OFDM channel equalization based on radial basis function networks

The universal approximation property makes neural networks very attractive for system modelling and identification. Channel estimation and equalization for digital communications are good examples. We explore the application of a Radial Basis Function Network to approximate the frequency response of a wireless channel, under the settings established by the IEEE 802.11 family of standards for wireless LAN architecture. We aim to exploit the channel impulse response correlation in the frequency domain to reduce the effect of noise. We obtain a smoother reconstructed function than by using a single tap Zero Forcing frequency domain equalizer. This is achieved by using a smaller number of basis functions, in the approximating Radial Basis Function Network, than the number of sub-carriers used by the OFDM modulation technique adopted in the transmission system. Although the training of the network following the Least Squares criterion requires the inversion of a matrix, this is feasible given the relatively small number of sub-carriers in the WLAN. Simulations show that the proposed algorithm behaves considerably better with respect to a simple single tap Zero Forcing algorithm, by reducing the bit error rate by more than a half. We also outline a possible solution based on the Kalman filter to update the network parameters adaptively and thus exploit any time correlation of the channel impulse response.