1st International ICST Conference on Communications and Networking in China

Research Article

Parametric Channel Estimation for OFDM Systems in Time-Varying Environment Based on Subspace Projecting and Tracking

  • @INPROCEEDINGS{10.1109/CHINACOM.2006.344742,
        author={Liang Dong and Xiuying  Cao and Guangguo  Bi},
        title={Parametric Channel Estimation for OFDM Systems in Time-Varying Environment Based on Subspace Projecting and Tracking},
        proceedings={1st International ICST Conference on Communications and Networking in China},
        publisher={IEEE},
        proceedings_a={CHINACOM},
        year={2007},
        month={4},
        keywords={},
        doi={10.1109/CHINACOM.2006.344742}
    }
    
  • Liang Dong
    Xiuying Cao
    Guangguo Bi
    Year: 2007
    Parametric Channel Estimation for OFDM Systems in Time-Varying Environment Based on Subspace Projecting and Tracking
    CHINACOM
    IEEE
    DOI: 10.1109/CHINACOM.2006.344742
Liang Dong1,2,*, Xiuying Cao1,2, Guangguo Bi1,2
  • 1: National Mobile Communications Research Laboratory, Southeast University
  • 2: Nanjing 210096, China.
*Contact email: dongliangseu@gmail.com

Abstract

Channel estimation is critical for coherent detection in OFDM systems. In pilot symbol aided channel estimation methods, the LS channel estimation can be viewed as a noisy observation of the true channel frequency response, so the noise component can be compressed through signal subspace projecting. In this paper, we focus on parametric channel estimation method combined with subspace projecting. First, we estimate the signal space of the OFDM system, and then we can use ESPRIT method to estimate the multipath delay of wireless channel, finally we use subspace projecting to get more accurate channel estimation. When the channel multipath delays vary with time, the signal subspace should be tracked to keep the efficiency of channel estimation. Computer simulations show that the performance of our method is much better than traditional channel estimation methods and its nonparametric counterpart.