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Reinforce Learning Based Cooperative Sensing for Cognitive Radio Networks

인지 무선 시스템에서 강화학습 기반 협력 센싱 기법

  • 김도윤 (아주대학교 컴퓨터공학과) ;
  • 최영준 (아주대학교 컴퓨터공학과) ;
  • ;
  • 최증원 (국방과학연구소)
  • Received : 2018.08.18
  • Accepted : 2018.10.15
  • Published : 2018.10.31

Abstract

In this paper, we propose a reinforce learning based on cooperative sensing scheme to select optimal secondary users(SUs) to enhance the detection performance of spectrum sensing in Cognitive radio(CR) networks. The SU with high accuracy is identified based on the similarity between the global sensing result obtained through cooperative sensing and the local sensing result of the SU. A fusion center(FC) uses similarity of SUs as reward value for Q-learning to determine SUs which participate in cooperative sensing with accurate sensing results. The experimental results show that the proposed method improves the detection performance compared to conventional cooperative sensing schemes.

본 논문은 인지 무선(CR, Cognitive Radio) 네트워크에서 우선 사용자(Primary User)의 존재 유무를 2차 사용자(Secondary User)가 결정하기 위하여 협력 센싱을 사용하는 환경에서 스펙트럼 센싱의 감지 성능을 높이기 위해 강화 학습(Reinforce learning) 기반으로 최적의 인지 무선 사용자 선택하는 협력 센싱 방안을 제안한다. 협력 센싱을 통해 파악한 전역 센싱 결과와 인지 무선 사용자의 센싱 결과 간의 유사도에 따라 정확도가 높은 사용자를 파악한다. 이 정확도를 강화학습의 보상으로 사용하여 협력 센싱을 수행할수록 전역 결정과 일치하는 센싱 정보를 전송하는 사용자를 선택할 수 있다. 실험 결과 제안한 기법이 기존 협력 센싱 대비 향상된 스펙트럼 감지 성능을 보임을 확인할 수 있다.

Keywords

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