This thesis addresses the problem of blind adaptive equalization of finite impulse response (FIR) and infinite impulse response (IIR) channels (their characteristics are unknown) in the fractionally sampling scenario. An adaptive equalizer is used at the receiver to compensate the time dispersion induced by noisy communication channels and eliminate the effect of Inter-Symbol-Interference (ISI). In other words, the overall our system model, which is a cascade connection of the channel and equalizer, provides nearly an ideal transmission medium that the information source signals can be sent through. Due to this and rely only on probabilistic and statistical properties (Second Order Statistics (SOS) which has most communication channel information) of the received signals, the unknown input information signals can be recovered successfully.;Various blind adaptive algorithms are discussed throughout this thesis. Simulation results are presented by evaluating the mean square symbol error (MSE) of these techniques to study their performance behavior in blind channel equalization concept. These algorithms operate blindly in the practical situation, and they can achieve a complete adaptation without the aid of a training sequence, desired response, which is either impractical or very costly. The parameters of equalizer are updated in a recursive way with each single output measurement. Finally, the performance comparisons are realized to show which algorithm is more efficient and robustness to noisy channel model (three classes of channel model are used through thesis's simulation). The aim of this thesis is to improve the performance of a wireless communication channel using various blind adaptive equalization algorithms through computer simulations.
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