Performance Analysis and Comparison of Adaptive Filters for Wireless Channel Equalization

In this paper we have applied radial basis function neural networks for equalization of wireless channel in the presence of additive noise. We have been tested the adaptive filter algorithms of simple Kalman filter, extended Kalman filter, rbfn Kalman filter, rbfn extended Kalman filter, fuzzy Kalman filter and fuzzy extended Kalman filter which might be considered applicable for wireless channel equalization in presence of noise. The Kalman filter is one of the most widely used due to its simplicity, optimality, tractability and robustness. However, the application of the KF to nonlinear systems can be difficult. The most common approach is to use the extended Kalman filter. We have shown that performance of the fuzzy extended Kalman filter is superior. More specifically, this paper highlights the error rate performance of the adaptive filters for channel equalization. Simulation results demonstrate that the adaptive fuzzy Kalman filter and fuzzy extended Kalman filter works successfully, efficiently.