Sparse DFT Based Channel Estimation In OFDM Systems
Channel estimation is an essential part of Orthogonal Frequency Division Multiplexing (OFDM) communication systems. In this paper, two Discrete Fourier Transform (DFT) improvement algorithms are proposed and compared where the 1st one exploits channel sparsity concept while the other considers significant channel coefficients only. In the proposed algorithms; Enhanced and Sparse DFT (E-DFT and S-DFT), different number of significant channel components is selected either by a threshold determining procedure such as in E-DFT, or through determining channel sparsity level such as in S-DFT. In the presence of Doppler frequency shifts, the Inter Symbol Interference (ISI) effect on channel coefficients is successfully reduced using the proposed estimation algorithms. Vehicular A-ITU channel model is considered with a relatively high vehicle speed up to 68 Km/h in order to test the suitability of the proposed algorithms for mobile systems. E-DFT and S-DFT improves conventional as well as previous DFT improvement methods (I-DFT) suggested by , , , . For 64 subcarriers, S-DFT outperforms E-DFT and I-DFT by about 3dB at a BER of 0.01 with a mobility reaches 45 Km/h, and by about 0.4dB and 2.5dB at a BER of 0.02 with a mobility reaches 68Km/h.