DOI QR코드

DOI QR Code

Adaptive Modulation System Using SNR Estimation Method Based on Correlation of Decision Feedback Signal

Decision Feedback 신호의 자기 상관 기반 SNR 추정 방법을 적용한 적응 변조 시스템

  • Kim, Seon-Ae (Department of Electronic Engineering, Chungbuk National University) ;
  • Ryu, Heung-Gyoon (Department of Electronic Engineering, Chungbuk National University)
  • Accepted : 2011.01.07
  • Published : 2011.03.31

Abstract

Adaptive modulation(AM) is an important technique to increase the system efficiency, in which transmitter selects the most suitable modulation mode adaptively according to channel state in the temporary and spatially varying communication environment. Fixed modulation on channels with varying signal-to-noise ratio(SNR) is that the bit-errorrate(BER) probability performance is changing with the channel quality. An adaptive modulation scheme can be designed to have a BER which is constant for all channel SNRs. The correct as well as fast and simple SNR estimation is required essentially for this adaptive modulation. In order to operate adaptive modulation system effectively, in this paper, we analyze the effect of SNR estimation performance to it through the average BER and data throughput. Applying SNR estimation based on auto-correlation of decision feedback signal and others to adaptive modulation system, we also confirm performance degradation or improvement of its which is decided by SNR estimation error at each transition point of modulation level. Since SNR estimation based on auto-correlation of decision feedback signal shows stable estimation performance for various quadrature amplitude modulation(QAM) comparatively, this can be reduced degradation than others at each transition point of modulation level.

적응 변조(Adaptive Modulation: AM) 방식은 시간적으로 공간적으로 바뀌는 채널의 상태에 적합한 변조 방식을 적응적으로 할당함으로써, 시스템의 효율을 높이는 중요한 통신 방식이다. 고정 변조 방식은 시간에 따라 신호 대 잡음비(SNR: Signal-to-Noise Ratio)가 변하는 채널에서 BER(Bit Error Rate) 성능이 변한다. 하지만 적응변조 방식은 모든 채널 상태의 SNR에 대하여 일정한 평균 BER 성능을 유지하므로 채널의 상태가 수시로 변하는 통신 환경에서 시스템의 성능을 확보한다. 이를 위해서 무엇보다도 정확하고, 빠르게 신호 대 잡음비를 추정할 수 있는 간단한 SNR 추정 방법이 요구된다. 본 논문에서는 효과적인 적응 변조를 위하여 SNR 추정 성능이 적응 변조 시스템에 미치는 영향을 평균 BER과 평균 데이터 처리율(throughput)을 통하여 분석한다. 또한, 본 논문에서는 decision feedback 신호의 자기 상관 기반의 SNR 추정 방법 및 기존의 SNR 추정 방법들을 적응 변조시스템에 적용하여 각 변조 레벨 변환 점에서 SNR 추정 성능에 따라 결정되는 적응 변조 시스템의 성능 변화를 확인한다. Decision feedback 신호의 자기 상관 기반 SNR 추정 방법은 M-QAM(Quadrature Amplitude Modulation) 신호에서도 비교적 안정적인 추정 성능을 보이기 때문에 적응 변조 시스템에서 다른 SNR 추정 방법들에 비해 변조 레벨 변환 점에서 성능 열화를 줄인다.

Keywords

References

  1. G. Albertazzi, S. Cioni, G. Corazza, M. Neri, R. Pedone, P. Salmi, A. Vanelli-Coralli, and M. Villanti, "On the adaptive DVB-S2 physical layer: Design and performance", IEEE Wireless Commun. Mag., vol. 12, no. 6, pp. 62-68, Dec. 2005. https://doi.org/10.1109/MWC.2005.1561946
  2. A. Svensson, "An introduction to adaptive QAM modulation schemes for known and predicted channels", Proceedings of the IEEE, vol. 95, no. 12, pp. 2322-2336, Dec. 2007. https://doi.org/10.1109/JPROC.2007.904442
  3. K. M. Kamath, D. L. Goeckel, "Adaptive-modulation schemes for minimum outage probability in wireless systems", IEEE Transactions on Commun., vol. 52, no. 10, pp. 1632-1635, Oct. 2004. https://doi.org/10.1109/TCOMM.2004.836439
  4. S. Falahari, A. Svensson, T. Ekman, and M. Sternad, "Adaptive modulation systems for predicted wireless channels", IEEE Transactions on Commun., vol. 52, no. 2, pp. 307-316, Feb. 2004. https://doi.org/10.1109/TCOMM.2003.822715
  5. D. R. Pauluzzi, N. C. Beaulieu, "A comparison of SNR estimation techniques for the AWGN channel", IEEE Transactions on Commun., vol. 48, no. 10, pp. 1680-1691, Oct. 2000. https://doi.org/10.1109/26.871393
  6. R. Lopez-Valcarce, C. Mosquera, "Sixth-order statistics-based nondata-aided SNR estimation", IEEE Commun. Lett., vol. 11, no. 4, pp. 351-353, Apr. 2007. https://doi.org/10.1109/LCOM.2007.348298
  7. M. Alvarez-Diaz, R. Lopez-Valcarce, and C. Mosquera, "SNR estimation for multilevel constellations using higher-order moments", IEEE Transactions on Signal Processing, vol. 58, no. 3, Part 2, pp. 1515-1526, Mar. 2010. https://doi.org/10.1109/TSP.2009.2036069
  8. A. Stephenne, F. Bellili, and S. Affes, "Momentbased SNR estimation over linearly-modulated wireless SIMO channels", IEEE Transactions on Wireless Communications, vol. 9, no. 2, pp. 714-722, Feb. 2010. https://doi.org/10.1109/TWC.2010.02.081719
  9. W. Gappmair, R. Lopez-Valcarce, and C. Mosquera, "Cramer-Rao lower bound and EM algorithm for envelope-based SNR estimation of nonconstant modulus constellations", IEEE Transactions on Commun., vol. 57, no. 6, pp. 1622-1627, Jun. 2009. https://doi.org/10.1109/TCOMM.2009.06.0700932
  10. H. Hayes, Statistical Digital Signal Processing and Modeling, John Wiley, 1996.
  11. H. Van Trees, Detection, Estimation, and Modulation Theory, vol. 1, New York, Wiley, 1968.
  12. R. van Nee, R. Prasad. OFDM for Wireless Multimedia Communications. Norwood, MA: Artech House, 2000.