한국전자파학회논문지 (The Journal of Korean Institute of Electromagnetic Engineering and Science)

  • 제16권10호
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  • Pages.1010-1019
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  • 2005
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  • 1226-3133(pISSN)
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  • 2288-226X(eISSN)
한국전자파학회 (The Korean Institute of Electromagnetic Engineering and Science)
권호 표지

Two-Tone 입력을 이용한 RF 전력증폭기 메모리 특성의 신경망 모델링

Neural Network Modeling of Memory Effects in RF Power Amplifier Using Two-tone Input Signals

  • 황보훈 (성균관대학교 정보통신공학부) ;
  • 김원호 (성균관대학교 정보통신공학부) ;
  • 나완수 (성균관대학교 정보통신공학부) ;
  • 김병성 (성균관대학교 정보통신공학부) ;
  • 박천석 (성균관대학교 정보통신공학부) ;
  • 양영구 (성균관대학교 정보통신공학부)
  • Hwangbo Hoon (School of Information and Communication Engineering, Sungkyunkwan University) ;
  • Kim Won-Ho (School of Information and Communication Engineering, Sungkyunkwan University) ;
  • Nah Wansoo (School of Information and Communication Engineering, Sungkyunkwan University) ;
  • Kim Byung-Sung (School of Information and Communication Engineering, Sungkyunkwan University) ;
  • Park Cheonsuk (School of Information and Communication Engineering, Sungkyunkwan University) ;
  • Yang Youngoo (School of Information and Communication Engineering, Sungkyunkwan University)
  • 발행 : 2005.10.01
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초록

본 논문에서는 투톤(two-tone) 신호가 입력된 RF 전력 증폭기의 출력단에서 관찰되는 메모리 효과를 신경망회로를 이용하여 모델링 하였다. 입력 신호의 톤 간격과 전력 레벨의 변화에 따른 출력 전력의 IMD(Inter-Modulation Distortion) 비대칭성을 측정하여 고출력 RF 전력 증폭기의 메모리 효과를 확인하였으며, 서로 다른 중심 주파수에서의 메모리 효과도 실험적으로 확인하였다. 투톤 입력 신호 테스트에 기초한 전력 증폭기의 모델링 방법으로 TDNN(Tapped Delay Line-Neural Network)방식을 적용하였으며 이 방식이 다른 여러 가지 모델링 방법과 비교하여 매우 신뢰할만한 정확성을 가짐을 보였다.

In this paper, we used neural network technique to model memory effects of RF power amplifier which is fed by two-tone input signals. The memory effects in power amplifier were identified by observing the unsymmetrical distribution of IMD(Inter-Modulation Distortion) measurements with the change of tone spacings and power levels. Different asymmetries of IMD were also found at different center frequencies. We applied TDNN technique to model LDMOS power amplifier based on two tone IMD data, and the accuracy was very high compared to other modeling methods such as the(memoryless) adaptive modeling method.

키워드

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