• Title/Summary/Keyword: network slicing

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Standardization Trends in Network Slicing and Management Technologies of 5G Core Network (5G 네트워크 슬라이싱 및 네트워크 관리 기술 표준화 동향)

  • Lee, S.I.;Lee, J.H.;Shin, M.K.
    • Electronics and Telecommunications Trends
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    • v.32 no.2
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    • pp.62-70
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    • 2017
  • 5G 네트워크 기술은 4G LTE 이동 통신 기술의 후속 기술로서, ITU-R, ITU-T, NGMN, 3GPP 등의 표준화 그룹을 중심으로 고성능, 저지연, 고가용성 등의 특성을 가지는 새로운 Clean-slate 형태의 이동 통신 시스템 및 네트워크 구조를 설계 중이다. 특히 다양한 5G 융합 서비스를 효율적으로 제공하기 위해 서비스 및 네트워크 자원의 독립성 및 유연성을 지향하는 네트워크 슬라이싱을 적용하고, ETSI NFV 네트워크 기능 가상화 기술을 포함하는 네트워크 관리 구조를 도입하고자 한다. 본고에서는 5G 네트워크 슬라이싱 기술 및 5G 네트워크 관리 기술의 개념 및 요구사항을 분석하고, 이에 대해 3GPP SA WG2 및 SA WG5에서 진행 중인 표준화 현황을 소개한다.

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금속조형법을 위한 실시간 형상 모델링과 VRML 응용에 관한 연구

  • 정영대;최홍태;이석희
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 1997.04a
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    • pp.321-326
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    • 1997
  • This paper present how VRML file format can be used for RP Technology. VRML standards provids compact and powerful interface between remote RP manufacturer in network independent environment. We have constructed integrated and network-connected server system which can share the CAD data and varios process which is STL-to-VRML translater,slicing process,slice anchor process etc. This Server system consisted in file converter between STL and VRML,CGI system which sends a generated data to VRML client or browser, slice-generator which can re-slice at varied thickness and simulator which can show and check simultaneously status between near slices with support. This system aims to the integrated simulator which supports graphic animator and FEA analysis system.

Machine Learning-based Network Slicing Resource Reservation Scheme in 5G Network (5G 네트워크에서 기계학습 기반 트래픽 예측을 통한 네트워크 슬라이싱 자원 예약 기법)

  • Lee, Pil-Won;Lee, A-Reum;Park, Soo-Yong;Shin, Yong-Tae
    • Annual Conference of KIPS
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    • 2020.05a
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    • pp.56-59
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    • 2020
  • 최근 초저지연, 초고속, 초연결 네트워크를 요구하는 기술들이 급속하게 발전하고 있다. 기존 4G 네트워크는 위 요구사항을 만족할 수 없었기 때문에 5G 네트워크가 등장했다. 5G 네트워크는 네트워크 가상화 기반 네트워크 슬라이싱을 통해 각각의 서비스 마다 독립적인 네트워크 환경을 제공한다. 그러나 네트워크에 참여하는 서비스가 다양해질수록 트래픽 부하가 폭발적으로 증가할 것으로 예상되며 트래픽 부하에 따른 병목현상이 발생할 가능성이 여전히 존재한다. 본 논문에서는 인공 신경망 알고리즘 RNN을 활용하여 트래픽을 예측하고 예측 결과를 기반으로 네트워크 슬라이스의 자원을 선제적으로 조절하는 기계학습 기반 네트워크 슬라이싱 자원 예약 기법을 제안한다.

A Study on the Analysis of Security Requirements through Literature Review of Threat Factors of 5G Mobile Communication

  • DongGyun Chu;Jinho Yoo
    • Journal of Information Processing Systems
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    • v.20 no.1
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    • pp.38-52
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    • 2024
  • The 5G is the 5th generation mobile network that provides enhanced mobile broadband, ultra-reliable & low latency communications, and massive machine-type communications. New services can be provided through multi-access edge computing, network function virtualization, and network slicing, which are key technologies in 5G mobile communication. However, these new technologies provide new attack paths and threats. In this paper, we analyzed the overall threats of 5G mobile communication through a literature review. First, defines 5G mobile communication, analyzes its features and technology architecture, and summarizes possible security issues. Addition, it presents security threats from the perspective of user devices, radio access network, multi-access edge computing, and core networks that constitute 5G mobile communication. After that, security requirements for threat factors were derived through literature analysis. The purpose of this study is to conduct a fundamental analysis to examine and assess the overall threat factors associated with 5G mobile communication. Through this, it will be possible to protect the information and assets of individuals and organizations that use 5G mobile communication technology, respond to various threat situations, and increase the overall level of 5G security.

Network Slice Selection Function on M-CORD (M-CORD 기반의 네트워크 슬라이스 선택 기능)

  • Rivera, Javier Diaz;Khan, Talha Ahmed;Asif, Mehmood;Song, Wang-Cheol
    • KNOM Review
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    • v.21 no.2
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    • pp.35-45
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    • 2018
  • As Network Slicing functionality gets applied to mobile networking, a mechanism that enables the selection of network slices becomes indispensable. Following the 3GPP Technical Specification for the 5G Architecture, the inclusion of the Network Slice Selection Function (NSSF) in order to leverage the process of slice selection is apparent. However, actual implementation of this network function needs to deal with the dynamic changes of network instances, due to this, a platform that supports the orchestration of Virtual Network Functions (VNF) is required. Our proposed solution include the use of the Central Office Rearchitected as a Data Center (CORD) platform, with the specified profile for mobile networks (M-CORD) that integrates a service orchestrator (XOS) alongside solutions oriented to Software Defined Networking (SDN), Network Function Virtualization (VNF) and virtual machine management through OpenStack, in order to provide the right ecosystem where our implementation of NSSF can obtain slice information dynamically by relying on synchronization between back-end services and network function instances.

Graph Neural Network and Reinforcement Learning based Optimal VNE Method in 5G and B5G Networks (5G 및 B5G 네트워크에서 그래프 신경망 및 강화학습 기반 최적의 VNE 기법)

  • Seok-Woo Park;Kang-Hyun Moon;Kyung-Taek Chung;In-Ho Ra
    • Smart Media Journal
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    • v.12 no.11
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    • pp.113-124
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    • 2023
  • With the advent of 5G and B5G (Beyond 5G) networks, network virtualization technology that can overcome the limitations of existing networks is attracting attention. The purpose of network virtualization is to provide solutions for efficient network resource utilization and various services. Existing heuristic-based VNE (Virtual Network Embedding) techniques have been studied, but the flexibility is limited. Therefore, in this paper, we propose a GNN-based network slicing classification scheme to meet various service requirements and a RL-based VNE scheme for optimal resource allocation. The proposed method performs optimal VNE using an Actor-Critic network. Finally, to evaluate the performance of the proposed technique, we compare it with Node Rank, MCST-VNE, and GCN-VNE techniques. Through performance analysis, it was shown that the GNN and RL-based VNE techniques are better than the existing techniques in terms of acceptance rate and resource efficiency.

Rank-weighted reconstruction feature for a robust deep neural network-based acoustic model

  • Chung, Hoon;Park, Jeon Gue;Jung, Ho-Young
    • ETRI Journal
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    • v.41 no.2
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    • pp.235-241
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    • 2019
  • In this paper, we propose a rank-weighted reconstruction feature to improve the robustness of a feed-forward deep neural network (FFDNN)-based acoustic model. In the FFDNN-based acoustic model, an input feature is constructed by vectorizing a submatrix that is created by slicing the feature vectors of frames within a context window. In this type of feature construction, the appropriate context window size is important because it determines the amount of trivial or discriminative information, such as redundancy, or temporal context of the input features. However, we ascertained whether a single parameter is sufficiently able to control the quantity of information. Therefore, we investigated the input feature construction from the perspectives of rank and nullity, and proposed a rank-weighted reconstruction feature herein, that allows for the retention of speech information components and the reduction in trivial components. The proposed method was evaluated in the TIMIT phone recognition and Wall Street Journal (WSJ) domains. The proposed method reduced the phone error rate of the TIMIT domain from 18.4% to 18.0%, and the word error rate of the WSJ domain from 4.70% to 4.43%.

RP Preprocessor Based on Distributed Objects (분산객체를 응용한 RP Preprocessor의 기능 구현)

  • 지해성;이승원
    • Journal of the Korean Society for Precision Engineering
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    • v.20 no.2
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    • pp.120-128
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    • 2003
  • When considering the use of rapid prototyping (RP), there are many issues a designer has to address for handling an STL model, the de facto standard fur RP. Today designers can skip all these issues by visiting web-based service bureaus that readily supply needed information for the RP services. Since orders are taken for RP parts through the web page of service providers designers are now asked to upload their STL files to the company server either by direct upload, ftp file transfer, or as an e-mail attachment. If the service bureau, however, fixes or edits an STL filceto optimize the RP process but neglects to tell its customer about the rework in detail, it may cause problems down the line in processing of the original CAD data for other applications. In this paper, we propose a framework for a collaborative virtual environment between CAD designers and RP processes on the internet which directly provides designers with an advanced preprocessor functionality, design visualization, as well as model display, repair, and slicing over the network. This can help smooth data transfer from CAD to RP process with minimum inconsistency in CAD.

서비스 융합 네트워크를 위한 5G의 보안 전략: EAP 인증 프레임워크

  • Yun, Keon;Park, Hoon Yong;You, Ilsun
    • Review of KIISC
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    • v.29 no.5
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    • pp.51-61
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    • 2019
  • 보안은 5G 이동통신 네트워크의 성공적인 정착을 위한 필요충분조건이다. 5G 보안의 중요한 표준으로 3GPP (3rd Generation Partnership Project)의 보안 담당 워킹그룹인 SA3는 3GPP 5G 보안구조를 제시하였다. 특히, 3GPP 5G 보안구조는 Extensible Authentication Protocol (EAP) 인증 프레임워크를 채택함으로써 이기종의 다양한 인증 기법과 자격증명을 용이하게 포용할 수 있는 유연성을 갖추었다. 서비스 융합 네트워크를 지향하는 5G의 비전을 고려할 때, EAP 인증 프레임워크는 보안측면에 있어서 매우 중요한 전략이라고 볼 수 있다. 따라서, 본 논문에서는 3GPP 5G 보안구조를 위한 EAP 인증 프레임워크를 고찰한다. 이를 위해, 1차 인증을 위한 EAP 기반의 인증 프로토콜 EAP-AKA'을 면밀히 검토하면서 1차 인증에서의 EAP 인증 프레임워크 적용방안을 분석한다. 아울러, 2차 인증을 위한 EAP 인증 프레임워크의 적용과 네트워크 슬라이싱 (Network Slicing)과의 연동을 살펴본다.

Improved Adapting a Single Network to Multiple Tasks By Bit Plane Slicing and Dithering (향상된 비트 평면 분할을 통한 다중 학습 통합 신경망 구축)

  • Bae, Joon-ki;Bae, Sung-ho
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • 2020.07a
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    • pp.643-646
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    • 2020
  • 본 논문에서는 직전 연구였던 비트 평면 분할과 디더링을 통한 다중 학습 통합 신경망 구축에서의 한계점을 분석하고, 향상시킨 방법을 제시한다. 통합 신경망을 구축하는 방법에 대해 최근까지 시도되었던 방법들은 신경망을 구성하는 가중치(weight)나 층(layer)를 공유하거나 태스크 별로 구분하는 것들이 있다. 이와 같은 선상에서 본 연구는 더 작은 단위인 가중치의 비트 평면을 태스크 별로 할당하여 보다 효율적인 통합 신경망을 구축한다. 실험은 이미지 분류 문제에 대해 수행하였다. 대중적인 신경망 구조인 ResNet18 에 대해 적용한 결과 데이터셋 CIFAR10 과 CIFAR100 에서 이론적인 압축률 50%를 달성하면서 성능 저하가 거의 발견되지 않았다.

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