🏛️國立陽明交通大學機構典藏

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Applying Big Data, Machine Learning, and SDN/NFV to 5G Traffic Clustering, Forecasting, and Management
(2018-01-01)
Luong-Vy Le
;
Sinh, Do
;
Lin, Bao-Shuh Paul
;
Tung, Li-Ping
;
資訊工程學系
Traffic clustering, forecasting, and management play a crucial role in improving network efficiency, network quality, load balancing (LB), and energy saving of mobile networks. Especially, in 5G networks, a dense heterogeneous architecture of various types of cells (macro cells and small cells) make traffic management become more complicated. Moreover, investigating and understanding traffic patterns of a huge number of cells are challenging issues, but valuable for network operators. On the other hand, big data, machine learning (ML), software defined network (SDN), and network functions virtualization (NFV) have recently been proposed as emerging technologies and the necessary tools for empowering the SON of 5G to address the intensive computation and optimization issues. In this study, the authors applied those technologies to build a practical and powerful framework for clustering, forecasting, and managing traffic behaviors for a huge number of base stations with different statistical traffic characteristics of different types of cells (GSM, 3G, 4G). Besides, several applications based on traffic forecasting were also introduced. Finally, the performance of the proposed models was evaluated by applying them to a real dataset that collected traffic KPIs (key performance indicators) of more than 6000 cells of a real network during the years, 2016 and 2017.
SDN/NFV, Machine Learning, and Big Data Driven Network Slicing for 5G
(2018-01-01)
Le, Luong-Vy
;
Lin, Bao-Shuh Paul
;
Tung, Li-Ping
;
Sinh, Do
;
資訊工程學系
5G networks are expected to be able to satisfy a variety of vertical services for mobile users, business demands, and automotive industry. Network slicing is a promising technology for 5G to provide a network as a service (NaaS) for a wide range of services that run on different virtual networks deployed on a shared network infrastructure. Moreover, the SON (self-organizing network) in 5G is expected as a significant evolution to guarantee for full intelligence, automatic, and faster management and optimization. To deal with those requirements, recently, software-defined networking (SDN), network functions virtualization (NFV), big data, and machine learning have been proposed as emerging technologies and the necessary tools for 5G, especially, for network slicing. This study aims to integrate various machine learning (ML) algorithms, big data, SDN, and NFV to build a comprehensive architecture and an experimental framework for the future SONs and network slicing. Finally, based on this framework, we successfully implemented an early state traffic classification and network slicing for mobile broadband traffic applications implemented at Broadband Mobile Lab (BML), National Chiao Tung University (NCTU).
Big Data and Machine Learning Driven Handover Management and Forecasting
(2017-01-01)
Vy, Le Luong
;
Tung, Li-Ping
;
Lin, Bao-Shuh Paul
;
材料科學與工程學系
;
資訊工程學系
Handover (HO), as a key aspect of mobility management, plays an important role in improving network quality and mobility performance in mobile networks. Especially, in 5G networks, heterogeneous networks (HetNets) deployment of macro cells and small cells, and the deployment of ultra-dense networks (UDNs) make HO management become more challenging. Besides, the understanding of HO behavior in a cell is quite limited in existing studies, thus the forecasting HO for an individual cell is complicated, even impossible. This challenge led the authors to propose a practical process for managing and forecasting HO for a huge number of cells, based on machine-learning (ML) algorithms and big data. Moreover, based on HO forecasting, the authors also propose an approach to detect any abnormal HO in cells. The performance of the proposed approaches was evaluated by applying it to a real dataset that collected HO KPI of more than 6000 cells of a real network during the years, 2016 and 2017. The results show that the study was successful in identifying, separating HO behavior, forecasting the future number of HO attempts, and detecting abnormal HO behaviors of cells.
A Practical Model for Traffic Forecasting based on Big Data, Machine-learning, and Network KPIs
(2018-01-01)
Le, Luong-Vy
;
Sinh, Do
;
Tung, Li-Ping
;
Lin, Bao-Shuh Paul
;
資訊工程學系
Traffic forecasting plays an important role in improving network quality and energy saving of mobile networks. In 5G, traffic forecasting directly influences the self-organizing network (SON) in managing and controlling the network effectively. Especially, long-term traffic forecasting can provide a detailed pattern of future traffic, besides permitting more time for planning and optimizing. Most of the traffic forecasting models used the history of traffic, while the utilization of another network KPIs (key performance indicators) for traffic forecasting is limited. Therefore, the authors propose here a practical platform and process for traffic forecasting, based on big data, machine-learning (ML), and network KPIs that are flexible to forecast accurately different statistical traffic characteristics of different types of cells (GSM, 3G, 4G) for both long-and short-term forecasting. The performance of the proposed model was evaluated by applying it to a real dataset that collected KPIs of more than 6000 cells of a real network during the years, 2016 and 2017

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🏛️國立陽明交通大學機構典藏

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