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 Real-Time Multimedia Data Transmission Rate Control Using Neural Network Prediction Model

신경 회로망 예측 모델을 이용한 실시간 멀티미디어 데이터 전송률 제어

  • 김용석 (삼성전자 Mobile R&D) ;
  • 권방현 (전북대학교 네트워크 시스템 제어연구실) ;
  • 정길도 (전북대학교 전자정보공학부)
  • Published : 2005.02.28
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Abstract

This paper proposes a neural network prediction model to improve the valid packet transmission rate for the QoS(Quality of Service) of multimedia transmission. The Round Trip Time(RTT) and Packet Loss Rate(PLR) are predicted using a neural network and then the transmission rate is decided based on the predicted RTT and the PLR. The suggested method will improve the transmission rate since it uses the rate control factors corresponding to time of data is being transmitted, while the conventional one uses the transmission rate determined based on the past informations. An experimental set-up has been established using a Linux PC system, and the multimedia data are transmitted using UDP protocol in real time. The valid transmitted packets are about 5% higher than the one in the conventional TCP-Friendly congestion control method when the suggested algorithm was applied.

본 논문에서는 멀티미디어 전송 시 QoS(Quality of Service)를 개선하기 위한 유효패킷 전송률을 향상 시키는 방법으로 신경회로망을 이용한 예측 알고리즘을 제안하였다. 신경회로망 모델을 이용하여 왕복지연시간과 패킷손실률을 예측하고 예측된 인자를 이용하여 데이터 전송률을 결정하는 방법이다. 제안한 방법은 과거의 데이터를 기준으로 전송률을 결정하여 전송하는 데이터의 양을 제어하는 기존의 방법보다 향상된 성능을 확보할 수 있게 된다. 제안한 방법의 성능을 확인하기 위하여 실 시스템에 적용하는 실험을 실시하였다. 리눅스 운영 PC를 사용하였으며, UDP 프로토콜을 이용하여 실시간 데이터를 전송하는 실험 장치를 구현하였다. 제안한 방법의 유효패킷 전송률이 기존의 TCP-Friendly 혼잡제어 방법에 비하여 5% 정도 향상된 성능을 보였다.

Keywords

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