Blind Identification and Digital Calibration of Volterra Model Based on Least Mean Square Method

Nonlinear distortions and memory effect of broadband receiver’s front-end are canceled out simultaneously using a digital post-calibration technique based on Volterra model. This paper develops a least mean squared blind identification criterion for the measurement of the model parameters without prior knowledge of the received signals, and the optimization goal could be described as for the minimizing of total energy of the calibrated outputs that located in the first Nyquist band other than strong signals. Frequency locations of the distortions are availably determined by the comparison between the input and output spectrums of a digital nonlinear polynomial with fixed coefficients. Experimental results on the actual nonlinear circuit show that with the proposed technique, 15 dB improvement in SpursFree-Dynamic-Range with multi-tone excitation signal is achieved. High-speed, high-precision Software Defined Radio systems would benefit much from the novel technical solution presented in the paper. Keywords-Volterra model; Least Mean Square (LMS); memory nonlinearity; blind sysmtem identification