• Journal of Digital Convergence
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• v.17 no.12
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• pp.197-202
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• 2019

In line with the era of the 4th Industrial Revolution, 5G technology has become common technology, and 5G technology is evaluated as a technology that minimizes the speed and response speed compared to 4G using technologies such as network slicing and ultra-multiple access. 5G NR stands for 5G mobile communication standard, and network slicing cuts the network into parallel connections to optimize the network. In addition, the risk of hacking is increasing as data is processed in the base station unit. In addition, since the number of accessible devices per unit area increases exponentially, there is a possibility of base station attack after hacking a large number of devices in the unit area. To solve this problem, this study proposes the introduction of quantum cryptography and 5G security standardization.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.549-550
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• 2020

최근 등장한 자율주행, 스마트팩토리 및 IoT 등 다양한 기술은 기존 4G 네트워크를 활용하기에 부적합한 사항이 많았다. 따라서 5G 네트워크가 등장하였으며 네트워크 슬라이싱 기술을 통해 다양한 서비스에 각각의 네트워크 환경을 구성하여 제공하였다. 그러나 같은 네트워크 환경인 슬라이스 내에서도 특징이 다른 트래픽이 발생할 수 있으며 서비스의 종류로 고정된 슬라이스의 네트워크 환경은 트래픽 처리시간 증가 및 응답시간 증가를 유발할 수 있다. 따라서 본 논문에서는 트래픽의 특성을 고려하여 클러스터링을 시행하여 자동으로 네트워크 슬라이스를 관리하는 시스템을 제안한다.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.101-112
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• 2022

In this paper, we analyze 5G network slicing and propose an efficient network slice configuration method in 5G mobile networks. Network slicing can be identified and performed based on the network slice instance information in 5G mobile networks. In case of discordance between the UE's network slice instance information and the network's one, the unnecessary signalling overhead occurs, when the UE's PDU Session Establishment request to the network fails. To solve this problem, this paper proposes two efficient network slice configuration methods, the UE-based ENSC(Efficient Network Slice Configuration) method and the Network-based ENSC method. The proposed schemes perform the prompt the configuration and provision of the updated network slice instance information between the UE and network and improve battery and resource efficiency and minimize unnecessary signalling overhead compared to existing methods in 5G mobile networks.

• 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.

• Electronics and Telecommunications Trends
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• v.34 no.2
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• pp.92-100
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• 2019

Vast amounts of different service-specific requirements and vertical network slicing in a 5G network increase the complexity, cost of the network management and resource operations for carriers. To solve this problem, 3GPP is working on the standardization of NWDAF to support the automation of the 5G network by utilizing artificial intelligence technologies based on Big Data to improve the efficiency of network management and resource operation. In addition, the ETSI ZSM Industry Specification Group is developing technical standards for the automation of end-to-end network management and service delivery. This document provides an overall survey of the latest standardization issues of the NWDAF in 3GPP and ETSI ZSM for 5G network automation and intelligence.

• ETRI Journal
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• v.27 no.3
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• pp.334-336
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• 2005

We improved the performance of the spectrum-sliced light source for wavelength-division-multiplexed passive optical networks by employing a Fabry-Perot laser diode(FP-LD). We found that the FP-LDs can suppress the intensity noise as significantly as using a gain-saturated semiconductor optical amplifier. The transmission characteristics were measured and analyzed in both conditions with and without employing an FP-LD.

• Journal of Information Processing Systems
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• v.16 no.4
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• pp.896-914
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• 2020

With the rapid growth of network traffic, a large number of connected devices, and higher application services, the traditional network is facing several challenges. In addition to improving the current network architecture and hardware specifications, effective resource management means the development trend of 5G. Although many existing potential technologies have been proposed to solve the some of 5G challenges, such as multiple-input multiple-output (MIMO), software-defined networking (SDN), network functions virtualization (NFV), edge computing, millimeter-wave, etc., research studies in 5G continue to enrich its function and move toward B5G mobile networks. In this paper, focusing on the resource allocation issues of 5G core networks and radio access networks, we address the latest technological developments and discuss the current challenges for resource management in 5G.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.4
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• pp.515-521
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• 2020

Recently, data traffic has exploded due to development of various industries, which causes problems about losing of efficiency and overloaded existing networks. To solve these problems, network slicing, which uses a virtualization technology and provides a network optimized for various services, has received a lot of attention. In this paper, we propose a resource allocation method using credit value. In the method using the clustering technology, an operation for selecting a cluster is performed whenever an allocation request for various services occurs. On the other hand, in the proposed method, the credit value is set by using the residual capacity and balancing so that the slice request can be processed without performing the operation required for cluster selection. To prove proposed method, we perform processing time and balancing simulation. As a result, the processing time and the error factor of the proposed method are reduced by about 13.72% and about 7.96% compared with the clustering method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.3 s.174
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• pp.769-776
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• 2000

RP(Rapid Prototyping) technology is gaining its popularity in building a prototype in all industries. SLS(Slective Laser Sintering) is one of RP technologies, which is focused on tooling processes as well as three dimension solid model. There are several factors, the length and the cross-sectional area of a part, that have an effect on build setup in SLS process. In this paper, the computation on geometrical relationship is used to slice STL file and to estimate these factors. Based on these values, the build setup parameters such as the heating temperature, the laser power, and the powder cartridge feed rate are determined by neural network approaches. The test results show that the computation time is saved and the neural network approach is able to apply to get the optimal parameters of build process within an acceptable error rate.

• Journal of the Korean Society of Systems Engineering
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• v.1 no.2
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• pp.26-33
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• 2005

In this paper, an algorithm is presented to recognize road based on unpaved test courses image. The road images obtained by a video camera undergoes a pre-processing that includes filtering, gray level slicing, masking and identification of unpaved test courses. After this pre-processing, a part of image is grouped into 27 sub-windows and fed into a three-layer feed-forward neural network. The neural network is trained to indicate the road direction. The proposed algorithm has been tested with the images different from the training images, and demonstrated its efficacy for recognizing unpaved road. Based on the test results, it can be said that the algorithm successfully combines the traditional image processing and the neural network principles towards a simpler and more efficient driver warning or assistance system.