Single-Carrier Frequency Domain Equalization for Hammerstein Communication Systems Using Complex-Valued Neural Networks

Abstract

Single-carrier (SC) block transmission with frequency-domain equalization (FDE) offers a viable transmission technology for combating the adverse effects of long dispersive channels encountered in high-rate broadband wireless communication systems. However, for high-bandwidth-efficiency and high-power-efficiency systems, the channel can generally be modeled by the Hammerstein system, which includes the nonlinear distortion effects of the high-power amplifier (HPA) at transmitter. For such nonlinear Hammerstein channels, the standard SC-FDE scheme no longer works. This paper advocates a complex-valued (CV) B-spline neural-network-based nonlinear SC-FDE scheme for Hammerstein channels. Specifically, we model the nonlinear HPA, which represents the CV static nonlinearity of the Hammerstein channel, by a CV B-spline neural network, and we develop two efficient alternating least squares schemes for estimating the parameters of the Hammerstein channel, including both the channel impulse response coefficients and the parameters of the CV B-spline model. We also use another CV B-spline neural network to model the inversion of the nonlinear HPA, and the parameters of this inverting B-spline model can easily be estimated using the standard least-squares algorithm based on the pseudo training data obtained as a natural byproduct of the Hammerstein channel identification. Equalization of the SC Hammerstein channel can then be accomplished by the usual one-tap linear equalization in the frequency domain as well as the inverse B-spline neural network model obtained in the time domain. Extensive simulation results are included to demonstrate the effectiveness of our nonlinear SC-FDE scheme for Hammerstein channels.