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Identification of Volterra model parameters in wireless systems

This paper reports the identification of nonlinear models for wireless communications systems. The procedure relies on a novel complex-valued Volterra series (CVS) representation to provide a sparse representation based on statistical hypothesis testing and the Bayesian information criterion (BIC). The approach has been experimentally evaluated with the front-end of a communications transmitter taking into account the possible nonlinear impairments associated with the I/Q modulator. The proposed identification method showed an excellent performance in terms of model complexity reduction and dynamic range.