The Journal of Korean Institute of Communications and Information Sciences (한국통신학회논문지)

The Korean Institute of Commucations and Information Sciences (한국통신학회)
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A Smart DTMC-based Handover Scheme Using Vehicle's Mobility Behavior Profile

차량의 이동성 행동 프로파일을 이용한 DTMC 기반의 스마트 핸드오버 기법

  • 한상혁 (광운대학교 제어계측공학과) ;
  • 김현우 (광운대학교 제어계측공학과) ;
  • 최용훈 (광운대학교 제어계측공학과) ;
  • 박수원 (광운대학교 전자통신공학과) ;
  • 이승형 (광운대학교 전파공학과)
  • Received : 2011.02.17
  • Accepted : 2011.05.20
  • Published : 2011.06.30
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Abstract

For improvement of wireless Internet service quality at vehicle's moving speed, it is advised to reduce the service disruption time by reducing the handover frequency on vehicle's moving path. Particularly, it is advantageous to avoid the handover to cell whose dwell time is short or can be ignored in terms of service continuity and average throughput. This paper proposes the handover scheme that is suitable for vehicle in order to improve the wireless Internet service quality. In the proposed scheme, the handover process continues to be learned before being modeled to Discrete-Time Markov Chain (DTMC). This modeling reduces the handover frequency by preventing the handover to cell that could provide service sufficiently to passenger even when vehicle passed through the cell but there was no need to perform handover. In order to verify the proposed scheme, we observed the average number of handovers, the average RSSI and the average throughput on various moving paths that vehicle moved in the given urban environment. The experiment results confirmed that the proposed scheme was able to provide the improved wireless Internet service to vehicle that moved to some degree of consistency.

차량과 같은 이동환경에서 무선 인터넷 서비스를 사용할 때, 불필요하게 발생하는 핸드오버는 서비스 품질 저하와 시그널링 오버헤드를 유발하여 실시간 멀티미디어 서비스 제공에 단점으로 작용한다. 본 논문에서는 Mobile Node (MN)가 일정한 이동패턴을 가지고 있을 때, Mobility behavior profile을 이용하여 생성한 DTMC을 이용하여 핸드오버의 발생 횟수를 감소시켜 무선 인터넷 서비스를 향상시킬 수 있는 핸드오버 방법을 제안한다. 기존 핸드오버 방법을 반복적으로 수행하며 Mobility behavior profile을 학습하고, Mobility behavior profile 에 충분한 양이 학습되면 Mobility behavior profile을 이용하여 DTMC의 1-step & 2-step transition probability matrix를 생성한다. 그 이후에는 DTMC의 1-step & 2-step transition probability matrix를 이용하여 핸드오버를 수행하며 Mobility behavior profile을 계속 업데이트한다. 4개의 Mobility model에서의 실험을 통하여 평균 핸드오버 횟수와 평균 RSSI값, 그에 따른 Throughput을 비교한다.

Keywords

References

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