Cost-Reward-based Carrier Aggregation with Differentiating Network Slicing for Optimizing Radio RB Allocation in 5G New Radio Network

Chang, Ben-Jye; Chang, Wei-Teng

doi:10.1109/iemcon.2019.8936296

Cited by 11 publications

(3 citation statements)

References 23 publications

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“…The tremendous interest and developments of mobile broadband Internet networks, undoubtedly lead to intensive research works towards advanced mobile and cloud computing algorithms and frameworks for high level of QoS provisioning in each core and access network. At the first place, the main motivation for our proposed intelligent multiaccess QoS provisioning framework could be found in [7][8][9][10][11]. Device-centric multi-RAT architectures, native support of machine-to-machine communications and smarter devices are part of the main features of 5G [12].…”

Section: Mec Fundamentals and Requirementsmentioning

confidence: 99%

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A MOBILE EDGE COMPUTING SERVICES WITH QoS SUPPORT MODEL FOR NEXT GENERATION MOBILE NETWORKS

Šuminoski,

Veličkovska,

Janevski

2022

JEEIT

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This paper presents a novel overview in intelligent multi-access QoS mobile edge computing (MEC) for beyond 5G networks and services. There are many challenges faced by the expansion of Cloud networks and Mobile networks, which can be solved by providing connectivity at the edge of the network, i.e. with Mobile Edge Computing networks. The MEC improves overall network performance and reduces end-to-end service delay. Also, the improved advanced QoS model including Machine Learning (ML) algorithm within for next generation of mobile networks and services are proposed. The purpose of the ML algorithm is to understand the traffic activity and determine how the traffic schedule should be made. Given a set of machines and a set of jobs, the model should compute the processing schedule that minimizes specified metrics. The proposed model combines the most powerful features of both Cloud and Edge computing, independent from any existing and future Radio Access Technology, leading to possible better performance utility networks, lower service delay with high QoS provisioning for many used multimedia service. Finally, this paper gives an overview of the existing Mobile Edge Computing technologies and several existing use cases. Undoubtedly, MEC with QoS support is an innovative network paradigm going in 6G, which can essentially answer many of the existing Mobile Networks' challenges.

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Section: Mec Fundamentals and Requirementsmentioning

confidence: 99%

“…Инф. Технол, 7 (1) 23 -31 (2022) IP networks [10,11]. Those papers were introducting a framework adaptive QoS provisioning model that provides the best QoS and lower cost for a given multimedia service by using one or more radio access technologies (RATs) at a given time.…”

Section: Mec Fundamentals and Requirementsmentioning

confidence: 99%

A MOBILE EDGE COMPUTING SERVICES WITH QoS SUPPORT MODEL FOR NEXT GENERATION MOBILE NETWORKS

Šuminoski,

Veličkovska,

Janevski

2022

JEEIT

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“…Works such as [15]- [18] focus on secondary cell selection which is mainly based on channel quality and cell load. Different scheduling approaches to guarantee quality of service (QoS) have been proposed in [7], [19], [20]. The joint optimization of secondary cell selection with resource allocation is addressed by authors in [21], [22].…”

Section: A Related Workmentioning

confidence: 99%

Proactive Dual Connectivity for Automated Guided Vehicles in Outdoor Industrial Environment

et al. 2022

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5G communication systems are one of the major enabling technologies to meet the needs of Industry 4.0. This paper focuses on the use case of automated guided vehicles (AGVs) in an outdoor industrial scenario. To meet the communication requirements in these type of use cases, dual connectivity (DC) with resource aggregation in the uplink (UL) is generally proposed. However, uncontrolled use of DC schemes may negatively affect the network causing effects such as reduced network capacity, increased signaling, and increased interference. To overcome these issues, this paper proposes and evaluates the use of a proactive DC activation algorithm based on the instantaneous quality of service (QoS) and network conditions. The proposed algorithm has two phases, a first phase in which the QoS prediction is performed, and a second phase in which the DC activation decision is made. The performance evaluation of the algorithm has been carried out in two different scenarios: a single-frequency (SF) network and a dual-frequency (DF) network; and compared to two baselines. Our results show that our predictive DC algorithm is sufficiently robust and can offer benefits in terms of reduced signaling and increased UL performance, especially in scenarios with low to medium traffic load.INDEX TERMS Automated guided vehicles, dual connectivity, industry 4.0, proactive network management, quality of service, uplink.

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Adaptive Cost-Reward Scheduling for Optimizing Radio Utilization and NR Numerology Efficiency in B5G New Radio Networking

Chang,

Chang

2024

Lecture Notes on Data Engineering and Communications Technologies

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Cost-Reward-based Carrier Aggregation with Differentiating Network Slicing for Optimizing Radio RB Allocation in 5G New Radio Network

Cited by 11 publications

References 23 publications

A MOBILE EDGE COMPUTING SERVICES WITH QoS SUPPORT MODEL FOR NEXT GENERATION MOBILE NETWORKS

A MOBILE EDGE COMPUTING SERVICES WITH QoS SUPPORT MODEL FOR NEXT GENERATION MOBILE NETWORKS

Proactive Dual Connectivity for Automated Guided Vehicles in Outdoor Industrial Environment

Adaptive Cost-Reward Scheduling for Optimizing Radio Utilization and NR Numerology Efficiency in B5G New Radio Networking

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