8th International Conference on Communications and Networking in China

Research Article

Adaptive Joint Frame Synchronization and Carrier Frequency Offset Estimation in OFDM Systems

  • @INPROCEEDINGS{10.1109/ChinaCom.2013.6694572,
        author={Dandan Zhang and Xu Zhang and Bin Zhong and Zhongshan Zhang and Keping Long},
        title={Adaptive Joint Frame Synchronization and Carrier Frequency Offset Estimation in OFDM Systems},
        proceedings={8th International Conference on Communications and Networking in China},
        publisher={IEEE},
        proceedings_a={CHINACOM},
        year={2013},
        month={11},
        keywords={frequency offset estimation adaptive joint frame synchronization},
        doi={10.1109/ChinaCom.2013.6694572}
    }
    
  • Dandan Zhang
    Xu Zhang
    Bin Zhong
    Zhongshan Zhang
    Keping Long
    Year: 2013
    Adaptive Joint Frame Synchronization and Carrier Frequency Offset Estimation in OFDM Systems
    CHINACOM
    IEEE
    DOI: 10.1109/ChinaCom.2013.6694572
Dandan Zhang1, Xu Zhang1, Bin Zhong1, Zhongshan Zhang1,*, Keping Long1
  • 1: University of Science and Technology Beijing (USTB)
*Contact email: zhangzs@ustb.edu.cn

Abstract

An adaptive joint frame synchronization and carrier frequency offset estimation scheme in orthogonal frequency division multiplexing (OFDM) systems is proposed in this paper. Compared with conventional algorithm, the proposed one can reduce the Cramer-Rao lower bound (CRLB) to the minimum without changing the total energy consumption. In the proposed scheme, a variable parameter M is introduced in generating training sequence, where M is a function of the maximum multipath delay of the multipath channel. By adptively adjusting M, the optimum training sequence can be obtained. Accurate frame synchronization and carrier frequency offset acquisition can be performed simultaneously in the proposed scheme. An adaptive tracking algorithm in the proposed scheme is also needed to estimate the remaining carrier frequency offset after acquisition. By using the proposed optimum training sequence, considerable performance improvement in the proposed tracking algorithm over Moose algorithm can be obtained.