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V2I 통신 시스템에서 ADPSS 채널 보간과 예측 기법

ADPSS Channel Interpolation and Prediction Scheme in V2I Communication System

  • 추명훈 (전남대학교 전자컴퓨터공학과) ;
  • 문상미 (전남대학교 전자컴퓨터공학과) ;
  • 권순호 (전남대학교 전자컴퓨터공학과) ;
  • 이지혜 (전남대학교 전자컴퓨터공학과) ;
  • 배사라 (전남대학교 전자컴퓨터공학과) ;
  • 김한종 (한국기술교육대학 정보기술공학부) ;
  • 김철성 (전남대학교 전자컴퓨터공학과) ;
  • 김대진 (전남대학교 전자컴퓨터공학과) ;
  • 황인태 (전남대학교 전자컴퓨터공학과)
  • Chu, Myeonghun (School of Electronics & Computer Engineering Chonnam National University) ;
  • Moon, Sangmi (School of Electronics & Computer Engineering Chonnam National University) ;
  • Kwon, Soonho (School of Electronics & Computer Engineering Chonnam National University) ;
  • Lee, Jihye (School of Electronics & Computer Engineering Chonnam National University) ;
  • Bae, Sara (School of Electronics & Computer Engineering Chonnam National University) ;
  • Kim, Hanjong (School of Electrical, Electronics & Communication Engineering, Korea University of Technology and Education) ;
  • Kim, Cheolsung (School of Electronics & Computer Engineering Chonnam National University) ;
  • Kim, Daejin (School of Electronics & Computer Engineering Chonnam National University) ;
  • Hwang, Intae (School of Electronics & Computer Engineering Chonnam National University)
  • 투고 : 2017.02.09
  • 심사 : 2017.07.28
  • 발행 : 2017.08.25

초록

V2I(Vehicle to Infrastructure) 통신은 ITS(Intelligent Transportation Systems)와 텔레매틱스 서비스를 제공하기 위한 차량과 노변 기지국간 통신 기술을 말한다. 차량은 Probe data를 기지국을 통하여 수집하며, 기지국은 도로 상태나 교통 정보를 차량에 제공한다. 이러한 V2I 통신 서비스를 제공하기 위해서는 신뢰성 있으며 높은 전송률을 달성할 수 있는 링크 적응 기법이 필요하다. 수신단은 추정한 CSI(Channel State Information)를 송신단으로 피드백하며 송신단은 이 정보를 이용하여 링크 적응을 한다. 그러나 차량 속도에 의한 채널의 빠른 변화와 계층 간 처리 지연 시간으로 인해 추정한 CSI는 outdated되게 된다. 이를 위해, V2I 통신 시스템에서 링크 적응을 위한 채널 보간과 예측 기법이 필요하다. 본 논문에서는 ADPSS(Advanced Discrete Prolate Spheroidal Sequence) 채널 보간과 예측 기법을 제안한다. 제안한 기법은 정규 직교 기저를 만들고 상관 행렬을 이용하여 채널 보간과 예측을 한다. 또한, 주파수 도메인에서 잡음 제거를 위해 스무딩을 한다. 모의실험 결과, 기존 기법과 비교했을 때 제안한 기법이 차량의 높은 속도와 낮은 속도를 가지는 고속도로와 도심지에서 성능이 향상된 것을 볼 수 있다.

Vehicle to Infrastructure(V2I) communication means the technology between the vehicle and the roadside unit to provide the Intelligent Transportation Systems(ITS) and Telematic services. The vehicle collects information about the probe data through the evolved Node B(eNodeB) and after that eNodeB provides road conditions or traffic information to the vehicle. To provide these V2I communication services, we need a link adaptation technology that enables reliable and higher transmission rate. The receiver transmits the estimated Channel State Information(CSI) to transmitter, which uses this information to enable the link adaptation. However, due to the rapid channel variation caused by vehicle speed and the processing delay between the layers, the estimated CSI quickly becomes outdated. For this reason, channel interpolation and prediction scheme are needed to achieve link adaptation in V2I communication system. We propose the Advanced Discrete Prolate Spheroidal Sequence(ADPSS) channel interpolation and prediction scheme. The proposed scheme creates an orthonomal basis, and uses a correlation matrix to interpolate and predict channel. Also, smoothing is applied to frequency domain for noise removal. Simulation results show that the proposed scheme outperforms conventional schemes with the high speed and low speed vehicle in the freeway and urban environment.

키워드

참고문헌

  1. Jihyung Kim, Junghyun Kim, and Kwangjae Lim, "Distributed Synchronization for OFDMA-Based Wireless Mesh Networks," ETRI Journal, Vol. 36, No. 1, pp. 1-11, February 2014. https://doi.org/10.4218/etrij.14.0113.0223
  2. 3GPP "LTE-Advanced Evaluation Workshop", Dec. 17-18, 2009.
  3. Sangmi Moon, Myeonghun Chu, Hanjong Kim, Daejin Kim, and Intae Hwang, "FFT-based Channel Estimation Scheme in LTE-A Downlink System" in IEIE, Vol. 53, No. 3, March 2016.
  4. Saransh Malik, Sherlie Portugal, Sangmi Moon, Bora Kim, Cheolsung Kim, and Intae Hwang, "Novel Channel Estimation Method in Fast Fading Channels Applied to LTE-Advanced" in IEIE, Vol. 49, No. 5, May 2012.
  5. Clarke, R.H., "A statistical theory of mobile-radio reception" in The Bell System Technical Journal, Vol. 47, No. 6, pp. 3597-3607, 2005.
  6. Alexandra Duel Hallen, "Fading Channel Prediction for Mobile Radio Adaptive Transmission Systems" in IEEE, Vol. 95, No. 12, December 2007.
  7. Yohei KOJIMA, Kazuki TAKEDA, and Fumiyuki ADACHI, "Polynomial Prediction RLS Channel Estimation for DS-CDMA Frequency-domain Equalization", in IEEE, 2009.
  8. Joao P. Leite, Paulo H. P. de Carvalho, Robson D. vieira, "OFDM channel prediction using set-membership affine projection algorithm in time-varying wireless channel", in IEEE, 2009.
  9. Liang Yao, Zhou Weiou, Zhou Mingyu, He Li, "Research and implementation for 2D MMSE channel estimation", in IEEE, 2014.
  10. Thomas Zemen, Christoph F. Mecklenbrauker, "Time-Variant Channel Estimation Using Discrete Prolate Spheroidal Sequences" in IEEE, Vol. 53, No. 9, September 2005.