Development of Wireless Access and Flexible Networking Technologies for 5G Cellular Systems

Sampei, Seiichi

doi:10.1587/transcom.2016fgi0001

Cited by 4 publications

(4 citation statements)

References 22 publications

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“…Networkconnectivitycanbedefinedastheaveragenumberofnodesconnectingtoothersetsof nodesoveracertaintimedurationunderthelinkcondition (Chuanmeizhietal.,2016).Thisimplies thatwhentheconnectivityofthenetworkissostrong,therewillbeadecreaseintheprobability ofreconnectionwhichisasaresultoftheincreaseofthedistance.Insimplerterms,connectivity istheconditionwhenavalidcommunicationroutebetweentwoormorenodesexistsinorderto exchangedatapackets (Artimy,Robertson,&Phillips,2004).Ifthereisconnectivityfailuredueto severalfactorsforexampleincreaseindistancebetweennodes,decreaseindensityofnodes,velocity inmobilitysituation,etc.willresultindatabeinglostduringthoseconnectivityoutageperiodsand causesomedatatobedeliveredoutoforder.Duetotheseconnectivityoutageperiods,severedelays canalsobeexperiencedwhichcompromisetime-criticalapplicationthusaffectingQoS (Pérezet al,2018).Intheofvehicularnetworks,(Chuanmeizhietal.,2016)concludedthatthedecreaseof stabilityofconnectivityleadstoasmallerprobabilityofreconnectionofnodesespeciallywhenthe communicationrangeandaveragedistancebecomeslarger.Tosolvethischallenge,theyproposethat thedensityofnodesbeincreasedinanareawheretherearemanyIoTdevicesastoavoidcongesting thenetworkthuscreatingstrongconnectivitybetweennodeswhichmakesanetworktobehaveas wired. In order to satisfy connection conditions in 5G systems, (Sampei, 2017) proposed that a controllerfunctionalityisshiftedtoanedgecontrollerwhichwillbelocatedclosetothecontrolled machine.Tofurtherenhancetheflexibilityofnetworking,networkslicingandsoftwarizationusing thesoftware-definednetwork(SDN)aretheothertwopillarswhichweresuggestedinthisworktobe includedinthenetwork.Itwasconcludedthat,itisbesttomakeaflexiblewirelessaccesssystemto supportthedeviationofthenumberofsimultaneouslyconnecteddevicesinenormousconnections.…”

Section: Connectivitymentioning

confidence: 99%

“…End-to-endlatencywhichcanalsobecalledservicedelayistheresponsetime(thetimerequired toservearequest)thatisthetimeintervalbetweenthemomentwhenanIoTnodesendsaservice requestandwhenitreceivestheresponseforthatrequest (Yousefpour,Ishigaki,Gour,&Jue,2018) isanotheraspectofnetworkqualitywhichneedsattention.Oneofthemajorcausesofanend-to-end latencycanbeattributedtoservercomputetime,networkflexibilityintermsoftransferspeeds (Chen etal.,2017),adistanceofthecontroller(servernode)tothecontrolledmachine(clientnode) (Sampei, 2017),etc.End-to-endlatencyisachallengebecauseofdifferentwirelessnetworkcommunication systemscomingintoplay(Wi-Fi,3G,4G,5G,LTE-U)whichdoeshavedifferentflexibilities.Toreduce end-to-endlatencyandsatisfylatencyconditionsinthenewcoming5G, Sampei(2017)proposed thatthelocationofcontrollersbenearthecontrolledmachine (Sampei,2017).Moreover,Sampei (2017)furthersuggestedthatnetworkfunctionalityinwirelessaccessoughttobemadeflexibleas tofulfillend-to-endQoSrequirement.Additionally,Souzaetal.highlightedthatanotherkeypoint tonoteoncontrollersis,theyshouldbeareducednumberofinvolvedcontrolelementsandaverage delay provided by each one of them if we are to enable low latency end-to-end communication (Souza et al, 2017). Authors Chen et al provided some insights on how to reduce end-to-end latencyby60%-70%withoutsacrificingaccuracybyintroducinganovelblack-boxmulti-algorithm whichleveragestemporallocalityapproach (Chenetal.,2017).Thetwoparameterstheyusedto measuretheiralgorithmforanend-to-endlatencywastheprocessingandnetworkingtime(when theframeiscapturedtowhenthecorrespondingresponseisreceived).Researchersin (Yousefpour etal.,2018)…”

Section: End-to-end Latencymentioning

confidence: 99%

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A Review of Quality of Service in Fog Computing for the Internet of Things

Vambe

Chang

Sibanda

2020

International Journal of Fog Computing

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With the advent of the paradigm of the Internet of Things, many computing elements need many modifications to promote Quality of Service (QoS). Quality of Service is a pillar that promotes real-time reaction to time-critical tasks. Any impediments to QoS should be resolved and handled. In 2012, fog computing was implemented to enhance QoS in current systems in a bid to tackle QoS problems encountered by using cloud computing alone. Currently, the primary focus in fog computing is now on enhancing QoS. The primary goal of this study is, therefore, to critically review and evaluate the literature on the work done to improve elements of QoS in fog computing. This study begins by examining the roots of history, characteristics, and advantages of fog computing. Secondly, it discusses the important elements of QoS parameters. Finally, open problems that still affect fog computing are identified and discussed in order to achieve enhanced QoS.

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Section: Connectivitymentioning

confidence: 99%

Section: End-to-end Latencymentioning

confidence: 99%

A Review of Quality of Service in Fog Computing for the Internet of Things

Vambe

Chang

Sibanda

2020

International Journal of Fog Computing

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“…To meet the needs of Industrial IoT in the future, it is important to focus on the 5G framework revolution, which Industrial IoT drives. Literature [24] explores the key 5G technologies, and after introducing the services and technology trends of 5G, it discusses the access to the key technologies, especially the cut-through angle, and also discusses the flexibility of the 5G network that can handle the QOS support services, especially dealing with the end-to-end delay constraints. Literature [25] summarizes the 5G Internet technologies, pointing out that the cell's breathing technique and the base station dormancy technique can reduce the base station's energy consumption, expand the communication capacity and ensure user fairness while guaranteeing network communication.…”

Section: Introductionmentioning

confidence: 99%

Smart Vehicle Driving Behavior Analysis Based on 5G, IoT and Edge Computing Technologies

Jin,

Zhang

2024

Applied Mathematics and Nonlinear Sciences

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With the development of science and technology, the realization of intelligent vehicle autonomous driving has become a popular research. In order to realize the optimal autonomous driving behavior, this paper proposes a vehicle-splittable task offloading algorithm for edge offloading in the IoT environment based on the study of edge computing technology and the integration of 5G communication. On this basis, a task offloading model is built by combining Markov decision learning algorithm in order to improve the ability of real-time computation of intelligent vehicles in real driving environment and, at the same time, to realize the recording of dynamic driving behavior of the vehicle and the prediction of intelligent and safe paths in the dynamic IoV environment. Simulation experiments verify the performance of the VPEO algorithm. When there are varying numbers of vehicles and tasks, the VPEO algorithm performs better according to the experimental results. After 750 iterations, the VPEO algorithm achieves a 94% success rate when calculating tasks and stabilizes. In the intelligent warning experiments, the VPEO algorithm measured the average accuracy of lane offset distance, the average judgment correctness of vehicle class, and vulnerable traffic participant in relation to the position of the lane line were 84.66%, 92.26% and 94.69%, respectively. The VPEO algorithm can perform real-time calculations of intelligent driving information, and warnings about intelligent driving safety can be provided.

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“…URLLC and m-MTC are particularly attractive as potential enablers to expand the mobile communication market to the new areas, such as mission critical and networked industrial applications. It is therefore urgent to identify new and concrete use cases as applications utilizing 5G [3]. In Japan, the Ministry of Internal Affairs and Communications (MIC) began its 5G Integrated Verification Trials in 2017 [4].…”

Section: Introductionmentioning

confidence: 99%

Field Trial on 5G Low Latency Radio Communication System Towards Application to Truck Platooning

Mikami

Yoshino

2019

IEICE Trans. Commun.

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The fifth generation mobile communication system (5G) is designed to have new radio capabilities to support not only conventional enhanced Mobile Broadband (eMBB) communications but also new machine type communications such as Ultra-Reliable Low-Latency communications (URLLC) and massive Machine Type communications (m-MTC). In such new areas of URLLC and m-MTC, mobile operators need to explore new use cases and/or applications together with vertical industries, the industries which are potential users of 5G, in order to fully exploit the new 5G capabilities. Intelligent Transport System (ITS), including automated driving, is one of the most promising application areas of 5G since it requires both ultra-reliable and low-latency communications. We are actively working on the research and development of truck platooning as a new 5G application. We have developed a field trial system for vehicular-to-network (V2N) communications using 5G prototype equipment and actual largesize trucks in order to assess 5G capabilities, including ultra-low-latency, in automotive test courses in the field. This paper discusses the fundamental performance evaluation required for vehicular communications between platooning trucks, such as low-latency message communication for vehicle control and low-latency video monitoring of following platooning truck vehicles. The paper also addresses the field evaluation results of 5G V2N communications in a rural area. It clarifies the fundamental radio propagation issues at the leading and the following vehicles in truck platooning for V2N communications, and discusses the impact of the radio propagation over a road to the over-the-air transmission performance of 5G V2N communications.

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Development of Wireless Access and Flexible Networking Technologies for 5G Cellular Systems

Cited by 4 publications

References 22 publications

A Review of Quality of Service in Fog Computing for the Internet of Things

A Review of Quality of Service in Fog Computing for the Internet of Things

Smart Vehicle Driving Behavior Analysis Based on 5G, IoT and Edge Computing Technologies

Field Trial on 5G Low Latency Radio Communication System Towards Application to Truck Platooning

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