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Estimation of nonlinear communication channels using random multisine signals

Estimation of nonlinear communication channel has long been an important problem in digital communications. Being able to identify the nonlinear characteristics of the channels can help in the design of the nonlinear equalizer. In this paper, we propose a novel method for estimation of cubically nonlinear channels using random multisine signals as the input. By analyzing the higher-order moment spectra of random multisine input signals, a computationally efficient algorithm for identifying the Volterra kernels of the nonlinear channel is derived. In addition, we show that, with properly designed random multisines as driving signals, the channel estimate obtained by the proposed method is equal to the optimum minimum mean square error solution. The goodness of the proposed method is also demonstrated by computer simulation.