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The Chinese digital television terrestrial broadcasting (DTTB) standard uses pseudo-noise (PN) sequence as guard interval (GI) and the training sequence. This PN sequence is used to do channel estimation and synchronization in the system. The existing channel estimation methods often have to adopt iterative scheme when the length of channel impulse response (CIR) is long, which will increase the receiver complexity dramatically. In this paper, a channel estimation scheme for time domain synchronous OFDM (TDS-OFDM) systems is proposed. Interference free CIR information can be obtained by constructing special correlation windows, rather than removing interference from the time domain sequences through iteration. Moreover, the estimation can be achieved by using correlation process. Comparing to conventional schemes, the proposed algorithm greatly reduces the computational complexity. Simulation results show that the proposed method can meet the requirement of the Chinese DTTB systems. And the scheme is also suitable for other communication OFDM systems with time domain sequence headers. [ABSTRACT FROM AUTHOR]

A channel estimation method based on frequency-domain pilots and time-domain processing (FPTP) is proposed for OFDM systems in time-varying channels. Through Discrete Fourier Transform (DFT), the channel impulse response (CIR) is estimated by using the frequency-domain pilots, which is equivalent to the CIR estimated by cyclic correlation using time-domain pilot sequence. Hence, the channel estimation error, caused by the spectral leakage of the DFT, can be reduced by canceling the non-idea auto-correlation property of the pilot sequence in time-domain. At the same time, the complicated cyclic correlation computation in time-domain is evitable. The theoretical analysis and simulation results show that the proposed method outperforms the traditional DFT-based method and Wiener filter algorithm in time-varying multi-path fading channels. Moreover, the uncoded bit error rate (BER) performance of proposed algorithm is very close to that with perfect channel knowledge, especially in fast fading channels. [ABSTRACT FROM AUTHOR]