Ahstract-A post-compensation method based on decoupled Wiener model to eliminate the nonlinearity of Analog-to-Digital Converters is proposed in this paper. The compensator of memoryless nonlinearity is composed of a nonlinear subsystem without memory and a dynamic linear subsystem. The model parameters of Wiener model are measured by using harmonic distortion information in ADC output by sinusoidal signal stimulation. The results of experiment indicate that the spurious free dynamic range (SFDR) of this technique achieves about 20-dB improvement. Frequency sweeping results show that the parameters of nonlinear compensator can be acquired by a training signal excitation. With the help of narrowband-dithering technique, the Wiener model based compensator results in extra lO-dB improvement in SFDR by tiny cost of signal-to-noise ratio.

A recursive least square algorithm is implemented in this paper. Memory Nonlinearity of digital receiver is compensated by using a blind identification algorithm based on nonlinear model. A least-squared blind identification criterion to minimize all the energy of the nonlinearity in the receiver's output signal is implemented under circumstances of not knowing the information of the receiver's input signal. The orders and memory depths of the Volterra model are tested and updated automatically. A double-precision arbitrarydimensional matrix inversion module is implemented in the requirement of the least-squared method. The digital post calibration processes in real-time. Experimental results on the actual nonlinear circuit illustrate the validity of the implemented technique.