This paper proposes a channel estimation technique which uses a postfixed pseudo-noise (PN) sequence combined with zero padding to accurately estimate the channel impulse response for mobile orthogonal frequency division multiplexing (OFDM) communications. The major advantage of the proposed techniques is the periodical insertion of PN sequences after each OFDM symbol within the original guard interval in conventional zero-padded OFDM or within the original cyclic prefix (CP) in conventional CP-OFDM. In addition, the proposed technique takes advantage of null samples padded after the PN sequences for reducing inter-symbol interference occurring with the information detection in conventional pseudo-random-postfix OFDM. The proposed technique successfully applies either (1) least-squares algorithm with decision-directed data-assistance, (2) approximate least-squares estimation, or (3) maximum-likelihood scheme with various observation windows for the purpose of improving channel estimation performance. Some comparative simulations are given to illustrate the excellent performance of the proposed channel estimation techniques in mobile environments.

This paper proposes an improved discrete Fourier transform (DFT)-based channel estimation technique for time domain synchronous orthogonal frequency division multiplexing (TDS-OFDM) communication systems. The proposed technique, based on the concept of significant channel tap detector (SCTD) scheme, can effectively improve the system performance of TDS-OFDM systems. The correlation of two successive preambles is employed to estimate the average noise power as the threshold for obtaining the SCTD threshold estimation error and loss path information in large delay spread channel environments. The proposed estimation scheme roughly predicts the noise power in order to choose the significant channel taps to estimate the channel impulse response. Some comparative simulations are given to show that the proposed technique has the potential to achieve bit error rate performance superior to that of the conventional least squares channel estimation.