QoS-Forecasting-Based Intelligent Flow-Control Scheme for Multi-Connectivity in 5G Heterogeneous Networks
In the fifth-generation (5G) wireless-network system, the convergence of multiple networks of different standards as well as that of high- and low-frequency networks exists since a long time. Owing to the inability of 5G networks to predict the user quality of service (QoS) accurately, these network...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9495797/ |
| _version_ | 1818652455022362624 |
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| author | Xinran Ba |
| author_facet | Xinran Ba |
| author_sort | Xinran Ba |
| collection | DOAJ |
| description | In the fifth-generation (5G) wireless-network system, the convergence of multiple networks of different standards as well as that of high- and low-frequency networks exists since a long time. Owing to the inability of 5G networks to predict the user quality of service (QoS) accurately, these networks are prone to issues such as access congestion, low QoS, and frequent congestion in one network while other network resources remain idle. Therefore, 5G networks fail to meet the QoS requirements and also prevent effective resource utilization. The deployment of multi-connectivity technologies can facilitate the optimization of the multinetwork convergence-system architecture. However, such technologies are faced with several challenges. Existing literature mainly focuses on the development of a multi-connectivity flow-control scheme to determine the secondary nodes (SNs) to which the master node (MN) should distribute data. This paper presents a three-step, QoS-forecasting, intelligent flow-control scheme, wherein the user equipment (UE) determines the data-flow direction based on the network characteristics and load handled by each node. Subsequently, the MN determines the initial user priority based on load balancing, user characteristics, and fairness. Finally, the MN allocates data to each SN in accordance with the QoS and average transmission capability of UE. The simulation results reveal that the proposed algorithm improves the system throughput significantly compared to the single connectivity and traditional fixed-data-split methods. Furthermore, the proposed method facilitates transmission-delay reduction, radio-link failure-probability control, and improved system robustness. |
| first_indexed | 2024-12-17T02:22:16Z |
| format | Article |
| id | doaj.art-b1aca9d3d873472a837d7fd21b76b6b9 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T02:22:16Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b1aca9d3d873472a837d7fd21b76b6b92022-12-21T22:07:14ZengIEEEIEEE Access2169-35362021-01-01910430410431510.1109/ACCESS.2021.30998249495797QoS-Forecasting-Based Intelligent Flow-Control Scheme for Multi-Connectivity in 5G Heterogeneous NetworksXinran Ba0https://orcid.org/0000-0003-4473-2548State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, ChinaIn the fifth-generation (5G) wireless-network system, the convergence of multiple networks of different standards as well as that of high- and low-frequency networks exists since a long time. Owing to the inability of 5G networks to predict the user quality of service (QoS) accurately, these networks are prone to issues such as access congestion, low QoS, and frequent congestion in one network while other network resources remain idle. Therefore, 5G networks fail to meet the QoS requirements and also prevent effective resource utilization. The deployment of multi-connectivity technologies can facilitate the optimization of the multinetwork convergence-system architecture. However, such technologies are faced with several challenges. Existing literature mainly focuses on the development of a multi-connectivity flow-control scheme to determine the secondary nodes (SNs) to which the master node (MN) should distribute data. This paper presents a three-step, QoS-forecasting, intelligent flow-control scheme, wherein the user equipment (UE) determines the data-flow direction based on the network characteristics and load handled by each node. Subsequently, the MN determines the initial user priority based on load balancing, user characteristics, and fairness. Finally, the MN allocates data to each SN in accordance with the QoS and average transmission capability of UE. The simulation results reveal that the proposed algorithm improves the system throughput significantly compared to the single connectivity and traditional fixed-data-split methods. Furthermore, the proposed method facilitates transmission-delay reduction, radio-link failure-probability control, and improved system robustness.https://ieeexplore.ieee.org/document/9495797/Intelligent flow controlmulti-connectivityQoS forecastinguser-centric |
| spellingShingle | Xinran Ba QoS-Forecasting-Based Intelligent Flow-Control Scheme for Multi-Connectivity in 5G Heterogeneous Networks IEEE Access Intelligent flow control multi-connectivity QoS forecasting user-centric |
| title | QoS-Forecasting-Based Intelligent Flow-Control Scheme for Multi-Connectivity in 5G Heterogeneous Networks |
| title_full | QoS-Forecasting-Based Intelligent Flow-Control Scheme for Multi-Connectivity in 5G Heterogeneous Networks |
| title_fullStr | QoS-Forecasting-Based Intelligent Flow-Control Scheme for Multi-Connectivity in 5G Heterogeneous Networks |
| title_full_unstemmed | QoS-Forecasting-Based Intelligent Flow-Control Scheme for Multi-Connectivity in 5G Heterogeneous Networks |
| title_short | QoS-Forecasting-Based Intelligent Flow-Control Scheme for Multi-Connectivity in 5G Heterogeneous Networks |
| title_sort | qos forecasting based intelligent flow control scheme for multi connectivity in 5g heterogeneous networks |
| topic | Intelligent flow control multi-connectivity QoS forecasting user-centric |
| url | https://ieeexplore.ieee.org/document/9495797/ |
| work_keys_str_mv | AT xinranba qosforecastingbasedintelligentflowcontrolschemeformulticonnectivityin5gheterogeneousnetworks |