Journal of Advanced Navigation Technology (한국항행학회논문지)

The Korean Navigation Institute (한국항행학회)
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A Predictive Connection Admission Control Using Neural Networks for Multiclass Cognitive Users Radio Networks

멀티 클래스 인지 사용자 네트워크에서 신경망을 이용한 예측 연결수락제어

  • Lee, Jin-Yi (Dept. of Electronic Engineering, Chungwoon University)
  • Received : 2013.07.17
  • Accepted : 2013.08.30
  • Published : 2013.08.30
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Abstract

This paper proposes a neural net based-predictive connection admission control (CAC) scheme for multiclass users in wireless cognitive radio networks. We classifies cognitive users(cu) into real and non real time services, and then permit only real time services to reserve the demanded resource for spectrum handoff in guard channel for provisioning the desired QoS. Neural net is employed to predict primary user's arrival on time and demanded channels. Resource scheduling scheme is based on $C_IA$(cognitive user I complete access) shown in this paper. For keeping primary users from interference, the CAC is performed on only cognitive user not primary user. Simulation results show that our schemes can guarantee the desired QoS by cognitive real time services.

본 논문에서는 무선 인지망에서 멀티 클래스 인지 사용자의 서비스를 지원하기 위해 신경망 예측기법에 의한 예측 연결수락제어기법을 제안한다. 제안한 기법에서는 주 사용자가 도착하여 스펙트럼 핸드오프를 해야 하는 인지 사용자를 실시간과 비 실시간 사용자로 구분하고, 실시간 인지사용자에게 가드채널(guard channel)을 우선하여 사용하도록 한다. 이를 위해서 신경망 예측기법을 이용하여 주 사용자의 도착을 예측하고, 실시간 인지사용자가 스펙트럼 핸드오프 하는데 필요로 하는 채널의 크기를 예약하여 요구하는 서비스 품질(QoS)를 보장한다. 실시간과 비 실시간 인지사용자 연결을 위한 자원스케줄링 방법은 본 논문의 $C_IA$(cognitive user I complete access)방법을 사용하며, 연결 수락제어는 인지사용자에 대해서만 실시하여 주 사용자에게는 간섭이 없도록 한다. 시뮬레이션을 통하여 제안한 연결 수락제어기법이 실시간 인지사용자가 원하는 서비스 품질을 보장할 수 있음을 보인다.

Keywords

References

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