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

  • 제32권3C호
  • /
  • Pages.314-322
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  • 2007
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  • 1226-4717(pISSN)
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  • 2287-3880(eISSN)
한국통신학회 (The Korean Institute of Commucations and Information Sciences)
권호 표지

다중모드 Cognitive Radio 통신 시스템을 위한 GBNSGA 최적화 알고리즘

GBNSGA Optimization Algorithm for Multi-mode Cognitive Radio Communication Systems

  • 박준수 (숭실대학교 정보통신전자공학부 통신 및 신호처리연구실) ;
  • 박순규 (숭실대학교 정보통신전자공학부 통신 및 신호처리연구실) ;
  • 김진업 (한국전자통신연구원 이동통신연구단) ;
  • 김형중 (한국전자통신연구원 이동통신연구단) ;
  • 이원철 (숭실대학교 정보통신전자공학부 통신 및 신호처리연구실)
  • 발행 : 2007.03.31
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⟨ 이전 논문 다음 논문 ⟩

초록

본 논문에서는 CR(Cognitive Radio)을 위해 사용자에게 최적의 통신 시스템 구성 변수들을 할당하기 위한 새로운 최적화 알고리즘인 GBNSGA(Goal-Pareto Based Non-dominated Sorting Genetic Algorithm)를 제안한다. 다중모드 선택적 CR 통신을 위해 사용되는 cognitive 엔진은 Mitola가 제안한 cognition 싸이클의 많은 논리 연산과정이 필요하다는 단점을 보완하기 위하여 일반적으로 유전자 알고리즘 기반의 접근 방식이 사용되고 있다. 본 논문에서는 cognitive 엔진의 효율적인 구동을 위하여 파레토(Pareto) 기반의 최적화 알고리즘인 NSGA(Non-dominated Sorting Genetic Algorithm)와 사용자 서비스의 요구사항을 goal로 설정하는 GP(Goal Programming)을 결합한 새로운 최적화 방법으로 GBNSGA를 제안하였으며, 시뮬레이션 수행을 통해 제안된 알고리즘이 요구사항에 적합한 다양한 해를 제공하고 최적화 수렴속도가 빠르다는 것을 확인하였다.

This paper proposes a new optimization algorithm named by GBNSGA(Goal-Pareto Based Non-dominated Sorting Genetic Algorithm) which determines the best configuration for CR(Cognitive Radio) communication systems. Conventionally, in order to select the proper radio configuration, genetic algorithm has been introduced so as to alleviate computational burden along the execution of the cognition cycle proposed by Mitola. This paper proposes a novel optimization algorithm designated as GBNSGA for cognitive engine which can be described as a hybrid algorithm combining well-known Pareto-based NSGA(Non-dominated Sorting Genetic Algorithm) as well as GP(Goal Programming). By conducting computer simulations, it will be verified that the proposed method not only satisfies the user's service requirements in the form of goals. It reveals the fast optimization capability and more various solutions rather than conventional NSGA or weighted-sum approach.

키워드

참고문헌

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