RISS 학술연구정보서비스

검색
다국어 입력

http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.

변환된 중국어를 복사하여 사용하시면 됩니다.

예시)
  • 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
  • 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
닫기
    인기검색어 순위 펼치기

    RISS 인기검색어

      KCI등재 SCOPUS

      Nearest neighbor Methods and their Applications in Design of 5G & Beyond Wireless Networks

      한글로보기

      https://www.riss.kr/link?id=A108002729

      • 0

        상세조회
      • 0

        다운로드
      서지정보 열기
      • 내보내기
      • 내책장담기
      • 공유하기
      • 오류접수

      부가정보

      다국어 초록 (Multilingual Abstract)

      In this paper, we present an overview of Nearest neighbor (NN) methods, which are frequently employed for solving classification problems using supervised learning. The article concisely introduces the theoretical background, algorithmic, and implementation aspects along with the key applications. From an application standpoint, this article explores the challenges related to the 5G and beyond wireless networks which can be solved using NN classification techniques.
      번역하기

      In this paper, we present an overview of Nearest neighbor (NN) methods, which are frequently employed for solving classification problems using supervised learning. The article concisely introduces the theoretical background, algorithmic, and implemen...

      In this paper, we present an overview of Nearest neighbor (NN) methods, which are frequently employed for solving classification problems using supervised learning. The article concisely introduces the theoretical background, algorithmic, and implementation aspects along with the key applications. From an application standpoint, this article explores the challenges related to the 5G and beyond wireless networks which can be solved using NN classification techniques.

      더보기

      참고문헌 (Reference)

      1 U. S. Hashmi, "User-centric cloud ran: An analytical framework for optimizing area spectral and energy efficiency" 6 : 19859-19875, 2018

      2 Y. Liao, "Use of k-nearest neighbor classifier for intrusion detection" 21 (21): 439-448, 2002

      3 M. Muja, "Scalable nearest neighbor algorithms for high dimensional data" 36 (36): 2227-2240, 2014

      4 Y. Liu, "Reconfigurable intelligent surfaces: Principles and opportunities"

      5 H. Zhang, "Network slicing based 5g and future mobile networks: mobility, resource management, and challenges" 55 (55): 138-145, 2017

      6 K. Clarkson, "Nearest-neighbor searching and metric space dimensions"

      7 T. Cover, "Nearest neighbor pattern classification" 13 (13): 21-27, 1967

      8 N. Salhab, "Machine learning based resource orchestration for 5g network slices" IEEE 1-6, 2019

      9 F. Che, "Machine learning based approach for indoor localization using ultra-wide bandwidth (uwb) system for industrial internet of things (iiot)" 1-4, 2020

      10 M. Datar, "Locality-sensitive hashing scheme based on p-stable distributions" 253-262, 2004

      1 U. S. Hashmi, "User-centric cloud ran: An analytical framework for optimizing area spectral and energy efficiency" 6 : 19859-19875, 2018

      2 Y. Liao, "Use of k-nearest neighbor classifier for intrusion detection" 21 (21): 439-448, 2002

      3 M. Muja, "Scalable nearest neighbor algorithms for high dimensional data" 36 (36): 2227-2240, 2014

      4 Y. Liu, "Reconfigurable intelligent surfaces: Principles and opportunities"

      5 H. Zhang, "Network slicing based 5g and future mobile networks: mobility, resource management, and challenges" 55 (55): 138-145, 2017

      6 K. Clarkson, "Nearest-neighbor searching and metric space dimensions"

      7 T. Cover, "Nearest neighbor pattern classification" 13 (13): 21-27, 1967

      8 N. Salhab, "Machine learning based resource orchestration for 5g network slices" IEEE 1-6, 2019

      9 F. Che, "Machine learning based approach for indoor localization using ultra-wide bandwidth (uwb) system for industrial internet of things (iiot)" 1-4, 2020

      10 M. Datar, "Locality-sensitive hashing scheme based on p-stable distributions" 253-262, 2004

      11 A. Sobehy, "Csi-mimo: K-nearest neighbor applied to indoor localization" 2020

      12 B. K. Donohoo, "Context-aware energy enhancements for smart mobile devices" 13 (13): 1720-1732, 2014

      13 V.P. Kafle, "Consideration on automation of 5g network slicing with machine learning" 1-8, 2018

      14 A. Imran, "Challenges in 5g: how to empower son with big data for enabling 5g" 28 (28): 27-33, 2014

      15 R. Castro, "Cal learning theory" 2018

      16 P. Indyk, "Approximate nearest neighbors: towards removing the curse of dimensionality" 604-613, 1998

      17 S. Chernov, "Anomaly detection algorithms for the sleeping cell detection in lte networks" 1-5, 2015

      18 Y. Xie, "An improved k-nearestneighbor indoor localization method based on spearman distance" 23 (23): 351-355, 2016

      19 K.L. Clarkson, "An algorithm for approximate closest-point queries" 160-164, 1994

      20 M. T. Dickerson, "Algorithms for proximity problems in higher dimensions" 5 (5): 277-291, 1996

      21 오종택, "Adaptive K-nearest neighbour algorithm for WiFi fingerprint positioning" 한국통신학회 4 (4): 91-94, 2018

      22 M. Reza, "A survey on nearest neighbor search methods" 95 (95): 39-52, 2014

      23 T.M. Breuel, "A note on approximate nearest neighbor methods"

      24 J. -B. Wang, "A machine learning framework for resource allocation assisted by cloud computing" 32 (32): 144-151, 2018

      25 S. Zaidi, "A jupyter notebook for knn los/nlos classification"

      26 P. Popovski, "5g wireless network slicing for embb, urllc, and mmtc: a communication-theoretic view" 6 : 55765-55779, 2018

      27 3GPP, "3gpp tr 28.801: Study on management and orchestration of network slicing for next generation network (release 15)"

      더보기

      동일학술지(권/호) 다른 논문

      분석정보

      View

      상세정보조회

      0

      Usage

      원문다운로드

      0

      대출신청

      0

      복사신청

      0

      EDDS신청

      0

      동일 주제 내 활용도 TOP

      더보기

      주제

      연도별 연구동향

      연도별 활용동향

      연관논문

      연구자 네트워크맵

      공동연구자 (7)

      유사연구자 (20) 활용도상위20명

      인용정보 인용지수 설명보기

      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2023 평가예정 해외DB학술지평가 신청대상 (해외등재 학술지 평가)
      2020-01-01 평가 등재학술지 유지 (해외등재 학술지 평가) KCI등재
      2017-08-01 평가 SCOPUS 등재 (기타) KCI등재
      2017-01-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
      더보기

      이 자료와 함께 이용한 RISS 자료

      나만을 위한 추천자료

      해외이동버튼