• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2573-2589
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• 2023

As 5G mobile systems carry multiple services and applications, numerous user, and application types with varying quality of service requirements inside a single physical network infrastructure are the primary problem in constructing 5G networks. Radio Access Network (RAN) slicing is introduced as a way to solve these challenges. This research focuses on optimizing RAN slices within a singular physical cell for vehicle-to-everything (V2X) and enhanced mobile broadband (eMBB) UEs, highlighting the importance of adept resource management and allocation for the evolving landscape of 5G services. We put forth two unique strategies: one being offline network slicing, also referred to as standard network slicing, and the other being Online reinforcement learning (RL) network slicing. Both strategies aim to maximize network efficiency by gathering network model characteristics and augmenting radio resources for eMBB and V2X UEs. When compared to traditional network slicing, RL network slicing shows greater performance in the allocation and utilization of UE resources. These steps are taken to adapt to fluctuating traffic loads using RL strategies, with the ultimate objective of bolstering the efficiency of generic 5G services.

• Review of KIISC
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• v.30 no.6
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• pp.17-22
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• 2020

5G 단독모드(StandAlone)가 미국, 중국 등에 상용화됨에 따라 고속 서비스 eMBB(enhanced Mobile BroadBand), 저지연 서비스 URLLC(Ultra Reliable Low Latency Communication), 다 연결 서비스 mMTC(massive Machine-Type Communications) 기술 또한 곧 상용 기술화 될 것으로 예상된다. 이러한 5G 서비스의 기술 개발에는 다양한 기업과 기관들이 참여하여 매년 신규 표준이 생성되고 있다. 따라서 본 논문에서는 5G 보안 표준화에 대한 현황을 파악하여 전반적인 5G 보안 동향에 대한 정보를 제공하고자 한다.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.87-96
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• 2021

The demand for e-learning through video lectures is rapidly increasing due to its diverse advantages over the traditional learning methods. This led to massive volumes of web-based lecture videos. Indexing and retrieval of a lecture video or a lecture video topic has thus proved to be an exceptionally challenging problem. Many techniques listed by literature were either visual or audio based, but not both. Since the effects of both the visual and audio components are equally important for the content-based indexing and retrieval, the current work is focused on both these components. A framework for automatic topic-based indexing and search depending on the innate content of the lecture videos is presented. The text from the slides is extracted using the proposed Merged Bounding Box (MBB) text detector. The audio component text extraction is done using Google Speech Recognition (GSR) technology. This hybrid approach generates the indexing keywords from the merged transcripts of both the video and audio component extractors. The search within the indexed documents is optimized based on the Naïve Bayes (NB) Classification and K-Means Clustering models. This optimized search retrieves results by searching only the relevant document cluster in the predefined categories and not the whole lecture video corpus. The work is carried out on the dataset generated by assigning categories to the lecture video transcripts gathered from e-learning portals. The performance of search is assessed based on the accuracy and time taken. Further the improved accuracy of the proposed indexing technique is compared with the accepted chain indexing technique.

• Electronics and Telecommunications Trends
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• v.35 no.6
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• pp.129-136
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• 2020

Korea launched the world's first commercial 5G services in April 2019. Mobile traffic is expected to increase further with the acceleration of mobile-centric data utilization. It is one of the most important indexes of the growth of the mobile communications market, and it has a close relationship with frequency demand and supply, network management, and information communication policy. To overcome the limitations of an analytical solution due to the high complexity of the real world, this paper estimates the diffusion of 5G users using systemic thinking and the behavior of individual agents. Based on these demand forecasts, contributions to the establishment of strategic policies are suggested. For better understanding, global 5G predictions of subscribers and mobile traffic are also compared.

• Electronics and Telecommunications Trends
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• v.36 no.4
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• pp.13-22
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• 2021

Among the activities of 3GPP for operating 5G-based unmanned aerial vehicles, we introduce several use cases of UAVs in 5G mobile communication such as radio access node onboard UAV, simultaneous support data transmission for UAVs and eMBB users, autonomous UAVs controlled by AI, isolated deployment of radio access through UAV, and separation of UAV service area. From this, we further summarize 5G mobile communication requirements for UAVs, including definition and operational criteria of UAS, UAS remote identification requirements, UAS usage requirements, and performance requirements. Finally, regarding 5G mobile communication-based UAS connectivity, identification and tracking support, we discuss the 3GPP UAV architecture, seven major problems, the proposed solutions to each problem, and propose the results for future specification work.

• Smart Media Journal
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• v.13 no.4
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• pp.33-48
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• 2024

With the advent of 5G, characterized by Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Machine Type Communications (mMTC), efficient network management and service provision are becoming increasingly critical. This paper proposes a novel approach to address key challenges of 5G networks, namely ultra-high speed, ultra-low latency, and ultra-reliability, while dynamically optimizing network slicing and resource allocation using machine learning (ML) and deep learning (DL) techniques. The proposed methodology utilizes prediction models for network traffic and resource allocation, and employs Federated Learning (FL) techniques to simultaneously optimize network bandwidth, latency, and enhance privacy and security. Specifically, this paper extensively covers the implementation methods of various algorithms and models such as Random Forest and LSTM, thereby presenting methodologies for the automation and intelligence of 5G network operations. Finally, the performance enhancement effects achievable by applying ML and DL to 5G networks are validated through performance evaluation and analysis, and solutions for network slicing and resource management optimization are proposed for various industrial applications.

• Journal of Korean Society for Quality Management
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• v.45 no.1
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• pp.1-10
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• 2017

Purpose: Most enterprises adopt six sigma acts to maximize the business performance with raising the executiveness for the project improvements in each parts. But there are little studies about six sigma actual operations in manufacturing whether the six sigma improvements that have injected a lot of budget, efforts, labours and time are run properly. Methods: This study select 31 interviewees who have MBB or BB from 5 large enterprises running six sigma over 10 years and 5 SMEs running six sigma over 5 years to understand and review the operational status of six sigma actions in manufacturing industry and to secure representativeness. This study identify the operational status of six sigma actions and key factors enhancing or impeding execution of six sigma projects through face-to-face interviews and online surveys by e-mail. Results: This study figured out the operational status of six sigma actions and key factors enhancing or impeding execution of six sigma projects. We used SPSS 16.0 for the reliability and the validity of survey data. Conclusion: There can be a lot of different factors that affect six sigma project improvements besides the key factors from this study. More study need to think of the organization characteristics and the basic conditions and to remedy the unreasonable points and defects rather than following foreign companies and enterprises.