature of these environments, dynamic testing and validation of the deployed services is necessary to assure their correct functioning. In this line, a solution that exploits the Multi-access Edge Computing (MEC) paradigm to instantiate virtual OBUs (vOBUs) to act as virtual counterparts of the physical ones is presented. By doing so, in-vehicle OBUs can be protected from the characteristic disconnections of vehicular networks using the vOBU as an intermediate communication layer. Besides, they can offload heavy computing processes to the edge. The solution is dynamically deployed as a Network Application (NetApp) in a real 5G testbed in the context of the 5GASP project, in which it is also possible to test and evaluate the functioning of the NetApp after the deployment.","PeriodicalId":300165,"journal":{"name":"2022 IEEE Future Networks World Forum (FNWF)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130975518","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}
Pub Date : 2022-10-01DOI: 10.1109/FNWF55208.2022.00102
Eduard Axel Jorswieck, Pin-Hsun Lin, C. Janda
Novel security primitives built on physical layer parameters gain interest for the design of the wireless 6G networks. Regarding key value indicators (KVI), the requirements for future 6G wireless networks are formulated. We select a subset of these KVIs, including safety, anonymity, scalability, sustainability, reliability, and resilience. First, the KVIs are defined. Next, we identify and review suitable physical layer security techniques, including selected results. They contain a coding theorem for arbitrarily varying wiretap channels with an informed jammer. Results on practical secret key generation are reviewed. Confidential stealth, and covert communications are included as well as certain differential privacy techniques. The zero-outage secrecy rate and ergodic secrecy rate maximization are briefly described, too. Finally, multi-mode fiber transmission, multiple-input multiple-output multiple-eavesdropper wiretap channels, and reconfigurable intelligent surfaces are listed. We argue that these results and physical layer security techniques enable the envisaged KVIs. Finally, open gaps for future research are motivated and discussed.
{"title":"Physical Layer Security Based Enabling Technologies for 6G Communications Values","authors":"Eduard Axel Jorswieck, Pin-Hsun Lin, C. Janda","doi":"10.1109/FNWF55208.2022.00102","DOIUrl":"https://doi.org/10.1109/FNWF55208.2022.00102","url":null,"abstract":"Novel security primitives built on physical layer parameters gain interest for the design of the wireless 6G networks. Regarding key value indicators (KVI), the requirements for future 6G wireless networks are formulated. We select a subset of these KVIs, including safety, anonymity, scalability, sustainability, reliability, and resilience. First, the KVIs are defined. Next, we identify and review suitable physical layer security techniques, including selected results. They contain a coding theorem for arbitrarily varying wiretap channels with an informed jammer. Results on practical secret key generation are reviewed. Confidential stealth, and covert communications are included as well as certain differential privacy techniques. The zero-outage secrecy rate and ergodic secrecy rate maximization are briefly described, too. Finally, multi-mode fiber transmission, multiple-input multiple-output multiple-eavesdropper wiretap channels, and reconfigurable intelligent surfaces are listed. We argue that these results and physical layer security techniques enable the envisaged KVIs. Finally, open gaps for future research are motivated and discussed.","PeriodicalId":300165,"journal":{"name":"2022 IEEE Future Networks World Forum (FNWF)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127503056","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}
Pub Date : 2022-10-01DOI: 10.1109/FNWF55208.2022.00103
Yao Wei, Ricardo Paredes Cabrera, Chung-Horng Lung, S. Ajila
Handover is an essential and significant component of mobility management in cellular networks. Handover management is more challenging in Fifth Generation (5G) networks because of ultra-reliable low latency communications (URLLC) requirements. This paper proposes a handover enhancement mechanism, namely pre-connect handover (PHO), for user equipment (UE) to support seamless and reliable handover management for 5G networks. PHO was designed for UEs to start the pre-connection process earlier than the 3GPP baseline handover to meet the strict low latency requirements, while satisfying the quality of service (QoS) demands. Specifically, the radio resources including a capacity-adjustable buffer are pre-allocated at the candidate target cell(s) in advance. The feasibility of PHO has been investigated by considering various scenarios and validated extensively via Network Simulator 3 (NS-3). The empirical results demonstrated that the proposed PHO can successfully achieve pre-connect handovers and support QoS without packet loss.
{"title":"Pre-connect Handover Management for 5G Networks","authors":"Yao Wei, Ricardo Paredes Cabrera, Chung-Horng Lung, S. Ajila","doi":"10.1109/FNWF55208.2022.00103","DOIUrl":"https://doi.org/10.1109/FNWF55208.2022.00103","url":null,"abstract":"Handover is an essential and significant component of mobility management in cellular networks. Handover management is more challenging in Fifth Generation (5G) networks because of ultra-reliable low latency communications (URLLC) requirements. This paper proposes a handover enhancement mechanism, namely pre-connect handover (PHO), for user equipment (UE) to support seamless and reliable handover management for 5G networks. PHO was designed for UEs to start the pre-connection process earlier than the 3GPP baseline handover to meet the strict low latency requirements, while satisfying the quality of service (QoS) demands. Specifically, the radio resources including a capacity-adjustable buffer are pre-allocated at the candidate target cell(s) in advance. The feasibility of PHO has been investigated by considering various scenarios and validated extensively via Network Simulator 3 (NS-3). The empirical results demonstrated that the proposed PHO can successfully achieve pre-connect handovers and support QoS without packet loss.","PeriodicalId":300165,"journal":{"name":"2022 IEEE Future Networks World Forum (FNWF)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121512447","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}
Pub Date : 2022-10-01DOI: 10.1109/FNWF55208.2022.00030
P. Djukic
We elucidate our approach to top-down design of Application Programming Interfaces (APIs) for AI-enabled autonomic network slices. We start with the notion that an API design follows from the underlying software and hardware network architecture and the function and role of each architectural block. We then proceed to describe an adaptive and fully autonomic software architecture for hybrid (software and hardware) network slices, which has recently been a topic of interest for 6G networks. The architecture uses several software design and architectural patterns, which show how the architectural blocks behave and interact with each other. The knowledge of behaviour leads to required APIs. The APIs are further specified in the pattern definitions. We provide two examples of how the architecture is used to achieve network intent with self-adapting network slices.
{"title":"An Architecture for Autonomic Networks","authors":"P. Djukic","doi":"10.1109/FNWF55208.2022.00030","DOIUrl":"https://doi.org/10.1109/FNWF55208.2022.00030","url":null,"abstract":"We elucidate our approach to top-down design of Application Programming Interfaces (APIs) for AI-enabled autonomic network slices. We start with the notion that an API design follows from the underlying software and hardware network architecture and the function and role of each architectural block. We then proceed to describe an adaptive and fully autonomic software architecture for hybrid (software and hardware) network slices, which has recently been a topic of interest for 6G networks. The architecture uses several software design and architectural patterns, which show how the architectural blocks behave and interact with each other. The knowledge of behaviour leads to required APIs. The APIs are further specified in the pattern definitions. We provide two examples of how the architecture is used to achieve network intent with self-adapting network slices.","PeriodicalId":300165,"journal":{"name":"2022 IEEE Future Networks World Forum (FNWF)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114975761","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}
Pub Date : 2022-10-01DOI: 10.1109/FNWF55208.2022.00078
Mikel Serón, Ángel Martín, G. Velez
Cars capture and generate huge volumes of data in real-time, including the driving dynamics, the environment, and the driver and passengers' activities. With the proliferation of Connected and Automated Mobility (CAM) applications, the value of vehicle data is getting higher for the automotive industry as it is not limited to onboard systems and services. This paper proposes an architecture that exploits Multi-access Edge Computing (MEC) technology of 5G networks to enable data monetisation. It employs a virtualisation framework that instantiates on consumer demand pipelines that process data samples according to Service Level Agreement (SLA) policies, licensing terms and Region Of Interest (ROI) clusters with a privacy-centric design. In addition, the aspects that need to be considered when creating a data marketplace for the automotive sector are identified while highlighting the design features that go beyond the current scientific and market solutions.
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