The Journal of the Korea Contents Association (한국콘텐츠학회논문지)

The Korea Contents Association (한국콘텐츠학회)
권호 표지
DOI QR코드

DOI QR Code

Implementation of Spectrum Sensing Module based on IEEE 802.22 WRAN

IEEE 802.22 WRAN 기반 스펙트럼 센싱 모듈 구현

  • 이현소 (충북대학교 전파통신공학과) ;
  • 김경석 (충북대학교 전파통신공학과)
  • Published : 2009.03.28
Download PDF
⟨ Previous Next ⟩

Abstract

The Spectrum Sensing technology is the core technology of the Cognitive Radio (CR) system that is one of the future wireless communication technologies. This is the technology that temporarily allocates the frequency bandwidth by scanning surrounding wireless environments to keep licensed terminals and search the unused frequency bandwidth. In this paper, we implement the efficient Spectrum Sensing methods based on CR technology in an embedded board. The DVB-H signal with the 6MHz bandwidth is used as the RF input signal. And we confirm the Spectrum Sensing result using Modified Periodogram Method, Welch's Method, SCF Method. And also, We examine the execution speed of each of detailed functions and the performance of Spectrum Sensing methods on TI320C6416 DSP board inserted in an embedded board.

스펙트럼 센싱 기술은 차세대 무선통신 기술들 중 하나인 Cognitive Radio (CR) 시스템에서의 핵심 기술이다. CR 시스템은주변의 허가된 무선국을 보호하기 위해 주변 무선 환경을 탐색하여 빈 주파수 대역을 찾아 임시적으로 주파수 대역을 사용할 수 있도록 하는 기술이다. 본 논문은 임베디드 보드에서 CR 기술 기반의 효율적인 스펙트럼 센싱 기법들을 구현하였다. 6MHz 대역폭을 가진 DVB-H 신호를 입력 신호로 실험하였으며, Modified Periodogram Method, Welch's Method, SCF Method을 통하여 스펙트럼 센싱 결과를 확인하였다. 또한, 각각의 스펙트럼 센싱 모듈의 성능과 세부 기능들의 실행 속도를 TI320C6416 DSP 보드를 통하여 비교하였다.

Keywords

References

  1. D. Cabric, S. M. Mishra, and R. W. Brodersen, "Implementation issues in spectrum sensing for cognitive radios," Proc. IEEE Signal, Systems and Computers Conference, Vol.1, pp.772-776, 2004(11). https://doi.org/10.1109/ACSSC.2004.1399240
  2. Federal Communications Commission (FCC), "Spectrum policy task force", Rep. ET Docket, No.02-135, Nov. 2002.
  3. Federal Communications Commission (FCC), "Notice of proposed rule making," ET Docket, No.04-113, 2004(5).
  4. J. Mitola, "Cognitive Radio for flexible mobile multimedia communications," Proc. of IEEE Workshop on Mobile Multimedia Comm., pp.3-10, 1999(11). https://doi.org/10.1109/MOMUC.1999.819467
  5. http://www.ieee802.org/22/.
  6. C. Stevenson, 'unctional requirements for the 802.22 WRAN standard," IEEE 802.22 Draft, 2006(2).
  7. Mitola, "Cognitive Radio for Flexible Mobile Multimedia Communications," IEEE Mobile Multimedia Conference, pp.3-10, 1999(11). https://doi.org/10.1109/MOMUC.1999.819467
  8. Karsten Vandborg Sorensen and Soren Vang Andersen, "Noise Power Spectral Density Estimation From Noisy Speech Using On-line Trained Hidden Markov Models," Department of Communication Technology, Aalborg University.
  9. N. R. Lomb, "Least-squares frequency analysis of unequally spaced data," Astrophysics and Space Science, Vol.39, pp.447-462, 1976. https://doi.org/10.1007/BF00648343
  10. P. D. Welch, "The Use of Fast Fourier Transform for the Estimation of Power Spectra: A Method Based on Time Averaging Over Short, Modified Periodograms," IEEE Transactions on Audio Electroacoustics, Vol.15, pp.70-73, 1967(6). https://doi.org/10.1109/TAU.1967.1161901
  11. J. Goerlich and D. A. Bruckner, "Signal Analysis Using Spectral Correlation Measurement," IEEE Instrumentation and Measurement, Technology Conference, Vol.2, pp.1313-1318, 1998(5). https://doi.org/10.1109/IMTC.1998.676961
  12. W. A. Gardner, "Spectral correlation of modulated signals : Part I-analog modulation," IEEE Trans. Communication, Vol.35, pp.584-594, 1987(6). https://doi.org/10.1109/TCOM.1987.1096820