Pub Date : 2022-10-01DOI: 10.1109/FNWF55208.2022.00020
M. A. Enright, Eman M. Hammad, Ashutosh Dutta
In the evolution of 6G and Future Networks, a dynamic, flexible, learning-based security architecture will be essential with the ability to handle both current and evolving cybersecurity threats. This is specially critical with future networks' increased reliance on distributed learning-based approaches for operation. To address this challenge, a distributed learning framework must provide security and trust in an integrated fashion. In contrast to existing approach such as federated learning (FL), that update parameters of a shared model, this work proposes an architecture that is capable of integrating advanced learning with real-time digital forensics, e.g. monitoring compute and storage resources. With real-time monitoring, it is possible to develop a learning-based, real-time Zero-Trust Architecture (ZTA) to achieve the high levels of security. The proposed architecture, serves as a framework to enable and spur innovation, where new machine learning based techniques can be developed for enhanced real-time, adaptive and proactive security, thus, embedding future networks' security with learning-based ZTA elements.
{"title":"A Learning-Based Zero-Trust Architecture for 6G and Future Networks","authors":"M. A. Enright, Eman M. Hammad, Ashutosh Dutta","doi":"10.1109/FNWF55208.2022.00020","DOIUrl":"https://doi.org/10.1109/FNWF55208.2022.00020","url":null,"abstract":"In the evolution of 6G and Future Networks, a dynamic, flexible, learning-based security architecture will be essential with the ability to handle both current and evolving cybersecurity threats. This is specially critical with future networks' increased reliance on distributed learning-based approaches for operation. To address this challenge, a distributed learning framework must provide security and trust in an integrated fashion. In contrast to existing approach such as federated learning (FL), that update parameters of a shared model, this work proposes an architecture that is capable of integrating advanced learning with real-time digital forensics, e.g. monitoring compute and storage resources. With real-time monitoring, it is possible to develop a learning-based, real-time Zero-Trust Architecture (ZTA) to achieve the high levels of security. The proposed architecture, serves as a framework to enable and spur innovation, where new machine learning based techniques can be developed for enhanced real-time, adaptive and proactive security, thus, embedding future networks' security with learning-based ZTA elements.","PeriodicalId":300165,"journal":{"name":"2022 IEEE Future Networks World Forum (FNWF)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121117113","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/FNWF55208.2022.00047
Filippo Malandra, Mari Silbey, Rolando Alvarez, Bob Cacace, Troy Hege
In the wake of the pandemic, the federal government is directing billions of dollars to state and local governments in an effort to connect all residents to fast, affordable, reliable Internet service. The funding is a welcome investment as communities work to connect the unconnected. However, it also means community leaders need to move quickly to evaluate technologies and broadband deployment strategies. Citizen Broadband and Radio Service (CBRS) spectrum represents a novel solution to support such broadband initiatives but, due to its recent use, it comes with a number of unknowns that need to be considered and experimentally evaluated. In this paper, we propose an overview on the CBRS technology with a particular focus on lessons learned from existing deployments in the US. In particular, two use cases-in Buffalo, NY and Cleveland, OH-are presented to focus on two important lessons learned regarding the importance of i) thoroughly characterizing the propagation in the area of interest and ii) involving experts in different areas of specialty, such as structural/RF engineering or property management.
{"title":"Community CBRS Networks - What You Need to Know","authors":"Filippo Malandra, Mari Silbey, Rolando Alvarez, Bob Cacace, Troy Hege","doi":"10.1109/FNWF55208.2022.00047","DOIUrl":"https://doi.org/10.1109/FNWF55208.2022.00047","url":null,"abstract":"In the wake of the pandemic, the federal government is directing billions of dollars to state and local governments in an effort to connect all residents to fast, affordable, reliable Internet service. The funding is a welcome investment as communities work to connect the unconnected. However, it also means community leaders need to move quickly to evaluate technologies and broadband deployment strategies. Citizen Broadband and Radio Service (CBRS) spectrum represents a novel solution to support such broadband initiatives but, due to its recent use, it comes with a number of unknowns that need to be considered and experimentally evaluated. In this paper, we propose an overview on the CBRS technology with a particular focus on lessons learned from existing deployments in the US. In particular, two use cases-in Buffalo, NY and Cleveland, OH-are presented to focus on two important lessons learned regarding the importance of i) thoroughly characterizing the propagation in the area of interest and ii) involving experts in different areas of specialty, such as structural/RF engineering or property management.","PeriodicalId":300165,"journal":{"name":"2022 IEEE Future Networks World Forum (FNWF)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121403348","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/FNWF55208.2022.00033
H. Kholidy, Riaad Kamaludeen
Federated Learning (FL) is a decentralized learning approach, meaning it learns from data housed locally on devices such as tablets, cellular phones, and more, and does not collect nor transfer user-sensitive data but merely learns from the data utilizing a shared model and sending periodical updates. Using federated learning throws out the problems associated with user privacy and the high bandwidth needed to transmit resource-intensive files to a central server for training. However, FL systems may be compromised to make a wrong decision or disclose private data once the attacker modifies the FL model and/or its paraments. The main contribution of this paper includes (1) introducing a comprehensive study that explores the FL and how it applies to different domains like healthcare and medicine, Insurance and Finance, Robotics and Autonomous Systems, Virtual Reality, and 5G. (2) Develop a Hashgraph-based federated learning Approach (HFLA) to protect the 5G network against poisoning and membership inherence attacks. The HFLA was evaluated using our Federated 5G testbed and proved its superiority compared to other existing FL approaches.
{"title":"An Innovative Hashgraph-based Federated Learning Approach for Multi Domain 5G Network Protection","authors":"H. Kholidy, Riaad Kamaludeen","doi":"10.1109/FNWF55208.2022.00033","DOIUrl":"https://doi.org/10.1109/FNWF55208.2022.00033","url":null,"abstract":"Federated Learning (FL) is a decentralized learning approach, meaning it learns from data housed locally on devices such as tablets, cellular phones, and more, and does not collect nor transfer user-sensitive data but merely learns from the data utilizing a shared model and sending periodical updates. Using federated learning throws out the problems associated with user privacy and the high bandwidth needed to transmit resource-intensive files to a central server for training. However, FL systems may be compromised to make a wrong decision or disclose private data once the attacker modifies the FL model and/or its paraments. The main contribution of this paper includes (1) introducing a comprehensive study that explores the FL and how it applies to different domains like healthcare and medicine, Insurance and Finance, Robotics and Autonomous Systems, Virtual Reality, and 5G. (2) Develop a Hashgraph-based federated learning Approach (HFLA) to protect the 5G network against poisoning and membership inherence attacks. The HFLA was evaluated using our Federated 5G testbed and proved its superiority compared to other existing FL approaches.","PeriodicalId":300165,"journal":{"name":"2022 IEEE Future Networks World Forum (FNWF)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128282808","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/FNWF55208.2022.00038
T. Miyazawa, M. Jibiki, Ved P. Kafle
The agile deployment of network functions on containers in a virtualized network infrastructure is a viable solution for realizing future diverse microservice-based applications in 5G and beyond-5G networks. Because the CPU utilization of each containerized network function (CNF) is time-varying, microservice-based applications may experience a shortage or wastage of CPU resources if a fixed amount of resources is allocated to each CNF. In this study, to realize autonomous and proactive resource control for CNFs, we proposed and implemented an automated sequential processing system that cascades CPU utilization analytics by applying least-squares support vector regression and resource arbitration scheduling for CNFs. Through experiments and numerical analyses, we prove that the proposed system is sufficiently agile to perform automated sequential processing in approximately 2 s.
{"title":"Automated Data Analytics and Resource Arbitration Scheduling for Containerized Network Functions","authors":"T. Miyazawa, M. Jibiki, Ved P. Kafle","doi":"10.1109/FNWF55208.2022.00038","DOIUrl":"https://doi.org/10.1109/FNWF55208.2022.00038","url":null,"abstract":"The agile deployment of network functions on containers in a virtualized network infrastructure is a viable solution for realizing future diverse microservice-based applications in 5G and beyond-5G networks. Because the CPU utilization of each containerized network function (CNF) is time-varying, microservice-based applications may experience a shortage or wastage of CPU resources if a fixed amount of resources is allocated to each CNF. In this study, to realize autonomous and proactive resource control for CNFs, we proposed and implemented an automated sequential processing system that cascades CPU utilization analytics by applying least-squares support vector regression and resource arbitration scheduling for CNFs. Through experiments and numerical analyses, we prove that the proposed system is sufficiently agile to perform automated sequential processing in approximately 2 s.","PeriodicalId":300165,"journal":{"name":"2022 IEEE Future Networks World Forum (FNWF)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127328283","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/FNWF55208.2022.00109
Ishani B. Majumdar, Shaghayegh Vosoughitabar, C. Wu, N. Mandayam, Joseph Brodie, Behzad Golparvar, Ruoqian Wang
The utilization of newer spectrum bands such as in 5G and 6G networks, has the potential to inadvertently cause interference to passive sensing applications operating in the adjacent portions of spectrum. One such application that has received a lot of attention has been passive weather sensing where leakage from 5G mmWave band transmissions in the 26 GHz spectrum could potentially impact the observations of passive sensors on weather prediction satellites. To mitigate problems such as the above, we present a design framework that can be employed in mm Wave networks by using filtennas (or filtering antennas) at the transmitter along with integrated resource allocation to minimize leakage into adjacent channels. Specifically, we propose an Iterative Leakage Aware Water Filling solution to allocate power and bandwidth in a system employing filtennas that guarantees performance requirements while reducing the leakage. In addition, a key contribution of this work is the characterization of the leakage function based on the order of filtennas which is incorporated in our resource allocation framework.
{"title":"Resource Allocation Using Filtennas in the Presence of Leakage","authors":"Ishani B. Majumdar, Shaghayegh Vosoughitabar, C. Wu, N. Mandayam, Joseph Brodie, Behzad Golparvar, Ruoqian Wang","doi":"10.1109/FNWF55208.2022.00109","DOIUrl":"https://doi.org/10.1109/FNWF55208.2022.00109","url":null,"abstract":"The utilization of newer spectrum bands such as in 5G and 6G networks, has the potential to inadvertently cause interference to passive sensing applications operating in the adjacent portions of spectrum. One such application that has received a lot of attention has been passive weather sensing where leakage from 5G mmWave band transmissions in the 26 GHz spectrum could potentially impact the observations of passive sensors on weather prediction satellites. To mitigate problems such as the above, we present a design framework that can be employed in mm Wave networks by using filtennas (or filtering antennas) at the transmitter along with integrated resource allocation to minimize leakage into adjacent channels. Specifically, we propose an Iterative Leakage Aware Water Filling solution to allocate power and bandwidth in a system employing filtennas that guarantees performance requirements while reducing the leakage. In addition, a key contribution of this work is the characterization of the leakage function based on the order of filtennas which is incorporated in our resource allocation framework.","PeriodicalId":300165,"journal":{"name":"2022 IEEE Future Networks World Forum (FNWF)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131692507","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/FNWF55208.2022.00031
C. Rinaldi, Francesco Smarra, F. Franchi, A. D’innocenzo
5G technologies have opened a wide range of possibilities in all the application fields that require features as low latency and massive Machine-Type Communications (mMTC), such as Structural Health Monitoring (SHM) systems. In this paper, an edge-based Machine Learning (ML) enabled public safety service is proposed where an SHM system is exploited to support public protection actions in case of critical situations. To this aim, an end-to-end solution based on ultra Reliable and Low Latency (uRLLC) networks is proposed together with an innovative ML-based approach that uses SHM systems information to detect critical issues in structures. The unprecedented level of reliability offered by uRLLC networks together with the efficient ML modeling capabilities allow to efficiently propagate an alarm message in case of emergency. Referring to the 5G vision, the proposed SHM system can thus be considered depending on the operational scenario: in the case of data collection and processing from sensors, considering the high number of sensors installed, it can refer to the massive Machine-Type Communications (mMTC) context; vice-versa, during a safety critical situation e.g., during an earthquake or under structural problems, or immediately after the event, the use case requires high reliability, connectivity, and sometimes low latency, i.e. uRLLC.
{"title":"An Edge-Based Machine Learning-Enabled Approach in Structural Health Monitoring for Public Protection","authors":"C. Rinaldi, Francesco Smarra, F. Franchi, A. D’innocenzo","doi":"10.1109/FNWF55208.2022.00031","DOIUrl":"https://doi.org/10.1109/FNWF55208.2022.00031","url":null,"abstract":"5G technologies have opened a wide range of possibilities in all the application fields that require features as low latency and massive Machine-Type Communications (mMTC), such as Structural Health Monitoring (SHM) systems. In this paper, an edge-based Machine Learning (ML) enabled public safety service is proposed where an SHM system is exploited to support public protection actions in case of critical situations. To this aim, an end-to-end solution based on ultra Reliable and Low Latency (uRLLC) networks is proposed together with an innovative ML-based approach that uses SHM systems information to detect critical issues in structures. The unprecedented level of reliability offered by uRLLC networks together with the efficient ML modeling capabilities allow to efficiently propagate an alarm message in case of emergency. Referring to the 5G vision, the proposed SHM system can thus be considered depending on the operational scenario: in the case of data collection and processing from sensors, considering the high number of sensors installed, it can refer to the massive Machine-Type Communications (mMTC) context; vice-versa, during a safety critical situation e.g., during an earthquake or under structural problems, or immediately after the event, the use case requires high reliability, connectivity, and sometimes low latency, i.e. uRLLC.","PeriodicalId":300165,"journal":{"name":"2022 IEEE Future Networks World Forum (FNWF)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121200014","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/FNWF55208.2022.00076
Yi Shi, Y. Sagduyu, T. Erpek, M. C. Gursoy
This paper studies how to launch an attack on reinforcement learning for network slicing in NextG radio access network (RAN). An adversarial machine learning approach is pursued to construct an over-the-air attack that manipulates the reinforcement learning algorithm and disrupts resource allocation of NextG RAN slicing. Resource blocks are allocated by the base station (gNodeB) to the requests of user equipments and reinforcement learning is applied to maximize the total reward of accepted requests over time. In the meantime, the jammer builds its surrogate model with its own reinforcement learning algorithm by observing the spectrum. This surrogate model is used to select which resource blocks to jam subject to an energy budget. The jammer's goal is to maximize the number of failed network slicing requests. For that purpose, the jammer jams the resource blocks and reduces the reinforcement learning algorithm's reward that is used as the input to update the reinforcement learning algorithm for network slicing. As result, the network slicing performance does not recover for a while even after the jammer stops jamming. The recovery time and the loss in the reward are evaluated for this attack. Results demonstrate the effectiveness of this attack compared to benchmark (random and myopic) jamming attacks, and indicate vulnerabilities of NextG RAN slicing to smart jammers.
{"title":"Jamming Attacks on NextG Radio Access Network Slicing with Reinforcement Learning","authors":"Yi Shi, Y. Sagduyu, T. Erpek, M. C. Gursoy","doi":"10.1109/FNWF55208.2022.00076","DOIUrl":"https://doi.org/10.1109/FNWF55208.2022.00076","url":null,"abstract":"This paper studies how to launch an attack on reinforcement learning for network slicing in NextG radio access network (RAN). An adversarial machine learning approach is pursued to construct an over-the-air attack that manipulates the reinforcement learning algorithm and disrupts resource allocation of NextG RAN slicing. Resource blocks are allocated by the base station (gNodeB) to the requests of user equipments and reinforcement learning is applied to maximize the total reward of accepted requests over time. In the meantime, the jammer builds its surrogate model with its own reinforcement learning algorithm by observing the spectrum. This surrogate model is used to select which resource blocks to jam subject to an energy budget. The jammer's goal is to maximize the number of failed network slicing requests. For that purpose, the jammer jams the resource blocks and reduces the reinforcement learning algorithm's reward that is used as the input to update the reinforcement learning algorithm for network slicing. As result, the network slicing performance does not recover for a while even after the jammer stops jamming. The recovery time and the loss in the reward are evaluated for this attack. Results demonstrate the effectiveness of this attack compared to benchmark (random and myopic) jamming attacks, and indicate vulnerabilities of NextG RAN slicing to smart jammers.","PeriodicalId":300165,"journal":{"name":"2022 IEEE Future Networks World Forum (FNWF)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123502959","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/FNWF55208.2022.00095
C. Lim, Chathurika Ranaweera, A. Nirmalathas, Yijie Tao, Sampath Edirisinghe, L. Wosinska, Tingting Song
In this paper, we review the work we have carried out in the investigation of the transport network in a hybrid fiber-wireless system to cater for the next generation wireless networks. We have demonstrated advanced coordination functionality in the physical layer to enable coordination between remote radio heads. We have also devised an optimization framework to jointly optimize the wireless and optical network that minimizes the deployment cost. We conclude the paper by providing insights into a reconfigurable optical architecture that can be used to support wireless networks of 6G and beyond.
{"title":"Optical X-haul for 5G /6G: Design and Deployment Standpoint","authors":"C. Lim, Chathurika Ranaweera, A. Nirmalathas, Yijie Tao, Sampath Edirisinghe, L. Wosinska, Tingting Song","doi":"10.1109/FNWF55208.2022.00095","DOIUrl":"https://doi.org/10.1109/FNWF55208.2022.00095","url":null,"abstract":"In this paper, we review the work we have carried out in the investigation of the transport network in a hybrid fiber-wireless system to cater for the next generation wireless networks. We have demonstrated advanced coordination functionality in the physical layer to enable coordination between remote radio heads. We have also devised an optimization framework to jointly optimize the wireless and optical network that minimizes the deployment cost. We conclude the paper by providing insights into a reconfigurable optical architecture that can be used to support wireless networks of 6G and beyond.","PeriodicalId":300165,"journal":{"name":"2022 IEEE Future Networks World Forum (FNWF)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121173441","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/FNWF55208.2022.00056
G. Renzi
The Analytic Hierarchic Process (AHP) is a type of analysis that allows finding - considering different criteria and given a certain objective - the best option among different choices [1]. Since it is not always possible and easy to collect proper data for AHP (especially if the context is complex and the stakeholders are complicated to contact), in this paper we want to propose a new methodology that allow to perform the AHP using data collected for other scopes. Following in the footsteps of the AHP proposed by Saaty [1], through a new methodology this paper identifies the most important 5G services for the competitiveness of the port. The research field is the 5G-LOGINNOV project and the research question that guided the work is: which 5G service best meets the most urgent needs of the ports? The aim of this paper is to propose a methodology for an AHP that can be re-used to understand which are the most urgent services needed in line with the defined objectives.
{"title":"Discovering the most urgent 5G services for the competitiveness of the port using an updated Analytic Hierarchic Process in the 5G-LOGINNOV project","authors":"G. Renzi","doi":"10.1109/FNWF55208.2022.00056","DOIUrl":"https://doi.org/10.1109/FNWF55208.2022.00056","url":null,"abstract":"The Analytic Hierarchic Process (AHP) is a type of analysis that allows finding - considering different criteria and given a certain objective - the best option among different choices [1]. Since it is not always possible and easy to collect proper data for AHP (especially if the context is complex and the stakeholders are complicated to contact), in this paper we want to propose a new methodology that allow to perform the AHP using data collected for other scopes. Following in the footsteps of the AHP proposed by Saaty [1], through a new methodology this paper identifies the most important 5G services for the competitiveness of the port. The research field is the 5G-LOGINNOV project and the research question that guided the work is: which 5G service best meets the most urgent needs of the ports? The aim of this paper is to propose a methodology for an AHP that can be re-used to understand which are the most urgent services needed in line with the defined objectives.","PeriodicalId":300165,"journal":{"name":"2022 IEEE Future Networks World Forum (FNWF)","volume":"22-23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121641027","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/FNWF55208.2022.00120
Caner Bektas, S. Böcker, C. Wietfeld
Increasing automation of industry verticals and frequently changing production cycles require a high level of production line modularity and are locally accompanied by frequently changing disjunctive application requirements. Thus, current and future wireless communication networks need to face the challenge of providing opportunities to rapidly adapt the network to its changing application demands in order to guarantee a resilient and interference-free communication. A possible key technology for implementing such a solution is represented by private 5G networks that are additionally equipped with network slicing in order to be able to meet the versatile requirements of novel applications. However, resilient network design as well as network slice dimensioning can only be guaranteed through detailed network planning. This requires expert knowledge, which is not yet present at most companies or institutions. Accordingly, automation of the network planning process is a possible solution. Existing coverage planning frameworks are extended by capacity planning in this work, and network slicing is introduced. It is shown on the basis of a realistic scenario that the predictability of data (e.g., traffic characteristics in low-latency slices) significantly influences capacity planning and must be taken into account in the dimensioning of 5G and beyond future mobile networks.
{"title":"The Cost of Uncertainty: Impact of Overprovisioning on the Dimensioning of Machine Learning-based Network Slicing","authors":"Caner Bektas, S. Böcker, C. Wietfeld","doi":"10.1109/FNWF55208.2022.00120","DOIUrl":"https://doi.org/10.1109/FNWF55208.2022.00120","url":null,"abstract":"Increasing automation of industry verticals and frequently changing production cycles require a high level of production line modularity and are locally accompanied by frequently changing disjunctive application requirements. Thus, current and future wireless communication networks need to face the challenge of providing opportunities to rapidly adapt the network to its changing application demands in order to guarantee a resilient and interference-free communication. A possible key technology for implementing such a solution is represented by private 5G networks that are additionally equipped with network slicing in order to be able to meet the versatile requirements of novel applications. However, resilient network design as well as network slice dimensioning can only be guaranteed through detailed network planning. This requires expert knowledge, which is not yet present at most companies or institutions. Accordingly, automation of the network planning process is a possible solution. Existing coverage planning frameworks are extended by capacity planning in this work, and network slicing is introduced. It is shown on the basis of a realistic scenario that the predictability of data (e.g., traffic characteristics in low-latency slices) significantly influences capacity planning and must be taken into account in the dimensioning of 5G and beyond future mobile networks.","PeriodicalId":300165,"journal":{"name":"2022 IEEE Future Networks World Forum (FNWF)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127002368","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}
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