In this thesis, an optimal estimation algorithm, based on the Kalman Filter, is introduced for data recovery of orthogonal frequency-division multiplexed (OFDM) signals transmitted over fading channels. We show that the use of a zero prefix (ZP) along with a fast Fourier transform (FFT) operation zero padded to twice the data length allows for the recovery of subcarriers located next to a deep faded (at low signal-to-noise ratio SNR) values, exploiting all other subcarriers with higher SNR. The same approach is also shown to improve demodulation in the presence of signal clipping due to high peak to average power ratio (PAPR), as is often seen in OFDM signals. The proposed method assumes prior knowledge of the channel, usually estimated using the preamble. Testing was conducted for random channels with zero frequency response at a random frequency omega 0 and a signal in additive white Gaussian noise for various conditions. Further testing was done with typical Stanford University Interim (SUI) channels. Additionally, the use of the method to recover OFDM signals based on the IEEE 802.11 and 802.16 standards was examined. Results show that the proposed optimal estimation algorithm has very satisfactory performance compared to the standard OFDM receiver algorithm.
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